diff --git a/.github/workflows/deptry.yml b/.github/workflows/deptry.yml
new file mode 100644
index 00000000..3df23c52
--- /dev/null
+++ b/.github/workflows/deptry.yml
@@ -0,0 +1,24 @@
+name: Deptry Dependency Check
+
+on: [pull_request]
+
+jobs:
+  dependencies:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      # Setup the minimum required python distribution
+      - uses: actions/setup-python@v5
+      - name: Setup uv
+        uses: astral-sh/setup-uv@v5
+      - name: Install GDAL system libraries
+        run: sudo apt-get update && sudo apt-get install -y libgdal-dev
+      # GDAL's Python bindings must match the system libgdal version exactly.
+      # Pin the version in pyproject.toml to match the system libgdal.
+      - run: |
+          sed -i "s/\"GDAL\"/\"GDAL==$(gdal-config --version)\"/" pyproject.toml
+          uv sync
+      # Install deptry
+      - run: uv pip install deptry
+      # Run deptry to check that all dependencies are present.
+      - run: uv run deptry . -ddg test,types
diff --git a/.github/workflows/numpydoc.yml b/.github/workflows/numpydoc.yml
new file mode 100644
index 00000000..a7de8833
--- /dev/null
+++ b/.github/workflows/numpydoc.yml
@@ -0,0 +1,36 @@
+name: Numpydoc Lint
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  numpydoc-lint:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - uses: awalsh128/cache-apt-pkgs-action@latest
+        with:
+          packages: fd-find
+          version: 1.0
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12'
+
+      - name: Install dependencies
+        run: |
+          pip install numpydoc
+          sudo apt-get install
+
+      - name: Run Numpydoc Lint
+        run: |
+          fdfind . vs30/ -E "__init__.py" --extension py | xargs numpydoc lint
diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml
index b64ab92a..2fbe4662 100644
--- a/.github/workflows/pytest.yaml
+++ b/.github/workflows/pytest.yaml
@@ -22,7 +22,7 @@ jobs:
       - name: Install dependencies with mamba
         shell: bash -el {0}
         run: |
-          mamba install -c conda-forge gdal numpy pandas pyproj scikit-learn scipy matplotlib pytest geopandas
+          mamba install -c conda-forge gdal numpy pandas pyproj scikit-learn scipy matplotlib pytest pytest-cov geopandas
 
       - name: Install project in dev mode
         shell: bash -el {0}
@@ -30,5 +30,53 @@ jobs:
 
       - name: Run tests
         shell: bash -el {0}
-        run: pytest
+        run: pytest --cov=vs30 --cov-report=html tests
 
+      - name: Upload coverage data
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-data
+          path: .coverage
+          include-hidden-files: true
+          if-no-files-found: ignore
+
+  coverage:
+    needs: test
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check out the repo
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade coverage[toml]
+
+      - name: Download coverage data
+        uses: actions/download-artifact@v4
+        with:
+          name: coverage-data
+
+      - name: Combine coverage and fail if it's under 85%
+        run: |
+          python -m coverage html --skip-covered --skip-empty
+
+          # Report and write to summary.
+          python -m coverage report | sed 's/^/    /' >> $GITHUB_STEP_SUMMARY
+
+          # Report again and fail if under 85%.
+          python -Im coverage report --fail-under=85
+
+      - name: Upload HTML report if check failed
+        uses: actions/upload-artifact@v4
+
+        with:
+          name: html-report
+          path: htmlcov
+
+        if: ${{ failure() }}
diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
new file mode 100644
index 00000000..8e10796b
--- /dev/null
+++ b/.github/workflows/ruff.yml
@@ -0,0 +1,8 @@
+name: Ruff
+on: [pull_request]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: chartboost/ruff-action@v1
diff --git a/.github/workflows/types.yml b/.github/workflows/types.yml
new file mode 100644
index 00000000..a6860f94
--- /dev/null
+++ b/.github/workflows/types.yml
@@ -0,0 +1,36 @@
+name: Type Check
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+    branches: [master]
+
+jobs:
+  typecheck:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
+      - name: Install GDAL system libraries
+        run: sudo apt-get update && sudo apt-get install -y libgdal-dev
+
+      - name: Install project with types
+        # GDAL's Python bindings must match the system libgdal version exactly.
+        # Pin the version in pyproject.toml to match the system libgdal.
+        run: |
+          sed -i "s/\"GDAL\"/\"GDAL==$(gdal-config --version)\"/" pyproject.toml
+          uv sync --all-extras --dev
+
+      - name: Run type checking with ty
+        run: uv run ty check --exclude setup.py --exclude VsViewer/
diff --git a/README.md b/README.md
index daeafb9f..bdd038d5 100644
--- a/README.md
+++ b/README.md
@@ -1,47 +1,255 @@
-# Vs30
-Vs30 is used in the high frequency workflow.
-Retrieve Vs30/standard deviation/residual at locations using Kevin Foster's research codes (modified).
-Run for specific locations or over a grid.
+# VS30 - Shear-Wave Velocity Mapping for New Zealand
 
-## Setup
-Make sure you have Python 3 on your system.
-There are a few Python packages that the code requires. Packages that require system libraries such as `gdal` require to be re-compiled with parent library ABI change. This is especially important when using `pip` because it doesn't manage binary dependencies. `gdal` should be installed using the system package manager.
+A Python package for calculating and mapping Vs30 (time-averaged shear-wave velocity in the upper 30 meters) values across New Zealand for seismic hazard analysis. The package uses a multi-stage Bayesian framework combining categorical geology/terrain models with spatial observations and statistical adjustments.
 
-You can either install the packages via the system package manager or using pip. The list of packages is:
- * GDAL (requires gdal system libraries, should be installed as system package, import is osgeo)
- * pandas
- * numpy
- * pyproj (requires proj system library)
- * scikit-learn (for measured site clustering)
- * scipy (for downsampling the McGann dataset)
+## Installation
+
+### Install from GitHub
+
+```bash
+pip install git+https://github.com/ucgmsim/Vs30.git
+```
+
+### Install from Source (Development Mode)
+
+Clone the repository and install in editable mode:
+
+```bash
+git clone https://github.com/ucgmsim/Vs30.git
+cd Vs30
+pip install -e .
+```
+
+The installation automatically extracts required shapefiles from the bundled archive.
+
+### Dependencies
+
+The package requires Python 3.10+ and will automatically install dependencies including:
+- numpy, pandas, scipy
+- rasterio, geopandas, shapely
+- scikit-learn (for DBSCAN clustering)
+- typer (for CLI)
+- pydantic (for configuration)
+- tqdm (for progress bars)
+
+## CLI Commands
+
+The package provides a `vs30` command-line interface with the following commands:
+
+### Full Pipeline
+
+Run the complete Vs30 mapping workflow:
+
+```bash
+vs30 grid
+```
+
+This runs both geology and terrain pipelines, then combines them into a final map. Output is saved to the directory specified in `config.yaml`.
+
+Options:
+- `--output-dir`: Output directory (default from config)
+- `--n-proc`: Number of parallel processes (-1 for all cores)
+- `--geology-csv`: Custom geology categorical model CSV
+- `--terrain-csv`: Custom terrain categorical model CSV
+
+### Compute at Specific Locations
+
+Calculate Vs30 at specific lat/lon points without generating full raster grids:
+
+```bash
+vs30 points \
+    --locations-csv sites.csv \
+    --output-csv results.csv
+```
+
+Input CSV must have `longitude` and `latitude` columns in WGS84 (EPSG:4326). Coordinates are converted internally to NZTM2000 (EPSG:2193) for processing. Use `--lon-column` and `--lat-column` to specify different column names.
+
+Options:
+- `--coast-distance-raster`: Required for hybrid geology modifications
+- `--include-intermediate/--final-only`: Include intermediate values in output
+- `--n-proc`: Number of parallel processes
+
+### Individual Pipeline Stages
+
+Run specific stages of the pipeline:
+
+```bash
+# Update categorical model values with Bayesian updates
+vs30 update-priors \
+    --categorical-model-csv model.csv \
+    --clustered-observations-csv cpt.csv \
+    --output-dir output/ \
+    --model-type geology
+
+# Create initial Vs30 rasters from categorical models
+vs30 make-initial-vs30-raster --geology --terrain --output-dir output/
+
+# Apply slope and coastal distance adjustments to geology
+vs30 adjust-geology-vs30-by-slope-and-coastal-distance \
+    --input-raster geology_initial.tif \
+    --id-raster gid.tif \
+    --output-dir output/
+
+# Apply spatial adjustment using observations
+vs30 spatial-fit \
+    --input-raster geology_hybrid.tif \
+    --observations-csv observations.csv \
+    --model-values-csv updated_model.csv \
+    --output-dir output/ \
+    --model-type geology
+
+# Combine geology and terrain models
+vs30 combine \
+    --geology-tif geology_final.tif \
+    --terrain-tif terrain_final.tif \
+    --output-path combined.tif
+```
+
+### Visualization
+
+Plot prior vs posterior categorical values:
+
+```bash
+vs30 plot-posterior-values \
+    --csv-path updated_model.csv \
+    --output-dir plots/
+```
+
+### Using a Custom Configuration
+
+Specify a custom config file for any command:
+
+```bash
+vs30 --config /path/to/config.yaml grid
+```
+
+## How It Works
+
+The Vs30 mapping pipeline consists of several stages that combine categorical models with observational data to produce spatially-adjusted Vs30 maps.
+
+### Overview
 
-Install the package (optional):
-```shell
-pip install --user .
 ```
-You may have to add the installed script location to `PATH` if you cannot run `vs30calc.py` from other locations:
-```shell
-export PATH=$PATH:$HOME/.local/bin
+Observations ──► Bayesian Update ──► Category CSV
+                                          │
+                               Create ID Raster ──► Map to Vs30 Raster
+                                                          │
+                               [Hybrid Modifications - geology only]
+                                                          │
+                               Spatial Adjustment
+                                                          │
+                               Combine Geology + Terrain ──► Final Map
 ```
 
-If you aren't using the installed package, you will need to extract the compressed shapefiles (`vs30/data/shapefiles.tar.xz`) such that the folders `vs30/data/coast` and `vs30/data/qmap` exist.
+### Stage 1: Bayesian Update of Categorical Models
+
+**Module**: `category.py`
+
+Each geology and terrain category has a prior Vs30 mean and standard deviation based on published studies. The Bayesian update stage refines these values using measured observations.
+
+**Key features**:
+- **DBSCAN clustering**: Observations are spatially clustered to prevent over-weighting of dense measurement clusters (e.g., from geotechnical investigations in urban areas). Each cluster is treated as a single pseudo-observation.
+- **Sequential updates**: Clustered observations (e.g., CPT data) are processed first to correct for spatial sampling bias, then independent observations (direct Vs30 measurements) refine the model.
+- **Log-normal assumption**: All Bayesian updates operate in log-space, reflecting the log-normal distribution of Vs30 values.
+
+### Stage 2: Raster Creation
+
+**Module**: `raster.py`
+
+Creates geospatial rasters from the categorical models:
+
+1. **ID Rasters**:
+   - Geology IDs from QMAP shapefile polygons
+   - Terrain IDs from IwahashiPike classification raster
+
+2. **Vs30 Rasters**: Maps category IDs to Vs30 mean and standard deviation values from the updated categorical model CSVs.
+
+### Stage 3: Hybrid Geology Modifications
+
+**Module**: `raster.py`
+
+Applies empirical adjustments to geology-based Vs30 values based on New Zealand-specific calibration:
+
+1. **Slope-based adjustments**: For certain geology categories, steeper terrain indicates more consolidated material with higher Vs30. Uses log-linear interpolation between slope thresholds.
+
+2. **Coastal distance adjustments**: Near-coast sediments (alluvium, floodplain deposits) tend to be unconsolidated with lower Vs30. Values increase with distance from the coast.
+
+### Stage 4: Spatial Adjustment
+
+**Module**: `spatial.py`
+
+The spatial adjustment stage uses Multivariate Normal (MVN) conditioning to update raster pixels based on nearby observations. This is the most computationally intensive stage.
+
+**Algorithm**:
+
+1. **Identify affected pixels**: Before computing updates, the algorithm first identifies which pixels are within `max_dist_m` of any observation. This uses a bounding-box search to efficiently find candidate pixels.
+
+   For clustered observations (e.g., CPT data), an optimization is applied: only every Nth observation within each cluster is checked when determining affected pixels. Since clustered points are spatially close together, they largely affect the same pixels, so checking every Nth point still covers most of the cluster's spatial footprint. This subsampling may miss a small number of pixels at the outer edges of a cluster's influence zone—pixels that would have received only a weak update anyway since they are near the `max_dist_m` boundary. The parameter `obs_subsample_step_for_clustered` in `config.yaml` controls this behavior (default: 100). Setting it to 1 will check every observation in each cluster for complete coverage, but this significantly increases computation time for large clustered datasets.
 
-## Workflow
-Everything is run from the `vs30calc.py` script which can be run directly or installed.
-The 2 main modes of operation are grid based (which creates TIF files) and point based (where wanted locations are passed in as rows in a file, output is a CSV file). Everything is NZTM2000 (EPSG:2193, easting/northing) internally, WGS84 (EPSG:4326, longitude/latitude) is only used as input to the point based calculation.
+2. **Build covariance matrices**: For each affected pixel, a covariance matrix is constructed relating the pixel to nearby observations using an exponential correlation function:
+   ```
+   correlation = exp(-distance / phi)
+   ```
+   where `phi` is the correlation length (different for geology and terrain models).
 
-Both modes produce `.qgz` files which can be opened in `QGIS` to view outputs.
+3. **MVN conditioning**: The posterior Vs30 and uncertainty are computed using standard MVN conditioning formulas, which adjust the model prediction toward observed values based on spatial correlation.
 
-## Grid based calculation
-This mode is good for viewing the entire model, making sure everything appears good. This is the default mode of operation if no locations file is specified.
-The grid has parameters which control the extent and spacing.
-`vs30calc.py --help`
-A sample command for creating a smaller grid (~2 mins depending on machine):
-```shell
-vs30calc.py --xmin 1470050 --xmax 1580050 --ymin 5150050 --ymax 5250050
+4. **Apply updates**: Updated values are written to the output raster.
+
+**Key parameters** (from `config.yaml`):
+- `phi_geology`, `phi_terrain`: Correlation lengths (meters)
+- `max_dist_m`: Maximum distance to consider observations
+- `max_points`: Maximum observations per pixel update
+- `noisy`: Whether to apply uncertainty-based noise weighting
+- `cov_reduc`: Covariance reduction for dissimilar Vs30 values
+
+### Stage 5: Model Combination
+
+**Module**: `cli.py`
+
+The final stage combines geology and terrain model outputs using weighted averaging in log-space. This produces a geometric mean of the two models, which is appropriate for log-normally distributed Vs30 values.
+
+**Combination methods** (configured via `combination_method` in `config.yaml`):
+
+1. **Ratio-based weighting** (default): Set `combination_method` to a float value representing the ratio of geology weight to terrain weight:
+   - `1.0` = equal weighting (both models contribute equally)
+   - `2.0` = geology has twice the weight of terrain
+   - `0.5` = terrain has twice the weight of geology
+
+   The weights are computed as: `w_geology = ratio / (ratio + 1)`, `w_terrain = 1 / (ratio + 1)`
+
+2. **Standard deviation weighting**: Set `combination_method: "standard_deviation_weighting"` to weight models inversely by their uncertainty (lower uncertainty = more weight). The weighting uses the formula: `weight ~ (sigma^2)^-k`, where `k` is controlled by the `k_value` parameter.
+   - Higher `k_value` (e.g., 5.0) gives more weight to the model with lower uncertainty
+   - Lower `k_value` (e.g., 1.0) makes the weighting less sensitive to uncertainty differences
+   - Default is `k_value: 3.0`
+
+**Log-space combination**: The combination is performed in log-space (geometric averaging):
+```
+combined_vs30 = exp(w_g * log(geology_vs30) + w_t * log(terrain_vs30))
 ```
 
-## Point based calculation
-This mode is much faster because it only calculates the model values at given locations, not at millions of points around the country.
-Specify a file containing locations with `--ll-path`. The default is no header to skip (`--skip-rows 0`), longitude in the first column (`--lon-col-ix 0`), latitude in the second (`--lat-col-ix 1`) and space as the column separator (`--col-sep " "`). There are options for this as well.
-Note that grids are used for some intermediate parts of the calculation in this mode so the parameters are still applicable.
+For example, with equal weighting (`ratio=1.0`) and inputs of 200 and 400 m/s, the result is approximately 283 m/s (geometric mean), not 300 m/s (arithmetic mean).
+
+**Combined standard deviation**: The output uncertainty accounts for both the individual model uncertainties and the difference between model predictions using the mixture of log-normals formula.
+
+## Configuration
+
+All parameters are centralized in `config.yaml`. Key sections:
+
+- **Grid parameters**: Domain bounds and resolution (NZTM2000 coordinates)
+- **Spatial parameters**: Correlation lengths, max distance, max observations
+- **Bayesian parameters**: Prior weight, minimum sigma, DBSCAN settings
+- **Hybrid parameters**: Slope and coastal distance thresholds
+- **File paths**: Input/output locations
+
+## Coordinate System
+
+All coordinates use NZTM2000 (EPSG:2193) in meters. The `compute-at-locations` command accepts WGS84 lat/lon input and converts internally.
+
+## Scientific Reference
+
+The methodology is based on:
+
+> Foster, K.M., et al. (2019). "A Vs30 Map for New Zealand Based on Geologic and Terrain Proxy Variables and Field Measurements"
+
+See `reference_papers/foster_2019_nz_vs30_map.pdf` for detailed scientific background.
diff --git a/pyproject.toml b/pyproject.toml
index 68000014..905b7f2d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,28 +4,38 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "Vs30"
-version = "2.0"
+version = "2026.02.1"
 description = "NZ Vs30 Calculation"
-requires-python = ">=3.7"
+requires-python = ">=3.11"
 dependencies = [
     "numpy",
     "pandas",
-    "pyproj",
     "scikit-learn",
     "scipy",
-    "matplotlib",
-    "pytest",
     "geopandas",
+    "typer",
+    "pydantic>=2.0",
+    "threadpoolctl",
+    "qcore-utils",
+    "rasterio",
+    "tqdm",
+    "pyyaml",
+    "shapely",
+    "GDAL",
 ]
 
+[project.optional-dependencies]
+test = ["pytest"]
+types = ["ty"]
+
 [project.urls]
 Repository = "https://github.com/ucgmsim/Vs30"
 
 [project.scripts]
-run-vs30calc = "run_vs30calc:main"
+vs30 = "vs30.cli:app"
 
 [tool.setuptools]
-packages = ["vs30", "VsViewer"]
+packages = ["vs30"]
 
 [tool.setuptools.package-data]
 vs30 = [
@@ -36,4 +46,57 @@ vs30 = [
     "data/*.qgz",
     "data/coast/*",
     "data/qmap/*",
+    "resources/**/*.csv",
+    "resources/**/*.yaml",
+    "resources/**/*.npy",
+]
+
+[tool.ruff]
+exclude = ["VsViewer", "build", "temp"]
+
+[tool.deptry]
+# .venv must be listed explicitly because custom exclude overrides deptry's defaults.
+exclude = ["VsViewer", ".venv"]
+
+[tool.deptry.package_module_name_map]
+scikit-learn = ["sklearn"]
+qcore-utils = ["qcore"]
+pyyaml = ["yaml"]
+GDAL = ["osgeo"]
+
+[tool.coverage.run]
+omit = [
+    "vs30/cli.py",
+    "vs30/config.py",
+    "vs30/constants.py",
+    "vs30/parallel.py",
+]
+
+[tool.deptry.per_rule_ignores]
+DEP001 = ["conftest", "setuptools"]
+DEP003 = ["setuptools"]
+DEP004 = ["pytest"]
+
+[tool.numpydoc_validation]
+checks = [
+  "GL05",
+  "GL08",
+  "GL10",
+  "PR01",
+  "PR02",
+  "PR03",
+  "PR04",
+  "PR05",
+  "PR06",
+  "PR07",
+  "RT01",
+  "RT02",
+  "RT03",
+  "RT04",
+  "YD01",
+]
+# remember to use single quotes for regex in TOML
+exclude = [ # don't report on objects that match any of these regex
+  '\.undocumented_method$',
+  '\.__repr__$',
 ]
diff --git a/reference_papers/foster_2019_nz_vs30_map.pdf b/reference_papers/foster_2019_nz_vs30_map.pdf
new file mode 100644
index 00000000..22f90217
Binary files /dev/null and b/reference_papers/foster_2019_nz_vs30_map.pdf differ
diff --git a/run_vs30calc.py b/run_vs30calc.py
deleted file mode 100755
index 6846bd8b..00000000
--- a/run_vs30calc.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python
-"""
-Calculate Vs30 over a region (default) or specify points at which to output for instead.
-"""
-from pyproj import Transformer
-
-from vs30 import (
-    params,
-    vs30calc,
-)
-
-WGS2NZTM = Transformer.from_crs(4326, 2193, always_xy=True)
-
-# work on ~50 points per process
-PROCESS_CHUNK_SIZE = 50
-
-def main():
-    p_paths, p_sites, p_grid, p_ll, p_geol, p_terr, p_comb, nproc = params.load_args()
-    vs30calc.run_vs30calc(
-        p_paths,
-        p_sites,
-        p_grid,
-        p_ll,
-        p_geol,
-        p_terr,
-        p_comb,
-        nproc,
-    )
-
-if __name__ == "__main__":
-    main()
diff --git a/setup.py b/setup.py
index d660e7e0..4cc1e7e8 100644
--- a/setup.py
+++ b/setup.py
@@ -3,8 +3,8 @@
 """
 
 import os
-from shutil import rmtree
 import tarfile
+from shutil import rmtree
 
 from setuptools import setup
 from setuptools.command.build_py import build_py
diff --git a/tests/benchmarks/both/coast_distance.tif b/tests/benchmarks/both/coast_distance.tif
new file mode 100644
index 00000000..18736f28
Binary files /dev/null and b/tests/benchmarks/both/coast_distance.tif differ
diff --git a/tests/benchmarks/both/combined_vs30.tif b/tests/benchmarks/both/combined_vs30.tif
new file mode 100644
index 00000000..8469730e
Binary files /dev/null and b/tests/benchmarks/both/combined_vs30.tif differ
diff --git a/tests/benchmarks/both/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif b/tests/benchmarks/both/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..808a6622
Binary files /dev/null and b/tests/benchmarks/both/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/both/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/both/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..dc8b1c11
Binary files /dev/null and b/tests/benchmarks/both/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/both/gid.tif b/tests/benchmarks/both/gid.tif
new file mode 100644
index 00000000..32b1bb87
Binary files /dev/null and b/tests/benchmarks/both/gid.tif differ
diff --git a/tests/benchmarks/both/initial_geology_vs30_with_uncertainty.tif b/tests/benchmarks/both/initial_geology_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..d8d5ff1b
Binary files /dev/null and b/tests/benchmarks/both/initial_geology_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/both/initial_terrain_vs30_with_uncertainty.tif b/tests/benchmarks/both/initial_terrain_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..02ee354d
Binary files /dev/null and b/tests/benchmarks/both/initial_terrain_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/both/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/both/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..3ebb5c1c
--- /dev/null
+++ b/tests/benchmarks/both/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,16 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,181.51157794172812,0.5,peat,id 01 in Ahdi et al. (2017ab),181.51157794172812,0.15012640625749019,3,0.5,187.71508366993837,0.33572902885063954,9
+2,194.4683389459399,0.5,fill,id 04 in Ahdi et al. (2017ab),194.4683389459399,0.13390620734580594,3,0.5,194.4683389459399,0.5,3
+3,185.4206365628782,0.5,fluvial estuarine,id 05 in Ahdi et al. (2017ab),185.4206365628782,0.11632145451833677,3,0.5,192.3198171854376,0.31182882818785534,14
+4,271.050623687222,0.5,alluvium,id 06 in Ahdi et al. (2017ab),271.050623687222,0.243486579272276,3,0.5,271.050623687222,0.5,3
+5,326.0,0.5,lacustrine,id 08 in Ahdi et al. (2017ab),326.0,0.5,3,0.5,326.0,0.5,3
+6,190.70159627073537,0.5,beach bar dune,id 09 in Ahdi et al. (2017ab),190.70159627073537,0.080418831734922,3,0.5,192.74982025543537,0.2372947355952319,45
+7,145.33895104414677,0.5,fan,id 10 in Ahdi et al. (2017ab),145.33895104414677,0.13546051245996446,3,0.5,165.14954256184248,0.4163091087740311,6
+8,164.85344116704303,0.5,loess,id 11 in Ahdi et al. (2017ab),164.85344116704303,0.15834948222274747,3,0.5,332.019740991781,0.7878620403778702,7
+9,399.0,0.5,outwash,id 12 in Ahdi et al. (2017ab),399.0,0.5,3,0.5,399.0,0.5,3
+10,173.74481010305055,0.5,flood plain,id 13 in Ahdi et al. (2017ab),173.74481010305055,0.15591978457615316,3,0.5,190.62967196682564,0.2552229798133276,186
+11,453.0,0.512,moraine till,id 14 in Ahdi et al. (2017ab),453.0,0.512,3,0.5,453.0,0.512,3
+12,455.0,0.545,undifferentiated sediment,id 15 in Ahdi et al. (2017ab),455.0,0.545,3,0.5,455.0,0.545,3
+13,335.279503497185,0.602886888230288,terrace,id 16 in Ahdi et al. (2017ab),335.279503497185,0.602886888230288,3,0.5,335.279503497185,0.602886888230288,3
+14,635.0,0.995,volcanic,id 17 in Ahdi et al. (2017ab),635.0,0.995,3,0.5,635.0,0.995,3
+15,164.8603254916743,0.5,crystalline,id 18 in Ahdi et al. (2017ab),164.8603254916743,0.12633544168632158,3,0.5,246.94863946665183,0.8290634725265621,4
diff --git a/tests/benchmarks/both/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/both/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..08defed0
--- /dev/null
+++ b/tests/benchmarks/both/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,200.36332724223746,0.5,"Well dissected alpine summits, mountains, etc.",,200.36332724223746,0.15653383309575877,3,0.5,200.36332724223746,0.5,3
+2,179.31157152307816,0.5,"Large volcano, high block plateaus, etc.",,179.31157152307816,0.20220710285368573,3,0.5,179.31157152307816,0.5,3
+3,547.0,0.5,"Well dissected, low mountains, etc.",,547.0,0.5,3,0.5,547.0,0.5,3
+4,179.71374074809216,0.5,"Volcanic fan, foot slope of high block plateaus, etc.",,179.71374074809216,0.21257549340200446,3,0.5,179.71374074809216,0.5,3
+5,167.59000454261616,0.5,"Dissected plateaus, etc.",,167.59000454261616,0.1447369849050979,3,0.5,182.08582513755627,0.4670609040137967,4
+6,156.07691296151862,0.5,"Basalt lava plain, glaciated plateau, etc.",,156.07691296151862,0.14496422745717819,3,0.5,200.50098279399302,0.5008680662869902,7
+7,177.84231438757715,0.5,"Moderately eroded mountains, lava flow, etc.",,177.84231438757715,0.16881735531276854,3,0.5,340.1872752052702,0.892760314294055,5
+8,159.25082075056565,0.5,"Desert alluvial slope, volcanic fan, etc.",,159.25082075056565,0.07206428514190885,3,0.5,240.61947925287825,0.8417563737895198,4
+9,172.89193805945112,0.5,"Well eroded plain of weak rocks, etc.",,172.89193805945112,0.16967911789552953,3,0.5,180.92681271081705,0.3884038963318245,7
+10,153.4542778738812,0.5,"Valley, till plain, etc.",,153.4542778738812,0.11363428306947543,3,0.5,163.63852762249866,0.4581342354690034,4
+11,174.45274319461768,0.5,"Eroded plain of weak rocks, etc.",,174.45274319461768,0.1583715824326628,3,0.5,176.90604491717684,0.44506747116323025,4
+12,190.4209731857251,0.5,"Desert plain, delta plain, etc.",,190.4209731857251,0.21640818982110446,3,0.5,190.4209731857251,0.5,3
+13,165.84240127063467,0.5,"Incised terrace, etc.",Prior mean and standard deviation were estimated by Kevin Foster,165.84240127063467,0.1699046912072648,3,0.5,192.22513996556623,0.35316089469397977,9
+14,168.72828919445692,0.5,"Eroded alluvial fan, till plain, etc.",,168.72828919445692,0.16421525924870298,3,0.5,165.6062987185437,0.4455854830344294,4
+15,181.04847012947647,0.5,"Dune, incised terrace, etc.",,181.04847012947647,0.13076765264661944,3,0.5,191.4610471309754,0.242251722265076,125
+16,178.20511669018813,0.5,"Fluvial plain, alluvial fan, low-lying flat plains, etc.",,178.20511669018813,0.1515358199486605,3,0.5,190.39840241989188,0.2647747805645107,100
diff --git a/tests/benchmarks/both/slope.tif b/tests/benchmarks/both/slope.tif
new file mode 100644
index 00000000..61b59505
Binary files /dev/null and b/tests/benchmarks/both/slope.tif differ
diff --git a/tests/benchmarks/both/terrain_vs30_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/both/terrain_vs30_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..1521e95e
Binary files /dev/null and b/tests/benchmarks/both/terrain_vs30_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/both/tid.tif b/tests/benchmarks/both/tid.tif
new file mode 100644
index 00000000..22d20dd2
Binary files /dev/null and b/tests/benchmarks/both/tid.tif differ
diff --git a/tests/benchmarks/clustered_only/coast_distance.tif b/tests/benchmarks/clustered_only/coast_distance.tif
new file mode 100644
index 00000000..18736f28
Binary files /dev/null and b/tests/benchmarks/clustered_only/coast_distance.tif differ
diff --git a/tests/benchmarks/clustered_only/combined_vs30.tif b/tests/benchmarks/clustered_only/combined_vs30.tif
new file mode 100644
index 00000000..6d4e0c01
Binary files /dev/null and b/tests/benchmarks/clustered_only/combined_vs30.tif differ
diff --git a/tests/benchmarks/clustered_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif b/tests/benchmarks/clustered_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..24b16a10
Binary files /dev/null and b/tests/benchmarks/clustered_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/clustered_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/clustered_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..719e6e70
Binary files /dev/null and b/tests/benchmarks/clustered_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/clustered_only/gid.tif b/tests/benchmarks/clustered_only/gid.tif
new file mode 100644
index 00000000..32b1bb87
Binary files /dev/null and b/tests/benchmarks/clustered_only/gid.tif differ
diff --git a/tests/benchmarks/clustered_only/initial_geology_vs30_with_uncertainty.tif b/tests/benchmarks/clustered_only/initial_geology_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..7d990a4a
Binary files /dev/null and b/tests/benchmarks/clustered_only/initial_geology_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/clustered_only/initial_terrain_vs30_with_uncertainty.tif b/tests/benchmarks/clustered_only/initial_terrain_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..9c4b87dc
Binary files /dev/null and b/tests/benchmarks/clustered_only/initial_terrain_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/clustered_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/clustered_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..7d39e71a
--- /dev/null
+++ b/tests/benchmarks/clustered_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,16 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations
+1,162.892120019443,0.301033220758108,peat,id 01 in Ahdi et al. (2017ab),181.51157794172812,0.15012640625749019
+2,272.512711921894,0.280305955290694,fill,id 04 in Ahdi et al. (2017ab),194.4683389459399,0.13390620734580594
+3,199.502400319993,0.438753673163297,fluvial estuarine,id 05 in Ahdi et al. (2017ab),185.4206365628782,0.11632145451833677
+4,271.050623687222,0.243486579272276,alluvium,id 06 in Ahdi et al. (2017ab),271.050623687222,0.243486579272276
+5,326.0,0.5,lacustrine,id 08 in Ahdi et al. (2017ab),326.0,0.5
+6,204.373998521848,0.232196981798137,beach bar dune,id 09 in Ahdi et al. (2017ab),190.70159627073537,0.080418831734922
+7,246.6138371423,0.344601218802256,fan,id 10 in Ahdi et al. (2017ab),145.33895104414677,0.13546051245996446
+8,472.749388885179,0.354562104171167,loess,id 11 in Ahdi et al. (2017ab),164.85344116704303,0.15834948222274747
+9,399.0,0.5,outwash,id 12 in Ahdi et al. (2017ab),399.0,0.5
+10,197.472849225266,0.202629769603115,flood plain,id 13 in Ahdi et al. (2017ab),173.74481010305055,0.15591978457615316
+11,453.0,0.512,moraine till,id 14 in Ahdi et al. (2017ab),453.0,0.512
+12,455.0,0.545,undifferentiated sediment,id 15 in Ahdi et al. (2017ab),455.0,0.545
+13,335.279503497185,0.602886888230288,terrace,id 16 in Ahdi et al. (2017ab),335.279503497185,0.602886888230288
+14,635.0,0.995,volcanic,id 17 in Ahdi et al. (2017ab),635.0,0.995
+15,690.974348966211,0.446036993981441,crystalline,id 18 in Ahdi et al. (2017ab),164.8603254916743,0.12633544168632158
diff --git a/tests/benchmarks/clustered_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/clustered_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..0619d2bc
--- /dev/null
+++ b/tests/benchmarks/clustered_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations
+1,519.0,0.5,"Well dissected alpine summits, mountains, etc.",,200.36332724223746,0.15653383309575877
+2,393.0,0.5,"Large volcano, high block plateaus, etc.",,179.31157152307816,0.20220710285368573
+3,547.0,0.5,"Well dissected, low mountains, etc.",,547.0,0.5
+4,459.0,0.5,"Volcanic fan, foot slope of high block plateaus, etc.",,179.71374074809216,0.21257549340200446
+5,323.504047697085,0.407430975749267,"Dissected plateaus, etc.",,167.59000454261616,0.1447369849050979
+6,300.639538792274,0.306594194335118,"Basalt lava plain, glaciated plateau, etc.",,156.07691296151862,0.14496422745717819
+7,535.786417756242,0.380788655293195,"Moderately eroded mountains, lava flow, etc.",,177.84231438757715,0.16881735531276854
+8,514.970575501527,0.380788655293195,"Desert alluvial slope, volcanic fan, etc.",,159.25082075056565,0.07206428514190885
+9,284.470950299338,0.360555127546399,"Well eroded plain of weak rocks, etc.",,172.89193805945112,0.16967911789552953
+10,317.368444092771,0.33166247903554,"Valley, till plain, etc.",,153.4542778738812,0.11363428306947543
+11,266.870166096091,0.4,"Eroded plain of weak rocks, etc.",,174.45274319461768,0.1583715824326628
+12,297.0,0.5,"Desert plain, delta plain, etc.",,190.4209731857251,0.21640818982110446
+13,216.62368022053,0.254950975679639,"Incised terrace, etc.",Prior mean and standard deviation were estimated by Kevin Foster,165.84240127063467,0.1699046912072648
+14,241.80869962047,0.307482445914323,"Eroded alluvial fan, till plain, etc.",,168.72828919445692,0.16421525924870298
+15,198.64481157494,0.205256370217328,"Dune, incised terrace, etc.",,181.04847012947647,0.13076765264661944
+16,202.125866002814,0.206820130727133,"Fluvial plain, alluvial fan, low-lying flat plains, etc.",,178.20511669018813,0.1515358199486605
diff --git a/tests/benchmarks/clustered_only/slope.tif b/tests/benchmarks/clustered_only/slope.tif
new file mode 100644
index 00000000..61b59505
Binary files /dev/null and b/tests/benchmarks/clustered_only/slope.tif differ
diff --git a/tests/benchmarks/clustered_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/clustered_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..6835d6b4
Binary files /dev/null and b/tests/benchmarks/clustered_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/clustered_only/tid.tif b/tests/benchmarks/clustered_only/tid.tif
new file mode 100644
index 00000000..22d20dd2
Binary files /dev/null and b/tests/benchmarks/clustered_only/tid.tif differ
diff --git a/tests/benchmarks/independent_only/coast_distance.tif b/tests/benchmarks/independent_only/coast_distance.tif
new file mode 100644
index 00000000..18736f28
Binary files /dev/null and b/tests/benchmarks/independent_only/coast_distance.tif differ
diff --git a/tests/benchmarks/independent_only/combined_vs30.tif b/tests/benchmarks/independent_only/combined_vs30.tif
new file mode 100644
index 00000000..5bbe9d1f
Binary files /dev/null and b/tests/benchmarks/independent_only/combined_vs30.tif differ
diff --git a/tests/benchmarks/independent_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif b/tests/benchmarks/independent_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..e1c5f276
Binary files /dev/null and b/tests/benchmarks/independent_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/independent_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/independent_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..7c4bb175
Binary files /dev/null and b/tests/benchmarks/independent_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/independent_only/gid.tif b/tests/benchmarks/independent_only/gid.tif
new file mode 100644
index 00000000..32b1bb87
Binary files /dev/null and b/tests/benchmarks/independent_only/gid.tif differ
diff --git a/tests/benchmarks/independent_only/initial_geology_vs30_with_uncertainty.tif b/tests/benchmarks/independent_only/initial_geology_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..b758dfb6
Binary files /dev/null and b/tests/benchmarks/independent_only/initial_geology_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/independent_only/initial_terrain_vs30_with_uncertainty.tif b/tests/benchmarks/independent_only/initial_terrain_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..2427e575
Binary files /dev/null and b/tests/benchmarks/independent_only/initial_terrain_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/independent_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/independent_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..fc93fe66
--- /dev/null
+++ b/tests/benchmarks/independent_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,16 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,162.892120019443,0.5,peat,id 01 in Ahdi et al. (2017ab),3,0.5,181.0635728230615,0.34313539457891296,9
+2,272.512711921894,0.5,fill,id 04 in Ahdi et al. (2017ab),3,0.5,272.512711921894,0.5,3
+3,199.502400319993,0.5,fluvial estuarine,id 05 in Ahdi et al. (2017ab),3,0.5,195.36024564804566,0.31143744178459426,14
+4,271.050623687222,0.5,alluvium,id 06 in Ahdi et al. (2017ab),3,0.5,271.050623687222,0.5,3
+5,326.0,0.5,lacustrine,id 08 in Ahdi et al. (2017ab),3,0.5,326.0,0.5,3
+6,204.373998521848,0.5,beach bar dune,id 09 in Ahdi et al. (2017ab),3,0.5,193.64163279165547,0.23771512635650285,45
+7,246.6138371423,0.5,fan,id 10 in Ahdi et al. (2017ab),3,0.5,215.12708111538444,0.4190978030658139,6
+8,472.749388885179,0.5,loess,id 11 in Ahdi et al. (2017ab),3,0.5,521.4941732201644,0.5102005833373486,7
+9,399.0,0.5,outwash,id 12 in Ahdi et al. (2017ab),3,0.5,399.0,0.5,3
+10,197.472849225266,0.5,flood plain,id 13 in Ahdi et al. (2017ab),3,0.5,191.02367809095,0.25498202375503515,186
+11,453.0,0.512,moraine till,id 14 in Ahdi et al. (2017ab),3,0.5,453.0,0.512,3
+12,455.0,0.545,undifferentiated sediment,id 15 in Ahdi et al. (2017ab),3,0.5,455.0,0.545,3
+13,335.279503497185,0.602886888230288,terrace,id 16 in Ahdi et al. (2017ab),3,0.5,335.279503497185,0.602886888230288,3
+14,635.0,0.995,volcanic,id 17 in Ahdi et al. (2017ab),3,0.5,635.0,0.995,3
+15,690.974348966211,0.5,crystalline,id 18 in Ahdi et al. (2017ab),3,0.5,723.3791170309055,0.45144365449990315,4
diff --git a/tests/benchmarks/independent_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/independent_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..dd374a7d
--- /dev/null
+++ b/tests/benchmarks/independent_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,519.0,0.5,"Well dissected alpine summits, mountains, etc.",,3,0.5,519.0,0.5,3
+2,393.0,0.5,"Large volcano, high block plateaus, etc.",,3,0.5,393.0,0.5,3
+3,547.0,0.5,"Well dissected, low mountains, etc.",,3,0.5,547.0,0.5,3
+4,459.0,0.5,"Volcanic fan, foot slope of high block plateaus, etc.",,3,0.5,459.0,0.5,3
+5,323.504047697085,0.5,"Dissected plateaus, etc.",,3,0.5,298.1946471208455,0.46627215687941415,4
+6,300.639538792274,0.5,"Basalt lava plain, glaciated plateau, etc.",,3,0.5,265.5424608824452,0.4640849698357664,7
+7,535.786417756242,0.5,"Moderately eroded mountains, lava flow, etc.",,3,0.5,659.3135840964096,0.4801684673770942,5
+8,514.970575501527,0.5,"Desert alluvial slope, volcanic fan, etc.",,3,0.5,580.2382228463999,0.49012061749339497,4
+9,284.470950299338,0.5,"Well eroded plain of weak rocks, etc.",,3,0.5,223.9686091002917,0.4384012040921267,7
+10,317.368444092771,0.5,"Valley, till plain, etc.",,3,0.5,282.2111187074934,0.488726791296901,4
+11,266.870166096091,0.5,"Eroded plain of weak rocks, etc.",,3,0.5,243.337667227288,0.4722971897097558,4
+12,297.0,0.5,"Desert plain, delta plain, etc.",,3,0.5,297.0,0.5,3
+13,216.62368022053,0.5,"Incised terrace, etc.",Prior mean and standard deviation were estimated by Kevin Foster,3,0.5,210.12624674963166,0.33806671363813273,9
+14,241.80869962047,0.5,"Eroded alluvial fan, till plain, etc.",,3,0.5,216.91513962386026,0.48260584936623807,4
+15,198.64481157494,0.5,"Dune, incised terrace, etc.",,3,0.5,191.88773060066958,0.24215378436194213,125
+16,202.125866002814,0.5,"Fluvial plain, alluvial fan, low-lying flat plains, etc.",,3,0.5,191.11921447732902,0.2647020565703985,100
diff --git a/tests/benchmarks/independent_only/slope.tif b/tests/benchmarks/independent_only/slope.tif
new file mode 100644
index 00000000..61b59505
Binary files /dev/null and b/tests/benchmarks/independent_only/slope.tif differ
diff --git a/tests/benchmarks/independent_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/independent_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..c87a71ea
Binary files /dev/null and b/tests/benchmarks/independent_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/independent_only/tid.tif b/tests/benchmarks/independent_only/tid.tif
new file mode 100644
index 00000000..22d20dd2
Binary files /dev/null and b/tests/benchmarks/independent_only/tid.tif differ
diff --git a/tests/benchmarks/nz_cities_vs30.csv b/tests/benchmarks/nz_cities_vs30.csv
new file mode 100644
index 00000000..09be529a
--- /dev/null
+++ b/tests/benchmarks/nz_cities_vs30.csv
@@ -0,0 +1,16 @@
+city,latitude,longitude,easting,northing,geology_id,geology_vs30,geology_stdv,geology_vs30_hybrid,geology_stdv_hybrid,geology_mvn_vs30,geology_mvn_stdv,terrain_id,terrain_vs30,terrain_stdv,terrain_mvn_vs30,terrain_mvn_stdv,vs30,stdv
+Auckland,-36.8485,174.7633,1757209.2534778407,5920482.808890771,12,455.0,0.545,455.0,0.545,142.7974061456941,0.21684733316208996,9,284.470950299338,0.360555127546399,152.3962862341263,0.20159067938132672,147.51879331281825,0.2118700071262805
+Wellington,-41.2865,174.7762,1748735.5530602792,5427916.478887198,2,272.512711921894,0.280305955290694,272.512711921894,0.280305955290694,292.7123437154324,0.13401540999754394,12,297.0,0.5,286.74410183730816,0.3202844447242645,289.71285455667163,0.24571776422786076
+Christchurch,-43.5321,172.6362,1570604.4091699158,5180029.367580267,10,197.472849225266,0.202629769603115,197.472849225266,0.202629769603115,167.13246976725165,0.074657481122376,15,198.64481157494,0.205256370217328,166.16040207503622,0.09048620296907996,166.6457271469039,0.08300152143915127
+Hamilton,-37.787,175.2793,1800707.1884579044,5815370.483291569,4,271.050623687222,0.243486579272276,271.050623687222,0.243486579272276,161.2121662608303,0.12752748119924417,15,198.64481157494,0.205256370217328,156.26150540547326,0.13706196188567604,158.71753460029166,0.13329602348381503
+Tauranga,-37.6878,176.1651,1879097.4223683695,5824107.691790847,13,335.279503497185,0.602886888230288,335.279503497185,0.602886888230288,145.19273501137513,0.2181061173402524,15,198.64481157494,0.205256370217328,144.96868902098888,0.11195094741537384,145.08066876731334,0.17335582692433424
+Napier,-39.4928,176.912,1936435.0119994713,5621223.7608034955,6,204.373998521848,0.232196981798137,204.373998521848,0.232196981798137,205.8010896471723,0.17160254871946543,10,317.368444092771,0.33166247903554,272.0710933606872,0.3157992105997089,236.6274022067752,0.28994814612713327
+Dunedin,-45.8788,170.5028,1406216.0743720918,4916386.023742298,2,272.512711921894,0.280305955290694,272.512711921894,0.280305955290694,154.79663917518448,0.2049401794787084,13,216.62368022053,0.254950975679639,162.7898825197699,0.21953429071266786,158.74295797226247,0.21384953270222562
+Palmerston North,-40.3523,175.6082,1821499.3071368549,5529874.200868085,4,271.050623687222,0.243486579272276,271.050623687222,0.243486579272276,252.10054510947745,0.2412587665911717,16,202.125866002814,0.206820130727133,201.4712983057012,0.20664414739239342,225.3686405132299,0.25103403270226826
+Nelson,-41.2706,173.284,1623786.895669358,5431164.177171369,4,271.050623687222,0.243486579272276,271.050623687222,0.243486579272276,201.67292054705388,0.15665489867178822,16,202.125866002814,0.206820130727133,179.95799472791055,0.16306922089072648,190.50631058463404,0.16973749576215666
+Rotorua,-38.1368,176.2497,1884820.7212518204,5774015.521418423,5,326.0,0.5,326.0,0.5,126.21991338998657,0.39469538200838056,15,198.64481157494,0.205256370217328,148.2507923766345,0.15355793952744748,136.79255160196294,0.31008516306530925
+New Plymouth,-39.0556,174.0752,1693031.6815514045,5676503.364156256,13,335.279503497185,0.602886888230288,335.279503497185,0.602886888230288,150.75426688468795,0.45829873975014424,255,,,,,,
+Whangarei,-35.7275,174.3166,1719072.2738431352,6045476.145548794,4,271.050623687222,0.243486579272276,271.050623687222,0.243486579272276,159.73446496510957,0.1801722941985794,13,216.62368022053,0.254950975679639,160.28054182630646,0.21261548367998462,160.00727043776152,0.19707006771688435
+Invercargill,-46.4132,168.3538,1242940.486519675,4849543.892746449,13,335.279503497185,0.602886888230288,335.279503497185,0.602886888230288,293.5499533820199,0.5987778012742518,15,198.64481157494,0.205256370217328,196.58195514382436,0.20474770897744868,240.22203014756155,0.4903286196332181
+Hastings,-39.6381,176.8493,1930349.784650855,5605323.875640106,4,271.050623687222,0.243486579272276,271.050623687222,0.243486579272276,160.20957073124177,0.12760622718736858,16,202.125866002814,0.206820130727133,161.16684595622436,0.1272113408297824,160.68749548970783,0.12744375140082054
+Queenstown,-45.0312,168.6626,1258332.2045771312,5004425.268503231,6,204.373998521848,0.232196981798137,204.373998521848,0.232196981798137,176.65817273932518,0.16236062340013346,10,317.368444092771,0.33166247903554,218.73631566056912,0.26155783384019143,196.57456050142497,0.24248371480311007
diff --git a/tests/benchmarks/small_both/coast_distance.tif b/tests/benchmarks/small_both/coast_distance.tif
new file mode 100644
index 00000000..5a05edce
Binary files /dev/null and b/tests/benchmarks/small_both/coast_distance.tif differ
diff --git a/tests/benchmarks/small_both/combined_vs30.tif b/tests/benchmarks/small_both/combined_vs30.tif
new file mode 100644
index 00000000..f4e60bf0
Binary files /dev/null and b/tests/benchmarks/small_both/combined_vs30.tif differ
diff --git a/tests/benchmarks/small_both/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif b/tests/benchmarks/small_both/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..15daedb8
Binary files /dev/null and b/tests/benchmarks/small_both/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_both/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/small_both/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..e48a6f15
Binary files /dev/null and b/tests/benchmarks/small_both/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_both/gid.tif b/tests/benchmarks/small_both/gid.tif
new file mode 100644
index 00000000..de240a0f
Binary files /dev/null and b/tests/benchmarks/small_both/gid.tif differ
diff --git a/tests/benchmarks/small_both/initial_geology_vs30_with_uncertainty.tif b/tests/benchmarks/small_both/initial_geology_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..29b4e8ca
Binary files /dev/null and b/tests/benchmarks/small_both/initial_geology_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_both/initial_terrain_vs30_with_uncertainty.tif b/tests/benchmarks/small_both/initial_terrain_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..cad9aeb9
Binary files /dev/null and b/tests/benchmarks/small_both/initial_terrain_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_both/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/small_both/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..9f4f9ab0
--- /dev/null
+++ b/tests/benchmarks/small_both/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,16 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,181.51157794172764,0.5,peat,id 01 in Ahdi et al. (2017ab),181.51157794172764,0.1501264062574902,3,0.5,187.7150836699382,0.33572902885063954,9
+2,194.46833894594008,0.5,fill,id 04 in Ahdi et al. (2017ab),194.46833894594008,0.13390620734580597,3,0.5,194.46833894594008,0.5,3
+3,185.42063656287655,0.5,fluvial estuarine,id 05 in Ahdi et al. (2017ab),185.42063656287655,0.11632145451833674,3,0.5,192.3198171854371,0.3118288281878557,14
+4,271.050623687222,0.5,alluvium,id 06 in Ahdi et al. (2017ab),271.050623687222,0.243486579272276,3,0.5,271.050623687222,0.5,3
+5,326.0,0.5,lacustrine,id 08 in Ahdi et al. (2017ab),326.0,0.5,3,0.5,326.0,0.5,3
+6,190.70159627073537,0.5,beach bar dune,id 09 in Ahdi et al. (2017ab),190.70159627073537,0.08041883173492194,3,0.5,192.7498202554352,0.2372947355952319,45
+7,145.33895104414677,0.5,fan,id 10 in Ahdi et al. (2017ab),145.33895104414677,0.13546051245996443,3,0.5,165.14954256184234,0.4163091087740311,6
+8,164.85344116704275,0.5,loess,id 11 in Ahdi et al. (2017ab),164.85344116704275,0.15834948222274753,3,0.5,332.019740991781,0.7878620403778707,7
+9,399.0,0.5,outwash,id 12 in Ahdi et al. (2017ab),399.0,0.5,3,0.5,399.0,0.5,3
+10,173.74481010305178,0.5,flood plain,id 13 in Ahdi et al. (2017ab),173.74481010305178,0.1559197845761531,3,0.5,190.62967196682445,0.2552229798133281,186
+11,453.0,0.512,moraine till,id 14 in Ahdi et al. (2017ab),453.0,0.512,3,0.5,453.0,0.512,3
+12,455.0,0.545,undifferentiated sediment,id 15 in Ahdi et al. (2017ab),455.0,0.545,3,0.5,455.0,0.545,3
+13,335.279503497185,0.602886888230288,terrace,id 16 in Ahdi et al. (2017ab),335.279503497185,0.602886888230288,3,0.5,335.279503497185,0.602886888230288,3
+14,635.0,0.995,volcanic,id 17 in Ahdi et al. (2017ab),635.0,0.995,3,0.5,635.0,0.995,3
+15,164.86032549167416,0.5,crystalline,id 18 in Ahdi et al. (2017ab),164.86032549167416,0.12633544168632158,3,0.5,246.94863946665183,0.8290634725265624,4
diff --git a/tests/benchmarks/small_both/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/small_both/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..22597952
--- /dev/null
+++ b/tests/benchmarks/small_both/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,200.36332724223746,0.5,"Well dissected alpine summits, mountains, etc.",,200.36332724223746,0.15653383309575877,3,0.5,200.36332724223746,0.5,3
+2,179.31157152307816,0.5,"Large volcano, high block plateaus, etc.",,179.31157152307816,0.20220710285368576,3,0.5,179.31157152307816,0.5,3
+3,547.0,0.5,"Well dissected, low mountains, etc.",,547.0,0.5,3,0.5,547.0,0.5,3
+4,179.71374074809216,0.5,"Volcanic fan, foot slope of high block plateaus, etc.",,179.71374074809216,0.21257549340200446,3,0.5,179.71374074809216,0.5,3
+5,167.59000454261601,0.5,"Dissected plateaus, etc.",,167.59000454261601,0.14473698490509787,3,0.5,182.08582513755627,0.46706090401379685,4
+6,156.07691296151862,0.5,"Basalt lava plain, glaciated plateau, etc.",,156.07691296151862,0.14496422745717819,3,0.5,200.50098279399285,0.5008680662869909,7
+7,177.84231438757715,0.5,"Moderately eroded mountains, lava flow, etc.",,177.84231438757715,0.16881735531276856,3,0.5,340.1872752052702,0.892760314294055,5
+8,159.25082075056565,0.5,"Desert alluvial slope, volcanic fan, etc.",,159.25082075056565,0.07206428514190885,3,0.5,240.61947925287825,0.8417563737895198,4
+9,172.89193805945098,0.5,"Well eroded plain of weak rocks, etc.",,172.89193805945098,0.16967911789552953,3,0.5,180.92681271081688,0.3884038963318245,7
+10,153.45427787388107,0.5,"Valley, till plain, etc.",,153.45427787388107,0.11363428306947541,3,0.5,163.63852762249851,0.4581342354690035,4
+11,174.45274319461782,0.5,"Eroded plain of weak rocks, etc.",,174.45274319461782,0.15837158243266283,3,0.5,176.90604491717684,0.44506747116323025,4
+12,190.42097318572561,0.5,"Desert plain, delta plain, etc.",,190.42097318572561,0.2164081898211045,3,0.5,190.42097318572561,0.5,3
+13,165.84240127063435,0.5,"Incised terrace, etc.",Prior mean and standard deviation were estimated by Kevin Foster,165.84240127063435,0.16990469120726479,3,0.5,192.22513996556606,0.35316089469398,9
+14,168.72828919445692,0.5,"Eroded alluvial fan, till plain, etc.",,168.72828919445692,0.164215259248703,3,0.5,165.60629871854354,0.4455854830344294,4
+15,181.04847012948144,0.5,"Dune, incised terrace, etc.",,181.04847012948144,0.13076765264661896,3,0.5,191.46104713097557,0.2422517222650758,125
+16,178.20511669019098,0.5,"Fluvial plain, alluvial fan, low-lying flat plains, etc.",,178.20511669019098,0.15153581994866056,3,0.5,190.3984024198917,0.26477478056451076,100
diff --git a/tests/benchmarks/small_both/slope.tif b/tests/benchmarks/small_both/slope.tif
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index 00000000..af86866d
Binary files /dev/null and b/tests/benchmarks/small_both/slope.tif differ
diff --git a/tests/benchmarks/small_both/terrain_vs30_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/small_both/terrain_vs30_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..373c29d8
Binary files /dev/null and b/tests/benchmarks/small_both/terrain_vs30_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_both/tid.tif b/tests/benchmarks/small_both/tid.tif
new file mode 100644
index 00000000..243c7aa5
Binary files /dev/null and b/tests/benchmarks/small_both/tid.tif differ
diff --git a/tests/benchmarks/small_clustered_only/coast_distance.tif b/tests/benchmarks/small_clustered_only/coast_distance.tif
new file mode 100644
index 00000000..5a05edce
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/coast_distance.tif differ
diff --git a/tests/benchmarks/small_clustered_only/combined_vs30.tif b/tests/benchmarks/small_clustered_only/combined_vs30.tif
new file mode 100644
index 00000000..9d185699
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/combined_vs30.tif differ
diff --git a/tests/benchmarks/small_clustered_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif b/tests/benchmarks/small_clustered_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..e4f3e845
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_clustered_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/small_clustered_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..8ee24569
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_clustered_only/gid.tif b/tests/benchmarks/small_clustered_only/gid.tif
new file mode 100644
index 00000000..de240a0f
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/gid.tif differ
diff --git a/tests/benchmarks/small_clustered_only/initial_geology_vs30_with_uncertainty.tif b/tests/benchmarks/small_clustered_only/initial_geology_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..dfac3ba1
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/initial_geology_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_clustered_only/initial_terrain_vs30_with_uncertainty.tif b/tests/benchmarks/small_clustered_only/initial_terrain_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..38218246
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/initial_terrain_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_clustered_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/small_clustered_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..14f6baea
--- /dev/null
+++ b/tests/benchmarks/small_clustered_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,16 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations
+1,162.892120019443,0.301033220758108,peat,id 01 in Ahdi et al. (2017ab),181.51157794172764,0.1501264062574902
+2,272.512711921894,0.280305955290694,fill,id 04 in Ahdi et al. (2017ab),194.46833894594008,0.13390620734580597
+3,199.502400319993,0.438753673163297,fluvial estuarine,id 05 in Ahdi et al. (2017ab),185.42063656287655,0.11632145451833674
+4,271.050623687222,0.243486579272276,alluvium,id 06 in Ahdi et al. (2017ab),271.050623687222,0.243486579272276
+5,326.0,0.5,lacustrine,id 08 in Ahdi et al. (2017ab),326.0,0.5
+6,204.373998521848,0.232196981798137,beach bar dune,id 09 in Ahdi et al. (2017ab),190.70159627073537,0.08041883173492194
+7,246.6138371423,0.344601218802256,fan,id 10 in Ahdi et al. (2017ab),145.33895104414677,0.13546051245996443
+8,472.749388885179,0.354562104171167,loess,id 11 in Ahdi et al. (2017ab),164.85344116704275,0.15834948222274753
+9,399.0,0.5,outwash,id 12 in Ahdi et al. (2017ab),399.0,0.5
+10,197.472849225266,0.202629769603115,flood plain,id 13 in Ahdi et al. (2017ab),173.74481010305178,0.1559197845761531
+11,453.0,0.512,moraine till,id 14 in Ahdi et al. (2017ab),453.0,0.512
+12,455.0,0.545,undifferentiated sediment,id 15 in Ahdi et al. (2017ab),455.0,0.545
+13,335.279503497185,0.602886888230288,terrace,id 16 in Ahdi et al. (2017ab),335.279503497185,0.602886888230288
+14,635.0,0.995,volcanic,id 17 in Ahdi et al. (2017ab),635.0,0.995
+15,690.974348966211,0.446036993981441,crystalline,id 18 in Ahdi et al. (2017ab),164.86032549167416,0.12633544168632158
diff --git a/tests/benchmarks/small_clustered_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/small_clustered_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..3b788b05
--- /dev/null
+++ b/tests/benchmarks/small_clustered_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,posterior_mean_vs30_km_per_s_clustered_observations,posterior_standard_deviation_vs30_km_per_s_clustered_observations
+1,519.0,0.5,"Well dissected alpine summits, mountains, etc.",,200.36332724223746,0.15653383309575877
+2,393.0,0.5,"Large volcano, high block plateaus, etc.",,179.31157152307816,0.20220710285368576
+3,547.0,0.5,"Well dissected, low mountains, etc.",,547.0,0.5
+4,459.0,0.5,"Volcanic fan, foot slope of high block plateaus, etc.",,179.71374074809216,0.21257549340200446
+5,323.504047697085,0.407430975749267,"Dissected plateaus, etc.",,167.59000454261601,0.14473698490509787
+6,300.639538792274,0.306594194335118,"Basalt lava plain, glaciated plateau, etc.",,156.07691296151862,0.14496422745717819
+7,535.786417756242,0.380788655293195,"Moderately eroded mountains, lava flow, etc.",,177.84231438757715,0.16881735531276856
+8,514.970575501527,0.380788655293195,"Desert alluvial slope, volcanic fan, etc.",,159.25082075056565,0.07206428514190885
+9,284.470950299338,0.360555127546399,"Well eroded plain of weak rocks, etc.",,172.89193805945098,0.16967911789552953
+10,317.368444092771,0.33166247903554,"Valley, till plain, etc.",,153.45427787388107,0.11363428306947541
+11,266.870166096091,0.4,"Eroded plain of weak rocks, etc.",,174.45274319461782,0.15837158243266283
+12,297.0,0.5,"Desert plain, delta plain, etc.",,190.42097318572561,0.2164081898211045
+13,216.62368022053,0.254950975679639,"Incised terrace, etc.",Prior mean and standard deviation were estimated by Kevin Foster,165.84240127063435,0.16990469120726479
+14,241.80869962047,0.307482445914323,"Eroded alluvial fan, till plain, etc.",,168.72828919445692,0.164215259248703
+15,198.64481157494,0.205256370217328,"Dune, incised terrace, etc.",,181.04847012948144,0.13076765264661896
+16,202.125866002814,0.206820130727133,"Fluvial plain, alluvial fan, low-lying flat plains, etc.",,178.20511669019098,0.15153581994866056
diff --git a/tests/benchmarks/small_clustered_only/slope.tif b/tests/benchmarks/small_clustered_only/slope.tif
new file mode 100644
index 00000000..af86866d
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/slope.tif differ
diff --git a/tests/benchmarks/small_clustered_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/small_clustered_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..f04790a4
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_clustered_only/tid.tif b/tests/benchmarks/small_clustered_only/tid.tif
new file mode 100644
index 00000000..243c7aa5
Binary files /dev/null and b/tests/benchmarks/small_clustered_only/tid.tif differ
diff --git a/tests/benchmarks/small_independent_only/coast_distance.tif b/tests/benchmarks/small_independent_only/coast_distance.tif
new file mode 100644
index 00000000..154520a6
Binary files /dev/null and b/tests/benchmarks/small_independent_only/coast_distance.tif differ
diff --git a/tests/benchmarks/small_independent_only/combined_vs30.tif b/tests/benchmarks/small_independent_only/combined_vs30.tif
new file mode 100644
index 00000000..3e600229
Binary files /dev/null and b/tests/benchmarks/small_independent_only/combined_vs30.tif differ
diff --git a/tests/benchmarks/small_independent_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif b/tests/benchmarks/small_independent_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..7a8a76ed
Binary files /dev/null and b/tests/benchmarks/small_independent_only/geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_independent_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/small_independent_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..2787d78d
Binary files /dev/null and b/tests/benchmarks/small_independent_only/geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_independent_only/gid.tif b/tests/benchmarks/small_independent_only/gid.tif
new file mode 100644
index 00000000..1bf6d983
Binary files /dev/null and b/tests/benchmarks/small_independent_only/gid.tif differ
diff --git a/tests/benchmarks/small_independent_only/initial_geology_vs30_with_uncertainty.tif b/tests/benchmarks/small_independent_only/initial_geology_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..74302380
Binary files /dev/null and b/tests/benchmarks/small_independent_only/initial_geology_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_independent_only/initial_terrain_vs30_with_uncertainty.tif b/tests/benchmarks/small_independent_only/initial_terrain_vs30_with_uncertainty.tif
new file mode 100644
index 00000000..ef67de90
Binary files /dev/null and b/tests/benchmarks/small_independent_only/initial_terrain_vs30_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_independent_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/small_independent_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..e0618c0a
--- /dev/null
+++ b/tests/benchmarks/small_independent_only/posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,16 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,162.892120019443,0.5,peat,id 01 in Ahdi et al. (2017ab),3,0.5,171.4280099463716,0.4129022810867317,5
+2,272.512711921894,0.5,fill,id 04 in Ahdi et al. (2017ab),3,0.5,272.512711921894,0.5,3
+3,199.502400319993,0.5,fluvial estuarine,id 05 in Ahdi et al. (2017ab),3,0.5,199.20702490538733,0.44441713088909907,4
+4,271.050623687222,0.5,alluvium,id 06 in Ahdi et al. (2017ab),3,0.5,271.050623687222,0.5,3
+5,326.0,0.5,lacustrine,id 08 in Ahdi et al. (2017ab),3,0.5,326.0,0.5,3
+6,204.373998521848,0.5,beach bar dune,id 09 in Ahdi et al. (2017ab),3,0.5,201.54123626877922,0.34693137066301066,8
+7,246.6138371423,0.5,fan,id 10 in Ahdi et al. (2017ab),3,0.5,246.6138371423,0.5,3
+8,472.749388885179,0.5,loess,id 11 in Ahdi et al. (2017ab),3,0.5,472.749388885179,0.5,3
+9,399.0,0.5,outwash,id 12 in Ahdi et al. (2017ab),3,0.5,399.0,0.5,3
+10,197.472849225266,0.5,flood plain,id 13 in Ahdi et al. (2017ab),3,0.5,188.77661747816614,0.2614887579220092,35
+11,453.0,0.512,moraine till,id 14 in Ahdi et al. (2017ab),3,0.5,453.0,0.512,3
+12,455.0,0.545,undifferentiated sediment,id 15 in Ahdi et al. (2017ab),3,0.5,455.0,0.545,3
+13,335.279503497185,0.602886888230288,terrace,id 16 in Ahdi et al. (2017ab),3,0.5,335.279503497185,0.602886888230288,3
+14,635.0,0.995,volcanic,id 17 in Ahdi et al. (2017ab),3,0.5,635.0,0.995,3
+15,690.974348966211,0.5,crystalline,id 18 in Ahdi et al. (2017ab),3,0.5,690.974348966211,0.5,3
diff --git a/tests/benchmarks/small_independent_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/tests/benchmarks/small_independent_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..968b625e
--- /dev/null
+++ b/tests/benchmarks/small_independent_only/posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id,prior_mean_vs30_km_per_s,prior_standard_deviation_vs30_km_per_s,description,comment,assumed_num_prior_observations,enforced_min_sigma,posterior_mean_vs30_km_per_s_independent_observations,posterior_standard_deviation_vs30_km_per_s_independent_observations,posterior_num_observations_independent_observations
+1,519.0,0.5,"Well dissected alpine summits, mountains, etc.",,3,0.5,519.0,0.5,3
+2,393.0,0.5,"Large volcano, high block plateaus, etc.",,3,0.5,393.0,0.5,3
+3,547.0,0.5,"Well dissected, low mountains, etc.",,3,0.5,547.0,0.5,3
+4,459.0,0.5,"Volcanic fan, foot slope of high block plateaus, etc.",,3,0.5,459.0,0.5,3
+5,323.504047697085,0.5,"Dissected plateaus, etc.",,3,0.5,323.504047697085,0.5,3
+6,300.639538792274,0.5,"Basalt lava plain, glaciated plateau, etc.",,3,0.5,300.639538792274,0.5,3
+7,535.786417756242,0.5,"Moderately eroded mountains, lava flow, etc.",,3,0.5,535.786417756242,0.5,3
+8,514.970575501527,0.5,"Desert alluvial slope, volcanic fan, etc.",,3,0.5,514.970575501527,0.5,3
+9,284.470950299338,0.5,"Well eroded plain of weak rocks, etc.",,3,0.5,284.470950299338,0.5,3
+10,317.368444092771,0.5,"Valley, till plain, etc.",,3,0.5,317.368444092771,0.5,3
+11,266.870166096091,0.5,"Eroded plain of weak rocks, etc.",,3,0.5,266.870166096091,0.5,3
+12,297.0,0.5,"Desert plain, delta plain, etc.",,3,0.5,297.0,0.5,3
+13,216.62368022053,0.5,"Incised terrace, etc.",Prior mean and standard deviation were estimated by Kevin Foster,3,0.5,212.9780673474088,0.3762919771685635,6
+14,241.80869962047,0.5,"Eroded alluvial fan, till plain, etc.",,3,0.5,241.80869962047,0.5,3
+15,198.64481157494,0.5,"Dune, incised terrace, etc.",,3,0.5,186.43548089692834,0.26584695189502144,24
+16,202.125866002814,0.5,"Fluvial plain, alluvial fan, low-lying flat plains, etc.",,3,0.5,193.8747179675098,0.29644483332101074,19
diff --git a/tests/benchmarks/small_independent_only/slope.tif b/tests/benchmarks/small_independent_only/slope.tif
new file mode 100644
index 00000000..c4577153
Binary files /dev/null and b/tests/benchmarks/small_independent_only/slope.tif differ
diff --git a/tests/benchmarks/small_independent_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif b/tests/benchmarks/small_independent_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif
new file mode 100644
index 00000000..1769a1d6
Binary files /dev/null and b/tests/benchmarks/small_independent_only/terrain_vs30_spatially_adjusted_with_uncertainty.tif differ
diff --git a/tests/benchmarks/small_independent_only/tid.tif b/tests/benchmarks/small_independent_only/tid.tif
new file mode 100644
index 00000000..21ea6bc8
Binary files /dev/null and b/tests/benchmarks/small_independent_only/tid.tif differ
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 00000000..12b30019
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,107 @@
+"""
+Pytest configuration and shared test utilities.
+
+This module contains fixtures and helper functions used across multiple test files.
+All shared test fixtures should be defined here to avoid duplication.
+"""
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+import rasterio
+from pandas.testing import assert_frame_equal
+
+TESTS_DIR: Path = Path(__file__).parent
+FIXTURES_DIR: Path = TESTS_DIR / "fixtures"
+BENCHMARKS_DIR: Path = TESTS_DIR / "benchmarks"
+
+TEST_RTOL: float = 1e-5
+TEST_ATOL: float = 1e-8
+
+
+def compare_output_files(
+    output_dir: Path, benchmark_dir: Path, filenames: list[str]
+) -> None:
+    """
+    Compare a list of output files against their benchmarks.
+
+    Routes .tif files to compare_rasters; .csv files are compared with pandas
+    assert_frame_equal after loading.
+
+    Parameters
+    ----------
+    output_dir : Path
+        Directory containing actual output files.
+    benchmark_dir : Path
+        Directory containing expected benchmark files.
+    filenames : list[str]
+        Names of files to compare.
+    """
+    for filename in filenames:
+        actual = output_dir / filename
+        expected = benchmark_dir / filename
+        assert actual.exists(), f"Missing output file: {filename}"
+
+        if filename.endswith(".tif"):
+            compare_rasters(actual, expected)
+        elif filename.endswith(".csv"):
+            actual_df = pd.read_csv(actual)
+            expected_df = pd.read_csv(expected)
+            assert_frame_equal(actual_df, expected_df)
+
+
+def compare_rasters(
+    actual_path: Path,
+    expected_path: Path,
+) -> None:
+    """
+    Compare two raster files for equality within tolerance.
+
+    Parameters
+    ----------
+    actual_path : Path
+        Path to the actual output raster.
+    expected_path : Path
+        Path to the expected benchmark raster.
+
+    Raises
+    ------
+    AssertionError
+        If the rasters differ beyond tolerance.
+    """
+    with rasterio.open(actual_path) as actual, rasterio.open(expected_path) as expected:
+        assert actual.count == expected.count, (
+            f"Band count mismatch: {actual.count} vs {expected.count}"
+        )
+        assert actual.width == expected.width, (
+            f"Width mismatch: {actual.width} vs {expected.width}"
+        )
+        assert actual.height == expected.height, (
+            f"Height mismatch: {actual.height} vs {expected.height}"
+        )
+
+        for band_idx in range(1, actual.count + 1):
+            actual_data = actual.read(band_idx)
+            expected_data = expected.read(band_idx)
+            nodata = actual.nodata
+
+            if nodata is not None:
+                actual_valid = actual_data != nodata
+                expected_valid = expected_data != nodata
+                assert np.array_equal(actual_valid, expected_valid), (
+                    f"Band {band_idx}: Valid data masks differ"
+                )
+                valid_actual = actual_data[actual_valid]
+                valid_expected = expected_data[expected_valid]
+            else:
+                valid_actual = actual_data
+                valid_expected = expected_data
+
+            if np.any(valid_actual):
+                assert valid_actual == pytest.approx(
+                    valid_expected,
+                    rel=TEST_RTOL,
+                    abs=TEST_ATOL,
+                ), f"Band {band_idx}: Data values differ beyond tolerance"
diff --git a/tests/expected_location_results/coast.tif b/tests/expected_location_results/coast.tif
deleted file mode 100644
index ee6ee43a..00000000
Binary files a/tests/expected_location_results/coast.tif and /dev/null differ
diff --git a/tests/expected_location_results/measured_sites.csv b/tests/expected_location_results/measured_sites.csv
deleted file mode 100644
index 8cf8114e..00000000
--- a/tests/expected_location_results/measured_sites.csv
+++ /dev/null
@@ -1,460 +0,0 @@
-easting,northing,vs30,uncertainty,station,q,gid,geology_vs30,geology_stdv,tid,terrain_vs30,terrain_stdv
-1576480.1104056418,5207643.269430228,224.85387,0.20000000298023224,NA,NA,6,211.89963,0.21705775,16,202.12587,0.20682013
-1577001.152476283,5207261.898184137,205.06209,0.20000000298023224,NA,NA,6,217.67502,0.21705775,16,202.12587,0.20682013
-1573315.2514270404,5206270.351476801,164.72166,0.20000000298023224,NA,NA,10,197.0,0.20262977,16,202.12587,0.20682013
-1573808.0613481023,5206002.938663059,151.65314,0.20000000298023224,NA,NA,10,197.0,0.20262977,15,198.6448,0.20525637
-1576829.9518384219,5206767.586019354,220.53214,0.20000000298023224,NA,NA,6,217.4745,0.21705775,16,202.12587,0.20682013
-1568063.6539701042,5205264.818172148,225.27951,0.20000000298023224,NA,NA,4,256.94333,0.2431944,16,202.12587,0.20682013
-1570917.6063284436,5204945.384204217,185.5686,0.20000000298023224,NA,NA,10,197.0,0.20262977,16,202.12587,0.20682013
-1574075.7063837913,5205783.013726249,160.4534,0.20000000298023224,NA,NA,10,197.0,0.20262977,16,202.12587,0.20682013
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diff --git a/tests/expected_location_results/qgis.qgz b/tests/expected_location_results/qgis.qgz
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index f62517f4..00000000
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diff --git a/tests/expected_location_results/slope.tif b/tests/expected_location_results/slope.tif
deleted file mode 100644
index 6e0c6d0a..00000000
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diff --git a/tests/expected_location_results/vs30points.csv b/tests/expected_location_results/vs30points.csv
deleted file mode 100644
index ec0ed5f9..00000000
--- a/tests/expected_location_results/vs30points.csv
+++ /dev/null
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-167.53821,-47.281174,1186997.438082311,4749105.704251487,15,690.97437,0.446037,690.97437,0.44602114,5,323.50406,0.40743098,323.50406,0.40741047,472.79272,0.571361,472.79272,0.57134753
-167.5388,-47.281174,1187042.0409935764,4749108.83740057,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.5394,-47.281174,1187087.3998901104,4749112.023304892,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54,-47.281174,1187132.7587903987,4749115.208857296,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54059,-47.281174,1187177.3617126795,4749118.340973997,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54118,-47.281174,1187221.964638588,4749121.472750414,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54178,-47.281174,1187267.323550015,4749124.657258808,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54237,-47.281174,1187311.9264832456,4749127.7883488955,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54297,-47.281174,1187357.2854021133,4749130.972159328,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54356,-47.281174,1187401.8883426618,4749134.10256309,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54416,-47.281174,1187447.247268971,4749137.285675564,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54477,-47.281174,1187493.3621812318,4749140.521479155,15,690.97437,0.446037,690.97437,0.44602114,3,547.0,0.5,547.0,0.49997482,614.78687,0.48797852,614.78687,0.4879584
-167.54536,-47.281174,1187537.9651328444,4749143.650844781,15,690.97437,0.446037,690.97437,0.44602114,255,NA,NA,NA,NA,NA,NA,NA,NA
-167.54596,-47.281174,1187583.3240704096,4749146.832901524,15,690.97437,0.446037,690.97437,0.44602114,255,NA,NA,NA,NA,NA,NA,NA,NA
-167.54655,-47.281174,1187627.9270293359,4749149.961580829,15,690.97437,0.446037,690.97437,0.44602114,255,NA,NA,NA,NA,NA,NA,NA,NA
-167.54715,-47.281174,1187673.2859743335,4749153.142939621,15,690.97437,0.446037,690.97437,0.44602114,255,NA,NA,NA,NA,NA,NA,NA,NA
-167.46245,-47.280724,1181266.591413566,4748750.549772519,15,690.97437,0.446037,690.97437,0.44602114,255,NA,NA,NA,NA,NA,NA,NA,NA
-167.46304,-47.280724,1181311.1942364,4748753.72662534,15,690.97437,0.446037,690.97437,0.44602114,255,NA,NA,NA,NA,NA,NA,NA,NA
-167.46364,-47.280724,1181356.553043062,4748756.956974063,15,690.97437,0.446037,690.97437,0.44602114,255,NA,NA,NA,NA,NA,NA,NA,NA
diff --git a/tests/fixtures/nz_cities.csv b/tests/fixtures/nz_cities.csv
new file mode 100644
index 00000000..5c76b9f8
--- /dev/null
+++ b/tests/fixtures/nz_cities.csv
@@ -0,0 +1,16 @@
+city,latitude,longitude
+Auckland,-36.8485,174.7633
+Wellington,-41.2865,174.7762
+Christchurch,-43.5321,172.6362
+Hamilton,-37.7870,175.2793
+Tauranga,-37.6878,176.1651
+Napier,-39.4928,176.9120
+Dunedin,-45.8788,170.5028
+Palmerston North,-40.3523,175.6082
+Nelson,-41.2706,173.2840
+Rotorua,-38.1368,176.2497
+New Plymouth,-39.0556,174.0752
+Whangarei,-35.7275,174.3166
+Invercargill,-46.4132,168.3538
+Hastings,-39.6381,176.8493
+Queenstown,-45.0312,168.6626
diff --git a/tests/fixtures/test_config_both.yaml b/tests/fixtures/test_config_both.yaml
new file mode 100644
index 00000000..72950a0b
--- /dev/null
+++ b/tests/fixtures/test_config_both.yaml
@@ -0,0 +1,28 @@
+# Test configuration - Both independent and clustered observations
+# 10km x 10km domain centered on University of Canterbury, Christchurch
+
+n_proc: 1
+
+# --- Grid Parameters ---
+grid_xmin: 1565300
+grid_xmax: 1575300
+grid_ymin: 5175500
+grid_ymax: 5185500
+grid_dx: 100
+grid_dy: 100
+
+# --- General Configuration ---
+do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: true
+noisy: true
+max_spatial_boolean_array_memory_gb: 1
+obs_subsample_step_for_clustered: 100
+
+# --- Observation files ---
+independent_observations_file: observations/test_independent_observations.csv
+clustered_observations_file: observations/test_cpt_observations.csv
+
+# Output directory - set at runtime
+output_dir: /tmp/vs30_test_both
+
+# Combination method
+combination_method: 1.0
diff --git a/tests/fixtures/test_config_clustered_only.yaml b/tests/fixtures/test_config_clustered_only.yaml
new file mode 100644
index 00000000..db7c3df7
--- /dev/null
+++ b/tests/fixtures/test_config_clustered_only.yaml
@@ -0,0 +1,28 @@
+# Test configuration - Clustered (CPT) observations only
+# 10km x 10km domain centered on University of Canterbury, Christchurch
+
+n_proc: 1
+
+# --- Grid Parameters ---
+grid_xmin: 1565300
+grid_xmax: 1575300
+grid_ymin: 5175500
+grid_ymax: 5185500
+grid_dx: 100
+grid_dy: 100
+
+# --- General Configuration ---
+do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: true
+noisy: true
+max_spatial_boolean_array_memory_gb: 1
+obs_subsample_step_for_clustered: 100
+
+# --- Observation files ---
+independent_observations_file: none
+clustered_observations_file: observations/test_cpt_observations.csv
+
+# Output directory - set at runtime
+output_dir: /tmp/vs30_test_clustered_only
+
+# Combination method
+combination_method: 1.0
diff --git a/tests/fixtures/test_config_independent_only.yaml b/tests/fixtures/test_config_independent_only.yaml
new file mode 100644
index 00000000..401cc9e7
--- /dev/null
+++ b/tests/fixtures/test_config_independent_only.yaml
@@ -0,0 +1,28 @@
+# Test configuration - Independent observations only
+# 10km x 10km domain centered on University of Canterbury, Christchurch
+
+n_proc: 1
+
+# --- Grid Parameters ---
+grid_xmin: 1565300
+grid_xmax: 1575300
+grid_ymin: 5175500
+grid_ymax: 5185500
+grid_dx: 100
+grid_dy: 100
+
+# --- General Configuration ---
+do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: true
+noisy: true
+max_spatial_boolean_array_memory_gb: 1
+obs_subsample_step_for_clustered: 100
+
+# --- Observation files ---
+independent_observations_file: observations/test_independent_observations.csv
+clustered_observations_file: none
+
+# Output directory - set at runtime
+output_dir: /tmp/vs30_test_independent_only
+
+# Combination method
+combination_method: 1.0
diff --git a/tests/fixtures/test_config_small_both.yaml b/tests/fixtures/test_config_small_both.yaml
new file mode 100644
index 00000000..d26a46d9
--- /dev/null
+++ b/tests/fixtures/test_config_small_both.yaml
@@ -0,0 +1,30 @@
+# Test configuration - Both observation types
+# 10km x 10km domain at 500m resolution for fast testing
+# Uses full test CPT dataset so clustered observations contribute properly
+
+n_proc: 1
+
+# --- Grid Parameters ---
+# 10km x 10km domain at 500m resolution = 20x20 = 400 pixels
+grid_xmin: 1565300
+grid_xmax: 1575300
+grid_ymin: 5175500
+grid_ymax: 5185500
+grid_dx: 500
+grid_dy: 500
+
+# --- General Configuration ---
+do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: true
+noisy: true
+max_spatial_boolean_array_memory_gb: 1
+obs_subsample_step_for_clustered: 10
+
+# --- Observation files ---
+independent_observations_file: observations/test_independent_observations.csv
+clustered_observations_file: observations/test_cpt_observations.csv
+
+# Output directory - set at runtime
+output_dir: /tmp/vs30_test_small_both
+
+# Combination method
+combination_method: 1.0
diff --git a/tests/fixtures/test_config_small_clustered_only.yaml b/tests/fixtures/test_config_small_clustered_only.yaml
new file mode 100644
index 00000000..224f9b21
--- /dev/null
+++ b/tests/fixtures/test_config_small_clustered_only.yaml
@@ -0,0 +1,30 @@
+# Test configuration - Clustered observations only
+# 10km x 10km domain at 500m resolution for fast testing with clustered data
+# Uses full test CPT dataset to ensure good stddev per category
+
+n_proc: 1
+
+# --- Grid Parameters ---
+# 10km x 10km domain at 500m resolution = 20x20 = 400 pixels
+grid_xmin: 1565300
+grid_xmax: 1575300
+grid_ymin: 5175500
+grid_ymax: 5185500
+grid_dx: 500
+grid_dy: 500
+
+# --- General Configuration ---
+do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: true
+noisy: true
+max_spatial_boolean_array_memory_gb: 1
+obs_subsample_step_for_clustered: 10
+
+# --- Observation files ---
+independent_observations_file: none
+clustered_observations_file: observations/test_cpt_observations.csv
+
+# Output directory - set at runtime
+output_dir: /tmp/vs30_test_small_clustered
+
+# Combination method
+combination_method: 1.0
diff --git a/tests/fixtures/test_config_small_independent_only.yaml b/tests/fixtures/test_config_small_independent_only.yaml
new file mode 100644
index 00000000..5f2d8610
--- /dev/null
+++ b/tests/fixtures/test_config_small_independent_only.yaml
@@ -0,0 +1,28 @@
+# Test configuration - Small domain, Independent observations only
+# 3km x 3km domain for fast testing
+
+n_proc: 1
+
+# --- Grid Parameters ---
+grid_xmin: 1569500
+grid_xmax: 1572500
+grid_ymin: 5178500
+grid_ymax: 5181500
+grid_dx: 100
+grid_dy: 100
+
+# --- General Configuration ---
+do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: true
+noisy: true
+max_spatial_boolean_array_memory_gb: 1
+obs_subsample_step_for_clustered: 10
+
+# --- Observation files ---
+independent_observations_file: observations/minimal_independent_observations.csv
+clustered_observations_file: none
+
+# Output directory - set at runtime
+output_dir: /tmp/vs30_test_small_independent
+
+# Combination method
+combination_method: 1.0
diff --git a/tests/fixtures/test_cpt_observations.csv b/tests/fixtures/test_cpt_observations.csv
new file mode 100644
index 00000000..d49e8dbc
--- /dev/null
+++ b/tests/fixtures/test_cpt_observations.csv
@@ -0,0 +1,19237 @@
+index,easting,northing,vs30,uncertainty,source
+0,1576467.2,5181262.5,184.62537,0.5,cpt
+1,1570681.6,5180753.0,166.26828,0.5,cpt
+3,1573105.0,5179270.0,159.10588,0.5,cpt
+4,1573150.0,5179295.0,187.60077,0.5,cpt
+5,1573160.0,5179410.0,187.29077,0.5,cpt
+6,1574119.2,5177888.0,186.3933,0.5,cpt
+7,1570469.2,5187186.0,157.44801,0.5,cpt
+8,1576583.0,5182912.0,200.3916,0.5,cpt
+9,1566029.6,5175892.5,178.41258,0.5,cpt
+13,1571192.0,5186865.0,177.85574,0.5,cpt
+14,1570698.0,5186853.0,210.96709,0.5,cpt
+17,1574010.4,5178985.5,186.7924,0.5,cpt
+18,1569700.0,5188217.0,227.97533,0.5,cpt
+19,1573719.5,5182091.5,190.13925,0.5,cpt
+20,1572621.8,5181199.5,161.07153,0.5,cpt
+21,1576077.4,5175392.5,174.45561,0.5,cpt
+22,1569251.2,5179617.0,154.85176,0.5,cpt
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+28,1576820.1,5183809.0,206.72958,0.5,cpt
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+40,1572871.5,5179159.5,184.29768,0.5,cpt
+41,1570776.0,5186432.0,217.92163,0.5,cpt
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+45,1578780.6,5177267.0,178.70105,0.5,cpt
+46,1571494.6,5180784.0,212.10167,0.5,cpt
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+49,1573477.9,5189033.5,157.9029,0.5,cpt
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+54,1571451.1,5178818.0,153.62111,0.5,cpt
+56,1575107.2,5177474.5,173.1595,0.5,cpt
+57,1570938.4,5179724.5,233.38391,0.5,cpt
+59,1570696.5,5181803.5,183.68665,0.5,cpt
+61,1568530.0,5182682.0,149.32098,0.5,cpt
+62,1570152.8,5178585.0,138.89926,0.5,cpt
+64,1569049.0,5182910.0,180.10312,0.5,cpt
+68,1572132.0,5178891.0,147.00543,0.5,cpt
+69,1570874.2,5178692.5,160.50485,0.5,cpt
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diff --git a/tests/fixtures/test_independent_observations.csv b/tests/fixtures/test_independent_observations.csv
new file mode 100644
index 00000000..6420366b
--- /dev/null
+++ b/tests/fixtures/test_independent_observations.csv
@@ -0,0 +1,253 @@
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diff --git a/tests/resources/test_sites.csv b/tests/resources/test_sites.csv
deleted file mode 100644
index 71bbf72f..00000000
--- a/tests/resources/test_sites.csv
+++ /dev/null
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diff --git a/tests/test_category.py b/tests/test_category.py
new file mode 100644
index 00000000..39c81338
--- /dev/null
+++ b/tests/test_category.py
@@ -0,0 +1,236 @@
+"""
+Tests for the VS30 category module.
+
+Tests cover:
+- Bayesian update formulas
+- Independent data update
+- Category edge cases
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from vs30 import category, constants
+
+
+class TestBayesianUpdateFormulas:
+    """Tests for the Bayesian update helper functions."""
+
+    def test_posterior_variance_basic(self):
+        """Test compute_bayesian_posterior_variance with simple inputs."""
+        prior_stdv = 0.5  # Prior std dev
+        num_prior_observations = 3  # Prior sample size
+        observation_uncertainty = 0.2  # Observation uncertainty
+        prior_mean = 200  # Prior mean
+        observation_value = 210  # Observation
+
+        var = category.compute_bayesian_posterior_variance(
+            prior_stdv,
+            num_prior_observations,
+            observation_uncertainty,
+            prior_mean,
+            observation_value,
+        )
+
+        # Variance should be positive
+        assert var > 0
+        # New variance should be less than prior variance when adding data
+        assert var < prior_stdv**2
+
+    def test_posterior_mean_basic(self):
+        """Test compute_bayesian_posterior_mean with simple inputs."""
+        prior_mean = 200  # Prior mean
+        num_prior_observations = 3  # Prior sample size
+        observation_value = 220  # Observation
+
+        mean = category.compute_bayesian_posterior_mean(
+            prior_mean, num_prior_observations, observation_value
+        )
+
+        # New mean should be between prior and observation
+        assert (
+            min(prior_mean, observation_value)
+            <= mean
+            <= max(prior_mean, observation_value)
+        )
+
+    def test_posterior_mean_pulls_toward_observation(self):
+        """Test that posterior mean is pulled toward observation."""
+        prior_mean = 200
+        num_prior_observations = 3
+        observation_value = 300  # Observation much higher than prior
+
+        mean = category.compute_bayesian_posterior_mean(
+            prior_mean, num_prior_observations, observation_value
+        )
+
+        # Mean should be closer to observation than prior was
+        assert mean > prior_mean
+
+    def test_posterior_variance_increases_with_mean_shift(self):
+        """Test that variance increases when observation far from prior."""
+        prior_stdv = 0.5
+        num_prior_observations = 3
+        observation_uncertainty = 0.2
+        prior_mean = 200
+
+        # Observation close to prior
+        obs_close = 205
+        var_close = category.compute_bayesian_posterior_variance(
+            prior_stdv,
+            num_prior_observations,
+            observation_uncertainty,
+            prior_mean,
+            obs_close,
+        )
+
+        # Observation far from prior
+        obs_far = 400
+        var_far = category.compute_bayesian_posterior_variance(
+            prior_stdv,
+            num_prior_observations,
+            observation_uncertainty,
+            prior_mean,
+            obs_far,
+        )
+
+        # Variance should be higher when observation is far from prior
+        assert var_far > var_close
+
+    def test_bayesian_update_convergence(self):
+        """Test that multiple observations converge toward true value."""
+        prior_mean = 200  # Prior mean
+        prior_stdv = 0.5  # Prior std dev
+        num_prior_observations = 3
+        true_value = 250  # True value observations are drawn from
+
+        # Simulate multiple observations around true value
+        current_mean = prior_mean
+        current_std = prior_stdv
+        current_num_observations = num_prior_observations
+
+        for _ in range(10):
+            # Observation with some noise
+            observation_value = true_value * (1 + np.random.normal(0, 0.05))
+            observation_uncertainty = 0.2
+
+            var = category.compute_bayesian_posterior_variance(
+                current_std,
+                current_num_observations,
+                observation_uncertainty,
+                current_mean,
+                observation_value,
+            )
+            current_mean = category.compute_bayesian_posterior_mean(
+                current_mean, current_num_observations, observation_value
+            )
+            current_std = np.sqrt(var)
+            current_num_observations += 1
+
+        # After many observations, mean should be close to true value
+        assert abs(current_mean - true_value) < abs(prior_mean - true_value)
+
+
+class TestUpdateWithIndependentData:
+    """Tests for the update_with_independent_data function."""
+
+    @pytest.fixture
+    def sample_categorical_model(self):
+        """Create sample categorical model DataFrame."""
+        return pd.DataFrame(
+            {
+                "id": [1, 2, 3],
+                "mean_vs30_km_per_s": [200.0, 300.0, 400.0],
+                "standard_deviation_vs30_km_per_s": [0.5, 0.4, 0.3],
+            }
+        )
+
+    @pytest.fixture
+    def sample_observations(self):
+        """Create sample observations DataFrame."""
+        return pd.DataFrame(
+            {
+                "id": [1, 1, 2],
+                "vs30": [210.0, 195.0, 320.0],
+                "uncertainty": [0.2, 0.2, 0.15],
+            }
+        )
+
+    def test_basic_update(self, sample_categorical_model, sample_observations):
+        """Test basic Bayesian update with observations."""
+        result = category.update_with_independent_data(
+            sample_categorical_model,
+            sample_observations,
+        )
+
+        # Check output has expected columns
+        assert "posterior_mean_vs30_km_per_s_independent_observations" in result.columns
+        assert (
+            "posterior_standard_deviation_vs30_km_per_s_independent_observations"
+            in result.columns
+        )
+        assert "posterior_num_observations_independent_observations" in result.columns
+
+        # Category 1 should be updated (has 2 observations)
+        # N_PRIOR=3 from constants, so 3 + 2 = 5
+        cat1 = result[result["id"] == 1].iloc[0]
+        assert (
+            cat1["posterior_num_observations_independent_observations"] == 5
+        )  # 3 prior + 2 obs
+
+        # Category 3 should remain unchanged (no observations)
+        cat3 = result[result["id"] == 3].iloc[0]
+        assert cat3["posterior_mean_vs30_km_per_s_independent_observations"] == 400.0
+
+    def test_min_sigma_enforced(self, sample_categorical_model, sample_observations):
+        """Test that minimum sigma is enforced using MIN_SIGMA constant."""
+        result = category.update_with_independent_data(
+            sample_categorical_model,
+            sample_observations,
+        )
+
+        # All posteriors should have stddev >= MIN_SIGMA
+        # Note: posteriors can go below MIN_SIGMA, but prior is floored at MIN_SIGMA
+        assert result["enforced_min_sigma"].iloc[0] == constants.MIN_SIGMA
+
+
+class TestCategoryEdgeCases:
+    """Tests for edge cases in category module."""
+
+    def test_update_with_no_matching_observations(self):
+        """Test Bayesian update when no observations match a category."""
+        # Create categorical model with some categories
+        categorical_model_df = pd.DataFrame(
+            {
+                "id": [1, 2, 3],
+                "mean_vs30_km_per_s": [300.0, 400.0, 500.0],
+                "standard_deviation_vs30_km_per_s": [30.0, 40.0, 50.0],
+            }
+        )
+
+        # Observations that don't match any category (use STANDARD_ID_COLUMN = 'id')
+        observations_df = pd.DataFrame(
+            {
+                "vs30": [350.0],
+                "uncertainty": [25.0],
+                "id": [99],  # Non-existent category - uses 'id' column
+                "easting": [1500000.0],
+                "northing": [5100000.0],
+            }
+        )
+
+        # Should not raise, categories without observations keep prior
+        result_df = category.update_with_independent_data(
+            categorical_model_df,
+            observations_df,
+        )
+
+        # Result should have posterior columns
+        assert (
+            "posterior_mean_vs30_km_per_s_independent_observations" in result_df.columns
+        )
+        assert (
+            "posterior_standard_deviation_vs30_km_per_s_independent_observations"
+            in result_df.columns
+        )
diff --git a/tests/test_compute_at_locations.py b/tests/test_compute_at_locations.py
new file mode 100644
index 00000000..336ddbf4
--- /dev/null
+++ b/tests/test_compute_at_locations.py
@@ -0,0 +1,50 @@
+"""
+Tests for the VS30 compute-at-locations functionality.
+
+These tests verify that compute_at_locations produces consistent Vs30 values
+for known locations (major NZ cities).
+"""
+
+import pandas as pd
+from pandas.testing import assert_frame_equal
+
+from conftest import BENCHMARKS_DIR
+from conftest import FIXTURES_DIR
+from vs30 import pipeline
+from vs30.config import Vs30Config
+
+EXPECTED_CSV = BENCHMARKS_DIR / "nz_cities_vs30.csv"
+
+
+def test_single_process(tmp_path):
+    """Test compute_at_locations with n_proc=1."""
+    output_csv = tmp_path / "output.csv"
+    pipeline.compute_at_locations(
+        locations_csv=FIXTURES_DIR / "nz_cities.csv",
+        output_csv=output_csv,
+        cfg=Vs30Config.default(),
+        lon_column="longitude",
+        lat_column="latitude",
+        include_intermediate=True,
+        n_proc=1,
+    )
+    actual_df = pd.read_csv(output_csv)
+    expected_df = pd.read_csv(EXPECTED_CSV)
+    assert_frame_equal(actual_df, expected_df)
+
+
+def test_multiprocess(tmp_path):
+    """Test that multiprocess results match single-process benchmark."""
+    output_csv = tmp_path / "output.csv"
+    pipeline.compute_at_locations(
+        locations_csv=FIXTURES_DIR / "nz_cities.csv",
+        output_csv=output_csv,
+        cfg=Vs30Config.default(),
+        lon_column="longitude",
+        lat_column="latitude",
+        include_intermediate=True,
+        n_proc=-1,
+    )
+    actual_df = pd.read_csv(output_csv)
+    expected_df = pd.read_csv(EXPECTED_CSV)
+    assert_frame_equal(actual_df, expected_df)
diff --git a/tests/test_full_pipeline.py b/tests/test_full_pipeline.py
new file mode 100644
index 00000000..5af8bf94
--- /dev/null
+++ b/tests/test_full_pipeline.py
@@ -0,0 +1,92 @@
+"""
+End-to-end tests for the VS30 full-pipeline command.
+
+These tests verify that the full pipeline produces consistent results
+for different observation type combinations and processing modes.
+
+Test scenarios (using small domain for fast execution):
+- Independent observations only
+- Clustered (CPT) observations only
+- Both observation types combined
+
+Each scenario is tested with both single-process (n_proc=1) and
+multi-process (n_proc=cpu_count) modes to ensure parallel processing works correctly.
+"""
+
+import os
+from pathlib import Path
+
+import pytest
+import yaml
+
+from conftest import BENCHMARKS_DIR
+from conftest import compare_output_files
+from conftest import FIXTURES_DIR
+
+from vs30 import pipeline
+from vs30.config import Vs30Config
+
+SCENARIOS = [
+    "small_independent_only",
+    "small_both",
+    "small_clustered_only",
+]
+
+KEY_OUTPUT_FILES = [
+    "combined_vs30.tif",
+    "geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif",
+    "terrain_vs30_spatially_adjusted_with_uncertainty.tif",
+    "posterior_geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv",
+    "posterior_terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv",
+]
+
+
+def create_test_config(scenario: str, output_dir: Path, n_proc: int = 1) -> Path:
+    """
+    Create a test configuration file with the specified settings.
+
+    Parameters
+    ----------
+    scenario : str
+        One of "small_independent_only", "small_clustered_only", or "small_both".
+    output_dir : Path
+        Directory for pipeline output.
+    n_proc : int
+        Number of processes to use.
+
+    Returns
+    -------
+    Path
+        Path to the created config file.
+    """
+    config_file = FIXTURES_DIR / f"test_config_{scenario}.yaml"
+    with open(config_file) as f:
+        config = yaml.safe_load(f)
+
+    config["n_proc"] = n_proc
+    config["output_dir"] = str(output_dir)
+
+    test_config_path = output_dir / "test_config.yaml"
+    with open(test_config_path, "w") as f:
+        yaml.dump(config, f)
+
+    return test_config_path
+
+
+@pytest.mark.parametrize("scenario", SCENARIOS)
+def test_single_process(tmp_path, scenario):
+    """Test pipeline with n_proc=1 for each scenario."""
+    config_path = create_test_config(scenario, tmp_path, n_proc=1)
+    cfg = Vs30Config.from_yaml(config_path)
+    pipeline.run_full_pipeline(cfg)
+    compare_output_files(tmp_path, BENCHMARKS_DIR / scenario, KEY_OUTPUT_FILES)
+
+
+@pytest.mark.parametrize("scenario", SCENARIOS)
+def test_multiprocess(tmp_path, scenario):
+    """Test pipeline with all available CPUs for each scenario."""
+    # `or 1` guards against None return from os.cpu_count() to satisfy the type checker.
+    config_path = create_test_config(scenario, tmp_path, n_proc=os.cpu_count() or 1)
+    cfg = Vs30Config.from_yaml(config_path)
+    pipeline.run_full_pipeline(cfg)
+    compare_output_files(tmp_path, BENCHMARKS_DIR / scenario, KEY_OUTPUT_FILES)
diff --git a/tests/test_raster.py b/tests/test_raster.py
new file mode 100644
index 00000000..b405f5c7
--- /dev/null
+++ b/tests/test_raster.py
@@ -0,0 +1,327 @@
+"""
+Tests for the VS30 raster module.
+
+Tests cover:
+- apply_hybrid_geology_modifications function
+- apply_hybrid_modifications_at_points function
+"""
+
+import numpy as np
+import pytest
+import rasterio
+from rasterio.transform import from_bounds
+
+from vs30 import raster
+
+# Shared extent for test rasters used by TestApplyHybridModificationsAtPoints
+TEST_XMIN, TEST_XMAX = 1740000, 1760000
+TEST_YMIN, TEST_YMAX = 5420000, 5440000
+TEST_RASTER_SIZE = 20
+
+
+def write_single_band_raster(path, data):
+    """Write a single-band GeoTIFF with the shared test extent."""
+    raster_transform = from_bounds(
+        TEST_XMIN,
+        TEST_YMIN,
+        TEST_XMAX,
+        TEST_YMAX,
+        TEST_RASTER_SIZE,
+        TEST_RASTER_SIZE,
+    )
+    with rasterio.open(
+        path,
+        "w",
+        driver="GTiff",
+        width=TEST_RASTER_SIZE,
+        height=TEST_RASTER_SIZE,
+        count=1,
+        dtype="float32",
+        crs="EPSG:2193",
+        transform=raster_transform,
+        nodata=-9999.0,
+    ) as dst:
+        dst.write(data, 1)
+
+
+class TestApplyHybridGeologyModifications:
+    """Tests for the apply_hybrid_geology_modifications function."""
+
+    @pytest.fixture
+    def sample_arrays(self):
+        """Create sample arrays for testing."""
+        id_array = np.array(
+            [
+                [1, 2, 3],
+                [4, 5, 6],
+                [7, 10, 11],
+            ],
+            dtype=np.uint8,
+        )
+
+        vs30_array = np.full((3, 3), 300.0, dtype=np.float32)
+        stdv_array = np.full((3, 3), 0.5, dtype=np.float32)
+
+        slope_array = np.array(
+            [
+                [0.01, 0.05, 0.1],
+                [0.2, 0.5, 1.0],
+                [2.0, 5.0, 10.0],
+            ],
+            dtype=np.float32,
+        )
+
+        coast_dist_array = np.array(
+            [
+                [5000.0, 8000.0, 12000.0],
+                [15000.0, 20000.0, 25000.0],
+                [30000.0, 10000.0, 5000.0],
+            ],
+            dtype=np.float32,
+        )
+
+        return id_array, vs30_array, stdv_array, slope_array, coast_dist_array
+
+    def test_mod6_applies_to_gid4(self, sample_arrays):
+        """Test that mod6 modification applies to geology ID 4."""
+        id_array, vs30_array, stdv_array, slope_array, coast_dist_array = sample_arrays
+
+        # Set specific ID for testing
+        id_array[1, 0] = 4  # Alluvium
+
+        result_vs30, result_stdv = raster.apply_hybrid_geology_modifications(
+            vs30_array.copy(),
+            stdv_array.copy(),
+            id_array,
+            slope_array,
+            coast_dist_array,
+            mod6=True,
+            mod13=False,
+            hybrid=False,
+            hybrid_mod6_dist_min=8000.0,
+            hybrid_mod6_dist_max=20000.0,
+            hybrid_mod6_vs30_min=240.0,
+            hybrid_mod6_vs30_max=500.0,
+        )
+
+        # GID 4 pixel at (1,0) has coast_dist=15000
+        # vs30 = 240 + (500-240) * (15000-8000) / (20000-8000)
+        # vs30 = 240 + 260 * 7000/12000 = 240 + 151.67 = 391.67
+        expected_vs30 = 240 + (500 - 240) * (15000 - 8000) / (20000 - 8000)
+        assert np.isclose(result_vs30[1, 0], expected_vs30, rtol=0.01)
+
+    def test_mod13_applies_to_gid10(self, sample_arrays):
+        """Test that mod13 modification applies to geology ID 10."""
+        id_array, vs30_array, stdv_array, slope_array, coast_dist_array = sample_arrays
+
+        # GID 10 is at position (2, 1) with coast_dist=10000
+        result_vs30, result_stdv = raster.apply_hybrid_geology_modifications(
+            vs30_array.copy(),
+            stdv_array.copy(),
+            id_array,
+            slope_array,
+            coast_dist_array,
+            mod6=False,
+            mod13=True,
+            hybrid=False,
+            hybrid_mod13_dist_min=8000.0,
+            hybrid_mod13_dist_max=20000.0,
+            hybrid_mod13_vs30_min=197.0,
+            hybrid_mod13_vs30_max=500.0,
+        )
+
+        # GID 10 pixel at (2,1) has coast_dist=10000
+        # vs30 = 197 + (500-197) * (10000-8000) / (20000-8000)
+        # vs30 = 197 + 303 * 2000/12000 = 197 + 50.5 = 247.5
+        expected_vs30 = 197 + (500 - 197) * (10000 - 8000) / (20000 - 8000)
+        assert np.isclose(result_vs30[2, 1], expected_vs30, rtol=0.01)
+
+    def test_mod6_clamps_at_minimum(self, sample_arrays):
+        """Test that mod6 clamps vs30 at minimum value."""
+        id_array, vs30_array, stdv_array, slope_array, coast_dist_array = sample_arrays
+
+        id_array[0, 0] = 4  # Alluvium
+        coast_dist_array[0, 0] = 1000.0  # Very close to coast
+
+        result_vs30, _ = raster.apply_hybrid_geology_modifications(
+            vs30_array.copy(),
+            stdv_array.copy(),
+            id_array,
+            slope_array,
+            coast_dist_array,
+            mod6=True,
+            mod13=False,
+            hybrid=False,
+            hybrid_mod6_dist_min=8000.0,
+            hybrid_mod6_dist_max=20000.0,
+            hybrid_mod6_vs30_min=240.0,
+            hybrid_mod6_vs30_max=500.0,
+        )
+
+        # Should clamp at minimum (240)
+        assert result_vs30[0, 0] == 240.0
+
+    def test_mod6_clamps_at_maximum(self, sample_arrays):
+        """Test that mod6 clamps vs30 at maximum value."""
+        id_array, vs30_array, stdv_array, slope_array, coast_dist_array = sample_arrays
+
+        id_array[0, 0] = 4  # Alluvium
+        coast_dist_array[0, 0] = 100000.0  # Very far inland
+
+        result_vs30, _ = raster.apply_hybrid_geology_modifications(
+            vs30_array.copy(),
+            stdv_array.copy(),
+            id_array,
+            slope_array,
+            coast_dist_array,
+            mod6=True,
+            mod13=False,
+            hybrid=False,
+            hybrid_mod6_dist_min=8000.0,
+            hybrid_mod6_dist_max=20000.0,
+            hybrid_mod6_vs30_min=240.0,
+            hybrid_mod6_vs30_max=500.0,
+        )
+
+        # Should clamp at maximum (500)
+        assert result_vs30[0, 0] == 500.0
+
+    def test_no_modifications_returns_unchanged(self, sample_arrays):
+        """Test that disabling all modifications returns unchanged arrays."""
+        id_array, vs30_array, stdv_array, slope_array, coast_dist_array = sample_arrays
+
+        original_vs30 = vs30_array.copy()
+        original_stdv = stdv_array.copy()
+
+        result_vs30, result_stdv = raster.apply_hybrid_geology_modifications(
+            vs30_array.copy(),
+            stdv_array.copy(),
+            id_array,
+            slope_array,
+            coast_dist_array,
+            mod6=False,
+            mod13=False,
+            hybrid=False,
+        )
+
+        np.testing.assert_array_equal(result_vs30, original_vs30)
+        np.testing.assert_array_equal(result_stdv, original_stdv)
+
+
+class TestApplyHybridModificationsAtPoints:
+    """Tests for apply_hybrid_modifications_at_points function."""
+
+    @pytest.fixture
+    def slope_raster(self, tmp_path):
+        """Create a test slope raster with values from 0.01 to 10."""
+        slope_path = tmp_path / "slope.tif"
+        n = TEST_RASTER_SIZE
+        slope_data = np.linspace(0.01, 10, n * n).reshape(n, n).astype(np.float32)
+        write_single_band_raster(slope_path, slope_data)
+        return slope_path
+
+    @pytest.fixture
+    def coast_distance_raster(self, tmp_path):
+        """Create a test coastal distance raster with values from 1000 to 30000 meters."""
+        coast_path = tmp_path / "coast_dist.tif"
+        n = TEST_RASTER_SIZE
+        coast_data = np.linspace(1000, 30000, n * n).reshape(n, n).astype(np.float32)
+        write_single_band_raster(coast_path, coast_data)
+        return coast_path
+
+    def test_hybrid_slope_modifications_at_points(
+        self, slope_raster, coast_distance_raster
+    ):
+        """Test slope-based modifications at points."""
+        # Points within the test raster extent
+        points = np.array(
+            [
+                [1745000.0, 5425000.0],
+                [1750000.0, 5430000.0],
+                [1755000.0, 5435000.0],
+            ]
+        )
+
+        vs30 = np.array([300.0, 300.0, 300.0])
+        stdv = np.array([0.5, 0.5, 0.5])
+        # GID 2 has slope-based modifications
+        geology_ids = np.array([2, 2, 2])
+
+        modified_vs30, modified_stdv = raster.apply_hybrid_modifications_at_points(
+            points,
+            vs30.copy(),
+            stdv.copy(),
+            geology_ids,
+            slope_raster_path=slope_raster,
+            coast_distance_raster_path=coast_distance_raster,
+            mod6=False,
+            mod13=False,
+            hybrid=True,
+        )
+
+        # VS30 values should have been modified by slope
+        assert modified_vs30.shape == vs30.shape
+        assert modified_stdv.shape == stdv.shape
+        # Values should differ due to varying slope
+        assert not np.allclose(modified_vs30, vs30)
+
+    def test_mod6_coastal_modifications_at_points(
+        self, slope_raster, coast_distance_raster
+    ):
+        """Test mod6 (alluvium) coastal modifications at points."""
+        # Points at different locations
+        points = np.array(
+            [
+                [1745000.0, 5425000.0],
+                [1755000.0, 5435000.0],
+            ]
+        )
+
+        vs30 = np.array([300.0, 300.0])
+        stdv = np.array([0.5, 0.5])
+        # GID 4 = Alluvium (mod6 applies)
+        geology_ids = np.array([4, 4])
+
+        modified_vs30, modified_stdv = raster.apply_hybrid_modifications_at_points(
+            points,
+            vs30.copy(),
+            stdv.copy(),
+            geology_ids,
+            slope_raster_path=slope_raster,
+            coast_distance_raster_path=coast_distance_raster,
+            mod6=True,
+            mod13=False,
+            hybrid=False,
+        )
+
+        # VS30 values should be modified (different from input)
+        assert modified_vs30.shape == vs30.shape
+        # Values should be modified to be within mod6 range [240, 500]
+        assert np.all(modified_vs30 >= 240)
+        assert np.all(modified_vs30 <= 500)
+
+    def test_all_modifications_disabled_returns_unchanged(
+        self, slope_raster, coast_distance_raster
+    ):
+        """Test that disabling all modifications returns unchanged values."""
+        points = np.array([[1750000.0, 5430000.0]])
+        vs30 = np.array([300.0])
+        stdv = np.array([0.5])
+        # Use a geology ID that doesn't have special handling
+        geology_ids = np.array([1])
+
+        modified_vs30, modified_stdv = raster.apply_hybrid_modifications_at_points(
+            points,
+            vs30.copy(),
+            stdv.copy(),
+            geology_ids,
+            slope_raster_path=slope_raster,
+            coast_distance_raster_path=coast_distance_raster,
+            mod6=False,
+            mod13=False,
+            hybrid=False,
+        )
+
+        # Values should be unchanged
+        np.testing.assert_array_equal(modified_vs30, vs30)
+        np.testing.assert_array_equal(modified_stdv, stdv)
diff --git a/tests/test_regression.py b/tests/test_regression.py
deleted file mode 100644
index c1a06587..00000000
--- a/tests/test_regression.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-import tempfile
-from pathlib import Path
-
-import pytest
-import pandas as pd
-
-from vs30 import params, vs30calc
-
-INPUT_SITE_FILE = Path(__file__).parent / "resources"/ "test_sites.csv"
-EXPECTED_OUTPUT_DIR = Path(__file__).parent / "expected_location_results"
-
-@pytest.mark.parametrize("n_procs", [1, os.cpu_count()])
-def test_vs30calc_regression(n_procs: int):
-    with tempfile.TemporaryDirectory() as tmpdir:
-        p_paths = params.PathsParams(
-            out=tmpdir,
-            overwrite=True,
-        )
-        p_ll = params.LLFileParams(
-            ll_path=str(INPUT_SITE_FILE),
-            lon_col_ix=1,
-            lat_col_ix=2,
-            col_sep=",",
-        )
-
-        vs30calc.run_vs30calc(
-            p_paths=p_paths,
-            p_sites=params.SitesParams(),
-            p_grid=params.GridParams(),
-            p_ll=p_ll,
-            p_geol=params.GeologyParams(),
-            p_terr=params.TerrainParams(),
-            p_comb=params.CombinationParams(),
-            n_procs=n_procs,
-        )
-
-        expected_vs30_df = pd.read_csv(
-            EXPECTED_OUTPUT_DIR / "vs30points.csv")
-        output_vs30_df = pd.read_csv(
-            Path(tmpdir) / "vs30points.csv")
-        
-        pd.testing.assert_frame_equal(output_vs30_df, expected_vs30_df)
-
-
-            
-
-
-
-
-
-
-
-
diff --git a/tests/test_spatial.py b/tests/test_spatial.py
new file mode 100644
index 00000000..7140a88a
--- /dev/null
+++ b/tests/test_spatial.py
@@ -0,0 +1,225 @@
+"""
+Tests for the VS30 spatial module.
+
+Tests cover:
+- Spatial adjustment computations (MVN conditioning)
+- Cluster subsampling
+- Point-based MVN adjustment
+"""
+
+import numpy as np
+
+from vs30.constants import ModelType
+import pytest
+
+from vs30 import spatial
+
+
+class TestComputeSpatialAdjustmentForPixel:
+    """Tests for the compute_spatial_adjustment_for_pixel function."""
+
+    @pytest.fixture
+    def pixel(self):
+        """Create a test pixel."""
+        return spatial.PixelData(
+            location=np.array([1000.0, 1000.0]),
+            vs30=250.0,
+            stdv=0.4,
+            index=12345,
+        )
+
+    @pytest.fixture
+    def nearby_observation(self):
+        """Create a single nearby observation."""
+        return spatial.ObservationData(
+            locations=np.array([[1100.0, 1000.0]]),  # 100m away
+            vs30=np.array([280.0]),  # Higher than prior
+            model_vs30=np.array([260.0]),
+            model_stdv=np.array([0.4]),
+            residuals=np.array([np.log(280.0 / 260.0)]),
+            omega=np.ones(1),
+            uncertainty=np.array([0.2]),
+        )
+
+    def test_updates_toward_observation(self, pixel, nearby_observation):
+        """Test that update shifts vs30 toward observation."""
+        result = spatial.compute_spatial_adjustment_for_pixel(
+            pixel,
+            nearby_observation,
+            model_type=ModelType.GEOLOGY,
+            max_dist_m=5000.0,
+            max_points=100,
+            noisy=False,
+        )
+
+        # Observation is higher (280), prior is 250, update should increase
+        assert result is not None  # Type guard: narrows Optional return type
+        assert result.updated_vs30 > pixel.vs30
+
+    def test_stdv_decreases_with_observation(self, pixel, nearby_observation):
+        """Test that standard deviation decreases when observation is added."""
+        result = spatial.compute_spatial_adjustment_for_pixel(
+            pixel,
+            nearby_observation,
+            model_type=ModelType.GEOLOGY,
+            max_dist_m=5000.0,
+            max_points=100,
+            noisy=False,
+        )
+
+        # Adding observation should reduce uncertainty
+        assert result is not None  # Type guard: narrows Optional return type
+        assert result.updated_stdv < pixel.stdv
+
+    def test_no_observations_returns_unchanged_vs30(self, pixel):
+        """Test that no nearby observations returns unchanged vs30."""
+        far_observation = spatial.ObservationData(
+            locations=np.array([[100000.0, 100000.0]]),  # Very far
+            vs30=np.array([300.0]),
+            model_vs30=np.array([300.0]),
+            model_stdv=np.array([0.4]),
+            residuals=np.zeros(1),
+            omega=np.ones(1),
+            uncertainty=np.array([0.2]),
+        )
+
+        result = spatial.compute_spatial_adjustment_for_pixel(
+            pixel,
+            far_observation,
+            model_type=ModelType.GEOLOGY,
+            max_dist_m=5000,
+        )
+
+        # VS30 should be unchanged when no nearby observations
+        assert result is not None  # Type guard: narrows Optional return type
+        assert result.updated_vs30 == pixel.vs30
+        assert result.n_observations_used == 0
+
+
+class TestSubsampleByCluster:
+    """Tests for the subsample_by_cluster function."""
+
+    def test_unclustered_all_included(self):
+        """Test that unclustered observations (-1) are all included."""
+        # All unclustered
+        cluster_labels = np.array([-1, -1, -1, -1, -1])
+
+        result = spatial.subsample_by_cluster(cluster_labels, step=2)
+
+        # All should be included since they are unclustered
+        assert len(result) == 5
+
+    def test_clustered_subsampled(self):
+        """Test that clustered observations are subsampled."""
+        # All in one cluster
+        cluster_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])  # 10 points
+
+        result = spatial.subsample_by_cluster(cluster_labels, step=3)
+
+        # Should take every 3rd: indices 0, 3, 6, 9
+        assert len(result) == 4
+
+    def test_mixed_clusters(self):
+        """Test with mixed clustered and unclustered."""
+        cluster_labels = np.array(
+            [
+                -1,
+                -1,
+                -1,  # 3 unclustered
+                0,
+                0,
+                0,
+                0,  # 4 in cluster 0
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,  # 6 in cluster 1
+            ]
+        )
+
+        result = spatial.subsample_by_cluster(cluster_labels, step=2)
+
+        # Unclustered: 3 (all included)
+        # Cluster 0: 2 (indices 0, 2 within cluster -> original indices 3, 5)
+        # Cluster 1: 3 (indices 0, 2, 4 within cluster -> original indices 7, 9, 11)
+        # Total: 3 + 2 + 3 = 8
+        assert len(result) == 8
+
+    def test_at_least_one_per_cluster(self):
+        """Test that at least one observation per cluster is kept."""
+        cluster_labels = np.array([0, 1, 2])  # Each cluster has one point
+
+        result = spatial.subsample_by_cluster(cluster_labels, step=100)
+
+        # Even with large step, each cluster should keep at least one
+        assert len(result) == 3
+
+
+class TestComputeMvnAtPoints:
+    """Tests for compute_spatial_adjustment_at_points function."""
+
+    def test_no_observations_returns_prior(self):
+        """Test that no observations returns prior values with shrunk stdv."""
+        points = np.array([[1500000, 5100000], [1501000, 5101000]])
+        model_vs30 = np.array([300.0, 400.0])
+        model_stdv = np.array([30.0, 40.0])
+
+        # Empty observations
+        obs_locations = np.empty((0, 2))
+        obs_vs30 = np.empty(0)
+        obs_model_vs30 = np.empty(0)
+        obs_model_stdv = np.empty(0)
+        obs_uncertainty = np.empty(0)
+
+        mvn_vs30, mvn_stdv = spatial.compute_spatial_adjustment_at_points(
+            points=points,
+            model_vs30=model_vs30,
+            model_stdv=model_stdv,
+            obs_locations=obs_locations,
+            obs_vs30=obs_vs30,
+            obs_model_vs30=obs_model_vs30,
+            obs_model_stdv=obs_model_stdv,
+            obs_uncertainty=obs_uncertainty,
+            model_type=ModelType.GEOLOGY,
+        )
+
+        # Should return prior vs30 unchanged
+        np.testing.assert_array_equal(mvn_vs30, model_vs30)
+
+    def test_with_nearby_observations(self):
+        """Test spatial adjustment with nearby observations."""
+        # Single query point
+        points = np.array([[1500000.0, 5100000.0]])
+        model_vs30 = np.array([300.0])
+        model_stdv = np.array([30.0])
+
+        # Nearby observation with higher vs30
+        obs_locations = np.array([[1500100.0, 5100100.0]])  # 141m away
+        obs_vs30 = np.array([400.0])
+        obs_model_vs30 = np.array([300.0])
+        obs_model_stdv = np.array([30.0])
+        obs_uncertainty = np.array([25.0])
+
+        mvn_vs30, mvn_stdv = spatial.compute_spatial_adjustment_at_points(
+            points=points,
+            model_vs30=model_vs30,
+            model_stdv=model_stdv,
+            obs_locations=obs_locations,
+            obs_vs30=obs_vs30,
+            obs_model_vs30=obs_model_vs30,
+            obs_model_stdv=obs_model_stdv,
+            obs_uncertainty=obs_uncertainty,
+            model_type=ModelType.GEOLOGY,
+            max_dist_m=5000,
+        )
+
+        # Should adjust toward observation (increase vs30)
+        assert mvn_vs30[0] > model_vs30[0], (
+            "Adjustment should pull vs30 toward observation"
+        )
+        # Uncertainty should decrease
+        assert mvn_stdv[0] < model_stdv[0], (
+            "Uncertainty should decrease with observation"
+        )
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 00000000..a44c8779
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,130 @@
+"""
+Tests for the VS30 utils module.
+
+Tests cover:
+- combine_vs30_models: Model combination in log-space
+"""
+
+import numpy as np
+
+from vs30 import utils
+
+
+class TestCombineVs30Models:
+    """Tests for the combine_vs30_models function.
+
+    This function combines geology and terrain Vs30 models using log-space
+    weighted mixture, matching the algorithm used in the raster-based combine
+    CLI command.
+    """
+
+    def test_equal_ratio_gives_geometric_mean(self):
+        """Test that ratio=1.0 gives geometric mean, not arithmetic mean."""
+        geol_vs30 = np.array([200.0])
+        geol_stdv = np.array([0.3])
+        terr_vs30 = np.array([400.0])
+        terr_stdv = np.array([0.3])
+
+        combined_vs30, combined_stdv = utils.combine_vs30_models(
+            geol_vs30,
+            geol_stdv,
+            terr_vs30,
+            terr_stdv,
+            combination_method=1.0,  # Equal weighting
+        )
+
+        # Geometric mean of 200 and 400 is sqrt(200*400) ≈ 282.84
+        # Arithmetic mean would be 300
+        expected_geometric = np.sqrt(200.0 * 400.0)
+        assert np.isclose(combined_vs30[0], expected_geometric, rtol=0.01)
+        assert combined_vs30[0] < 300.0  # Must be less than arithmetic mean
+
+    def test_ratio_2_gives_more_weight_to_geology(self):
+        """Test that ratio=2.0 gives geology twice the weight of terrain."""
+        geol_vs30 = np.array([200.0])
+        geol_stdv = np.array([0.3])
+        terr_vs30 = np.array([400.0])
+        terr_stdv = np.array([0.3])
+
+        combined_vs30, combined_stdv = utils.combine_vs30_models(
+            geol_vs30,
+            geol_stdv,
+            terr_vs30,
+            terr_stdv,
+            combination_method=2.0,  # Geology has 2x weight
+        )
+
+        # With ratio=2, w_g = 2/3, w_t = 1/3
+        # In log-space: log_comb = (2/3)*log(200) + (1/3)*log(400)
+        # exp(log_comb) = 200^(2/3) * 400^(1/3) ≈ 251.98
+        # Result should be closer to geology (200) than terrain (400)
+        assert combined_vs30[0] < 300.0  # Below midpoint
+        assert combined_vs30[0] > 200.0  # Above geology
+        # More specifically, check against expected value
+        expected = np.exp((2 / 3) * np.log(200.0) + (1 / 3) * np.log(400.0))
+        assert np.isclose(combined_vs30[0], expected, rtol=0.01)
+
+    def test_stdv_weighting_lower_stdv_gets_more_weight(self):
+        """Test that stdv weighting gives more weight to model with lower stdv."""
+        geol_vs30 = np.array([200.0])
+        geol_stdv = np.array([0.1])  # Low uncertainty
+        terr_vs30 = np.array([400.0])
+        terr_stdv = np.array([0.5])  # High uncertainty
+
+        combined_vs30, _ = utils.combine_vs30_models(
+            geol_vs30,
+            geol_stdv,
+            terr_vs30,
+            terr_stdv,
+            combination_method="standard_deviation_weighting",
+        )
+
+        # Geology has lower stdv, so it should get more weight
+        # Result should be closer to geology (200)
+        geometric_mean = np.sqrt(200.0 * 400.0)  # ≈ 282.84
+        assert combined_vs30[0] < geometric_mean  # Closer to geology
+
+    def test_stdv_weighting_equal_stdv_gives_equal_weight(self):
+        """Test that equal stdv gives equal weight in stdv weighting mode."""
+        geol_vs30 = np.array([200.0])
+        geol_stdv = np.array([0.3])
+        terr_vs30 = np.array([400.0])
+        terr_stdv = np.array([0.3])  # Same stdv as geology
+
+        combined_vs30, _ = utils.combine_vs30_models(
+            geol_vs30,
+            geol_stdv,
+            terr_vs30,
+            terr_stdv,
+            combination_method="standard_deviation_weighting",
+        )
+
+        # Equal stdv means equal weight → geometric mean
+        expected_geometric = np.sqrt(200.0 * 400.0)
+        assert np.isclose(combined_vs30[0], expected_geometric, rtol=0.01)
+
+    def test_combined_stdv_formula(self):
+        """Test that combined stdv uses mixture of log-normals formula."""
+        geol_vs30 = np.array([200.0])
+        geol_stdv = np.array([0.3])
+        terr_vs30 = np.array([400.0])
+        terr_stdv = np.array([0.4])
+
+        combined_vs30, combined_stdv = utils.combine_vs30_models(
+            geol_vs30,
+            geol_stdv,
+            terr_vs30,
+            terr_stdv,
+            combination_method=1.0,  # Equal weighting
+        )
+
+        # Manually compute expected stdv
+        # With ratio=1.0: w_g = w_t = 0.5
+        log_g = np.log(200.0)
+        log_t = np.log(400.0)
+        log_comb = 0.5 * log_g + 0.5 * log_t
+        expected_stdv = np.sqrt(
+            0.5 * ((log_g - log_comb) ** 2 + 0.3**2)
+            + 0.5 * ((log_t - log_comb) ** 2 + 0.4**2)
+        )
+        assert np.isclose(combined_stdv[0], expected_stdv, rtol=0.01)
diff --git a/vs30/CLAUDE.md b/vs30/CLAUDE.md
new file mode 100644
index 00000000..645a68c1
--- /dev/null
+++ b/vs30/CLAUDE.md
@@ -0,0 +1,174 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+VS30 is a Python package for calculating and mapping Vs30 (shear-wave velocity at 30m depth) values across New Zealand for seismic hazard analysis. It uses a multi-stage Bayesian framework combining categorical geology/terrain models with spatial observations and statistical adjustments.
+
+**Repository**: https://github.com/ucgmsim/Vs30
+
+**Scientific Reference**: `reference_papers/foster_2019_nz_vs30_map.pdf` provides the scientific context and methodology for this codebase.
+
+## Refactoring Context
+
+This codebase was refactored from legacy code to improve readability and understandability, especially for researchers and non-software engineers, with some performance improvements. The refactored code should produce the same output as the legacy code (small differences due to numerical precision or algorithm changes are acceptable).
+
+**Priority**: Readability is the most important priority and should not be sacrificed for small performance improvements.
+
+**Status**: The refactoring project is nearing completion, but additional features and testing still need to be implemented.
+
+**Validation**: When making large changes, compare the `full-pipeline` output to that produced by the legacy wrapper scripts. Comparison scripts are available in the `temp/` directory (note: `temp/` contains temporary development code that will not be included in the final release).
+
+## Environment Setup
+
+Activate the Python environment before running any commands:
+```bash
+mamba activate vs30_venv
+```
+
+All Python/pytest/vs30 commands must be run with the environment activated, e.g.:
+```bash
+mamba activate vs30_venv && pytest tests/
+mamba activate vs30_venv && vs30 grid
+```
+
+## Legacy Codebase Reference
+
+This code was refactored from a legacy codebase available at:
+```
+/home/arr65/src/pre-refactor-Vs30-for-comparison
+```
+
+To run the legacy code, use a separate environment:
+```bash
+mamba activate oldvs30_venv && python <script>
+```
+
+Helper wrapper scripts in the legacy directory can run the legacy code with the same settings as defined in the refactored code's `config.yaml` file, useful for comparison testing.
+
+## Build and Installation
+
+```bash
+# Install in development mode (extracts shapefiles during install)
+pip install -e /path/to/vs30
+
+# The setup.py custom build step extracts vs30/data/shapefiles.tar.xz
+```
+
+## CLI Commands
+
+The package provides a `vs30` CLI entry point via Typer. Main commands:
+
+```bash
+# Run the full pipeline (most common) - generates raster grids
+vs30 grid
+
+# Compute Vs30 at specific lat/lon locations (without generating rasters)
+vs30 points \
+    --locations-csv sites.csv \
+    --output-csv results.csv
+
+# Individual pipeline stages:
+vs30 update-priors                     # Bayesian update of category means/stdev
+vs30 make-initial-vs30-raster          # Convert categorical models to raster
+vs30 adjust-geology-vs30-by-slope-and-coastal-distance  # Apply hybrid modifications
+vs30 spatial-fit                       # MVN-based spatial adjustments
+vs30 combine                           # Merge geology and terrain models
+vs30 plot-posterior-values             # Visualize posterior distributions
+```
+
+### Location-Based Queries
+
+The `points` command computes Vs30 at specific latitude/longitude points without generating full raster grids. This is efficient for querying a small number of sites.
+
+```bash
+# Basic usage
+vs30 points \
+    --locations-csv sites.csv \
+    --output-csv results.csv
+
+# With custom column names
+vs30 points \
+    --locations-csv sites.csv \
+    --lon-column lon \
+    --lat-column lat \
+    --output-csv results.csv
+
+# With coastal distance raster for hybrid geology modifications
+vs30 points \
+    --locations-csv sites.csv \
+    --output-csv results.csv \
+    --coast-distance-raster /path/to/coast_distance.tif
+```
+
+Input CSV must have longitude and latitude columns (WGS84). Output includes geology/terrain IDs, intermediate Vs30 values, and final combined Vs30.
+
+## Running Tests
+
+```bash
+# Run regression tests (parameterized by processor count)
+pytest tests/test_regression.py
+
+# Run a specific test
+pytest tests/test_regression.py::test_vs30calc_regression -v
+```
+
+## Architecture
+
+### Pipeline Stages
+
+1. **Categorical Update** (`category.py`): Bayesian updating of Vs30 values per geology/terrain category using observations. Uses DBSCAN clustering to prevent over-weighting of spatially clustered measurements.
+
+2. **Raster Creation** (`raster.py`): Creates ID rasters from QMAP geology or IwahashiPike terrain data, then maps category IDs to Vs30 values.
+
+3. **Hybrid Modifications** (`raster.py`): Adjusts geology Vs30 for specific groups based on slope and coastal distance (NZ-empirical calibration).
+
+4. **Spatial Adjustment** (`spatial.py`): MVN (Multivariate Normal) conditioning to update pixels using nearby observations with exponential spatial correlation.
+
+5. **Model Combination** (`cli.py`): Combines geology and terrain models into final prediction.
+
+### Core Modules
+
+- `cli.py` - CLI orchestration, pipeline commands
+- `category.py` - Bayesian updates, observation assignment, DBSCAN clustering
+- `spatial.py` - MVN spatial interpolation (main algorithm complexity)
+- `raster.py` - Raster creation, hybrid geology modifications
+- `constants.py` - Configuration loading from config.yaml
+- `utils.py` - Shared utilities (correlation function, distance calculations)
+
+### Configuration
+
+All parameters centralized in `config.yaml` (loaded at import via `constants.py`):
+- Grid parameters (domain bounds, resolution)
+- Spatial parameters (phi correlation lengths, max_dist_m, max_points)
+- Bayesian parameters (n_prior, min_sigma, DBSCAN eps/min_group)
+- Hybrid model parameters (slope/distance thresholds)
+- Input/output paths
+
+### Key Implementation Details
+
+- **Coordinate System**: NZTM2000 (EPSG:2193), all coordinates in meters
+- **Log-space**: Bayesian updates and MVN conditioning work in log-space (log-normal Vs30 distribution)
+- **Observation Bias**: DBSCAN clustering groups nearby measurements; each cluster = one pseudo-observation
+- **Correlation**: Exponential model `corr = exp(-d/phi)` with separate phi for geology (1407m) and terrain (993m)
+
+### Data Flow
+
+```
+Observations → Bayesian Update → Category CSV
+                                     ↓
+                              Create ID Raster → Map to Vs30 Raster
+                                                       ↓
+                              [Hybrid Mods for geology only]
+                                                       ↓
+                              MVN Spatial Adjustment
+                                                       ↓
+                              Combine Geology + Terrain → Final Map
+```
+
+### Key Data Structures
+
+- `ObservationData` (spatial.py): Bundle of observation locations, vs30, residuals, uncertainties
+- `RasterData` (spatial.py): Input raster with vs30, stddev, and validity masks
+- `MVNUpdateResult` (spatial.py): Output of updating one pixel
diff --git a/vs30/category.py b/vs30/category.py
new file mode 100644
index 00000000..8a7c0444
--- /dev/null
+++ b/vs30/category.py
@@ -0,0 +1,537 @@
+"""
+Functions for relating measurement vs30 values to geology/terrain categories
+and performing Bayesian updates of categorical mean and standard deviation values.
+"""
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import rasterio
+import shapely
+import sklearn.cluster
+
+from vs30 import constants, raster
+
+
+def assign_to_category_geology(points: np.ndarray) -> np.ndarray:
+    """
+    Assign geology category IDs to points using polygon spatial join.
+
+    Uses QMAP shapefile polygons to determine which geology category
+    each point belongs to.
+
+    Parameters
+    ----------
+    points : ndarray
+        2D numpy array of NZTM coordinates (easting, northing).
+
+    Returns
+    -------
+    ndarray
+        Array of category IDs (1-indexed, or constants.RASTER_ID_NODATA_VALUE if outside polygons).
+    """
+    # load QMAP polygons
+    gdf = gpd.read_file(constants.DATA_DIR / constants.GEOLOGY_SHAPEFILE_PATH)[
+        [constants.SHAPEFILE_GEOLOGY_ID_COLUMN, constants.SHAPEFILE_GEOMETRY_COLUMN]
+    ]
+
+    # Build point GeoDataFrame
+    points_shapely = shapely.points(points)
+    points_gdf = gpd.GeoDataFrame(geometry=points_shapely, crs=gdf.crs)
+
+    # Spatial join
+    joined = gpd.sjoin(points_gdf, gdf, how="left", predicate="within")
+
+    # Default to ID_NODATA, fill with gid where available
+    values = np.full(len(points), constants.RASTER_ID_NODATA_VALUE, dtype=np.uint8)
+    value_mask = ~joined[constants.SHAPEFILE_GEOLOGY_ID_COLUMN].isna()
+    values[value_mask] = joined.loc[
+        value_mask, constants.SHAPEFILE_GEOLOGY_ID_COLUMN
+    ].values
+
+    return values
+
+
+def assign_to_category_terrain(points: np.ndarray) -> np.ndarray:
+    """
+    Assign terrain category IDs to points using raster nearest neighbor lookup.
+
+    Uses IwahashiPike terrain raster to determine which terrain category
+    each point belongs to. Reads the category ID value from the pixel
+    containing each point.
+
+    Parameters
+    ----------
+    points : ndarray
+        2D numpy array of NZTM coordinates (easting, northing).
+
+    Returns
+    -------
+    ndarray
+        Array of category IDs (1-indexed, or constants.RASTER_ID_NODATA_VALUE if outside raster).
+    """
+    with rasterio.open(constants.DATA_DIR / constants.TERRAIN_RASTER_FILENAME) as src:
+        terrain_ids = np.array(
+            [s[0] for s in src.sample(points, indexes=1)], dtype=src.dtypes[0]
+        )
+
+        # Handle nodata values
+        if src.nodata is not None:
+            terrain_ids[terrain_ids == src.nodata] = constants.RASTER_ID_NODATA_VALUE
+
+    return terrain_ids
+
+
+def compute_bayesian_posterior_mean(
+    prior_mean: float,
+    num_prior_observations: float,
+    observation_value: float,
+) -> float:
+    """
+    Compute posterior mean using Bayesian update formula.
+
+    Parameters
+    ----------
+    prior_mean : float
+        Prior mean (in linear space, not log space).
+    num_prior_observations : float
+        Effective number of prior observations.
+    observation_value : float
+        New observation value (in linear space).
+
+    Returns
+    -------
+    float
+        Posterior mean (in linear space).
+    """
+
+    weighted_log_mean = (
+        num_prior_observations * np.log(prior_mean) + np.log(observation_value)
+    ) / (num_prior_observations + 1)
+    return np.exp(weighted_log_mean)
+
+
+def compute_bayesian_posterior_variance(
+    prior_stdv: float,
+    num_prior_observations: float,
+    observation_uncertainty: float,
+    prior_mean: float,
+    observation_value: float,
+) -> float:
+    """
+    Compute posterior variance using Bayesian update formula.
+
+    Parameters
+    ----------
+    prior_stdv : float
+        Prior standard deviation.
+    num_prior_observations : float
+        Effective number of prior observations.
+    observation_uncertainty : float
+        Uncertainty (standard deviation) of new observation.
+    prior_mean : float
+        Prior mean.
+    observation_value : float
+        New observation value (in linear space).
+
+    Returns
+    -------
+    float
+        Posterior variance.
+    """
+    log_residual = np.log(observation_value) - np.log(prior_mean)
+    mean_shift = (
+        num_prior_observations / (num_prior_observations + 1)
+    ) * log_residual**2
+    pooled_variance = (
+        num_prior_observations * prior_stdv**2 + observation_uncertainty**2 + mean_shift
+    )
+    return pooled_variance / (num_prior_observations + 1)
+
+
+def update_with_independent_data(
+    categorical_model_df: pd.DataFrame,
+    observations_df: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    Perform Bayesian update of category mean and standard deviation values.
+
+    Finds the posterior model with observations, updating each category's
+    mean and standard deviation based on measurements assigned to that category.
+
+    Parameters
+    ----------
+    categorical_model_df : DataFrame
+        DataFrame with prior mean and standard deviation columns.
+        Can handle various column naming conventions.
+    observations_df : DataFrame
+        Observations containing vs30, uncertainty, and category ID column.
+
+    Returns
+    -------
+    DataFrame
+        Updated DataFrame with posterior mean and standard deviation values.
+        Columns:
+        - constants.COL_POSTERIOR_MEAN_INDEPENDENT
+        - constants.COL_POSTERIOR_STDV_INDEPENDENT
+        - constants.COL_POSTERIOR_NOBS_INDEPENDENT
+        - constants.COL_ASSUMED_NUM_PRIOR_OBS
+        - constants.COL_ENFORCED_MIN_SIGMA
+    """
+    # Make a working copy to avoid modifying the input DataFrame
+    updated_categorical_model_df = categorical_model_df.copy()
+
+    # Setup Bayesian prior from categorical data
+    if constants.COL_POSTERIOR_MEAN_CLUSTERED in updated_categorical_model_df.columns:
+        updated_categorical_model_df[constants.COL_PRIOR_MEAN] = (
+            updated_categorical_model_df[constants.COL_POSTERIOR_MEAN_CLUSTERED]
+        )
+        updated_categorical_model_df[constants.COL_PRIOR_STDV] = (
+            updated_categorical_model_df[constants.COL_POSTERIOR_STDV_CLUSTERED]
+        )
+    elif constants.COL_MEAN in updated_categorical_model_df.columns:
+        # Initial prior format - rename to prior_ columns
+        updated_categorical_model_df = updated_categorical_model_df.rename(
+            columns={
+                constants.COL_MEAN: constants.COL_PRIOR_MEAN,
+                constants.COL_STDV: constants.COL_PRIOR_STDV,
+            }
+        )
+    else:
+        raise ValueError(
+            f"No usable prior information found. Expected either posterior columns from "
+            f"previous Bayesian update or initial categorical model columns ('{constants.COL_MEAN}', "
+            f"'{constants.COL_STDV}')."
+        )
+
+    # Enforce minimum sigma value on prior
+    updated_categorical_model_df[constants.COL_PRIOR_STDV] = np.clip(
+        updated_categorical_model_df[constants.COL_PRIOR_STDV].values,
+        constants.MIN_SIGMA,
+        None,
+    )
+
+    # Initialize posterior columns
+    updated_categorical_model_df[constants.COL_ASSUMED_NUM_PRIOR_OBS] = (
+        constants.N_PRIOR
+    )
+    updated_categorical_model_df[constants.COL_ENFORCED_MIN_SIGMA] = constants.MIN_SIGMA
+    updated_categorical_model_df[constants.COL_POSTERIOR_MEAN_INDEPENDENT] = (
+        updated_categorical_model_df[constants.COL_PRIOR_MEAN]
+    )
+    updated_categorical_model_df[constants.COL_POSTERIOR_STDV_INDEPENDENT] = (
+        updated_categorical_model_df[constants.COL_PRIOR_STDV]
+    )
+    updated_categorical_model_df[constants.COL_POSTERIOR_NOBS_INDEPENDENT] = (
+        constants.N_PRIOR
+    )
+
+    for category_row_idx, category_row in updated_categorical_model_df.iterrows():
+        # Match observations to this category using model_id
+        category_id = category_row[constants.STANDARD_ID_COLUMN]
+        observations_for_category_df = observations_df[
+            observations_df[constants.STANDARD_ID_COLUMN] == category_id
+        ]
+
+        # Initialize running values for sequential update
+        current_mean = category_row[constants.COL_POSTERIOR_MEAN_INDEPENDENT]
+        current_std = category_row[constants.COL_POSTERIOR_STDV_INDEPENDENT]
+        current_n = category_row[constants.COL_POSTERIOR_NOBS_INDEPENDENT]
+
+        for _, observation_row in observations_for_category_df.iterrows():
+            new_variance = compute_bayesian_posterior_variance(
+                current_std,
+                current_n,
+                observation_row[constants.COL_UNCERTAINTY],
+                current_mean,
+                observation_row[constants.COL_VS30],
+            )
+
+            new_mean = compute_bayesian_posterior_mean(
+                current_mean,
+                current_n,
+                observation_row[constants.COL_VS30],
+            )
+
+            # Update running values for next iteration
+            current_mean = new_mean
+            current_std = np.sqrt(new_variance)
+            current_n += 1
+
+        # Write final posterior values for this category
+        updated_categorical_model_df.at[
+            category_row_idx, constants.COL_POSTERIOR_MEAN_INDEPENDENT
+        ] = current_mean
+        updated_categorical_model_df.at[
+            category_row_idx, constants.COL_POSTERIOR_STDV_INDEPENDENT
+        ] = current_std
+        updated_categorical_model_df.at[
+            category_row_idx, constants.COL_POSTERIOR_NOBS_INDEPENDENT
+        ] = current_n
+
+    return updated_categorical_model_df
+
+
+def perform_clustering(
+    sites_df: pd.DataFrame,
+    nproc: int = -1,
+) -> pd.DataFrame:
+    """
+    Apply DBSCAN clustering to sites DataFrame, adding cluster assignments.
+
+    Clusters sites spatially within each category to avoid over-weighting
+    dense measurement clusters. Clustering is performed separately for each
+    category ID.
+
+    Parameters
+    ----------
+    sites_df : DataFrame
+        Observations DataFrame with columns: constants.STANDARD_ID_COLUMN, easting, northing.
+        Must have category IDs already assigned.
+    nproc : int, optional
+        Number of processes for DBSCAN. -1 to use all available cores.
+        Default is -1.
+
+    Returns
+    -------
+    DataFrame
+        Modified DataFrame with added "cluster" column containing cluster IDs.
+        -1 indicates unclustered points.
+
+    Notes
+    -----
+    Uses MIN_GROUP and EPS constants from constants.py for DBSCAN parameters.
+    """
+    sites_df = sites_df.copy()
+    # Default not a member of any cluster
+    sites_df[constants.COL_CLUSTER] = constants.CLUSTER_UNCLUSTERED_LABEL
+
+    features = np.column_stack(
+        (
+            sites_df[constants.COL_EASTING].values,
+            sites_df[constants.COL_NORTHING].values,
+        )
+    )
+    model_ids = sites_df[constants.STANDARD_ID_COLUMN].values
+    ids = np.unique(model_ids)
+    ids = ids[ids != constants.RASTER_ID_NODATA_VALUE].astype(int)
+
+    # Perform DBSCAN clustering
+    for category_id in ids:
+        if features[model_ids == category_id].shape[0] < constants.MIN_GROUP:
+            # Can't form any groups
+            continue
+        dbscan = sklearn.cluster.DBSCAN(
+            eps=constants.EPS, min_samples=constants.MIN_GROUP, n_jobs=nproc
+        )
+        dbscan.fit(features[model_ids == category_id])
+        sites_df.loc[model_ids == category_id, constants.COL_CLUSTER] = dbscan.labels_
+
+    return sites_df
+
+
+def compute_cluster_weighted_mean_and_stddev(
+    category_sites: pd.DataFrame,
+    cluster_counts: pd.Series,
+    effective_n: int,
+) -> tuple[float, float]:
+    """Compute cluster-weighted geometric mean and log-space standard deviation.
+
+    Clusters contribute their geometric mean as a single pseudo-observation.
+    Unclustered points each contribute individually.
+
+    Parameters
+    ----------
+    category_sites : DataFrame
+        Observation sites for one category, with vs30 and cluster columns.
+    cluster_counts : Series
+        Value counts of cluster labels for this category.
+    effective_n : int
+        Number of effective independent observations.
+
+    Returns
+    -------
+    tuple[float, float]
+        (geometric_mean_vs30, log_space_standard_deviation)
+    """
+    weighted_log_vs30_sum = 0.0
+    weights = np.repeat(1.0 / effective_n, len(category_sites))
+
+    for cluster_label in cluster_counts.index:
+        cluster_mask = category_sites[constants.COL_CLUSTER] == cluster_label
+        cluster_sites = category_sites[cluster_mask]
+        if cluster_label == constants.CLUSTER_UNCLUSTERED_LABEL:
+            weighted_log_vs30_sum += np.sum(
+                np.log(cluster_sites[constants.COL_VS30].values)
+            )
+        else:
+            weighted_log_vs30_sum += np.sum(
+                np.log(cluster_sites[constants.COL_VS30].values)
+            ) / len(cluster_sites)
+            weights[cluster_mask] /= len(cluster_sites)
+
+    log_geometric_mean = weighted_log_vs30_sum / effective_n
+    geometric_mean_vs30 = np.exp(log_geometric_mean)
+    log_stddev = np.sqrt(
+        np.sum(
+            weights
+            * (np.log(category_sites[constants.COL_VS30].values) - log_geometric_mean)
+            ** 2
+        )
+    )
+    return geometric_mean_vs30, log_stddev
+
+
+def update_with_clustered_data(
+    prior_df: pd.DataFrame,
+    sites_df: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    Perform Bayesian update for clustered CPT data.
+
+    Each DBSCAN cluster contributes a single effective observation
+    (the geometric mean of its members), preventing spatially dense
+    geotechnical investigations from dominating the posterior.
+    Unclustered points (label = -1) each count as one observation.
+
+    Parameters
+    ----------
+    prior_df : DataFrame
+        Prior categorical model with mean and standard deviation columns.
+    sites_df : DataFrame
+        Clustered observation sites with vs30, cluster, and category ID columns.
+
+    Returns
+    -------
+    DataFrame
+        Updated DataFrame with posterior mean and standard deviation columns.
+    """
+    # Create a copy to update
+    posterior_df = prior_df.copy()
+
+    if constants.COL_PRIOR_MEAN not in posterior_df.columns:
+        if constants.COL_MEAN in posterior_df.columns:
+            # Initial prior format - rename to prior_ columns
+            posterior_df = posterior_df.rename(
+                columns={
+                    constants.COL_MEAN: constants.COL_PRIOR_MEAN,
+                    constants.COL_STDV: constants.COL_PRIOR_STDV,
+                }
+            )
+
+    # Initialize posterior columns with suffix
+    posterior_df[constants.COL_POSTERIOR_MEAN_CLUSTERED] = posterior_df[
+        constants.COL_PRIOR_MEAN
+    ]
+    posterior_df[constants.COL_POSTERIOR_STDV_CLUSTERED] = posterior_df[
+        constants.COL_PRIOR_STDV
+    ]
+
+    # Filter out sites with ID_NODATA
+    valid_sites = sites_df[
+        sites_df[constants.STANDARD_ID_COLUMN] != constants.RASTER_ID_NODATA_VALUE
+    ].copy()
+
+    # Build a mapping from category ID to DataFrame index for direct updates
+    id_to_idx = dict(
+        zip(
+            posterior_df[constants.STANDARD_ID_COLUMN].astype(int),
+            posterior_df.index,
+        )
+    )
+
+    # Process each category ID that exists in the sites
+    unique_ids = valid_sites[constants.STANDARD_ID_COLUMN].unique()
+
+    for category_id in unique_ids:
+        category_id_int = int(category_id)
+        if category_id_int not in id_to_idx:
+            continue
+
+        category_sites = valid_sites[
+            valid_sites[constants.STANDARD_ID_COLUMN] == category_id_int
+        ]
+        cluster_counts = category_sites[constants.COL_CLUSTER].value_counts()
+
+        # Effective independent observations: one per cluster, plus each unclustered point.
+        effective_n = len(cluster_counts)
+        if constants.CLUSTER_UNCLUSTERED_LABEL in cluster_counts.index:
+            effective_n += cluster_counts[constants.CLUSTER_UNCLUSTERED_LABEL] - 1
+        if effective_n == 0:
+            continue
+
+        mean_vs30, stddev = compute_cluster_weighted_mean_and_stddev(
+            category_sites, cluster_counts, effective_n
+        )
+        df_idx = id_to_idx[category_id_int]
+        posterior_df.at[df_idx, constants.COL_POSTERIOR_MEAN_CLUSTERED] = mean_vs30
+        posterior_df.at[df_idx, constants.COL_POSTERIOR_STDV_CLUSTERED] = stddev
+
+    return posterior_df
+
+
+def get_vs30_for_ids(
+    category_ids: np.ndarray,
+    categorical_model_df: pd.DataFrame,
+) -> pd.DataFrame:
+    """Get Vs30 mean and standard deviation for category IDs from categorical model.
+
+    Looks up the Vs30 mean and standard deviation for each category ID
+    from the categorical model DataFrame.
+
+    Parameters
+    ----------
+    category_ids : np.ndarray
+        Array of category IDs (e.g. from assign_to_category_geology or
+        assign_to_category_terrain).
+    categorical_model_df : pd.DataFrame
+        DataFrame with columns for category ID, Vs30 mean, and Vs30 standard deviation.
+        Supports various column naming conventions (see Notes).
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with columns:
+        - constants.COL_CATEGORY_VS30_MEAN: Vs30 mean values (m/s) for each
+          category ID. NaN for IDs not found in the model.
+        - constants.COL_CATEGORY_VS30_STDV: Vs30 standard deviation values for
+          each category ID. NaN for IDs not found in the model.
+
+    Notes
+    -----
+    The function automatically detects the column naming convention in the
+    categorical model DataFrame. See raster.select_vs30_columns_by_priority
+    for the priority order.
+    """
+    mean_col, stdv_col = raster.select_vs30_columns_by_priority(
+        list(categorical_model_df.columns)
+    )
+
+    # Build lookup dictionaries from category ID to Vs30 values
+    id_to_vs30 = dict(
+        zip(
+            categorical_model_df[constants.STANDARD_ID_COLUMN],
+            categorical_model_df[mean_col],
+        )
+    )
+    id_to_stdv = dict(
+        zip(
+            categorical_model_df[constants.STANDARD_ID_COLUMN],
+            categorical_model_df[stdv_col],
+        )
+    )
+
+    # Look up Vs30 values for each point
+    vs30_mean = np.array(
+        [id_to_vs30.get(cid, np.nan) for cid in category_ids], dtype=np.float64
+    )
+    vs30_stdv = np.array(
+        [id_to_stdv.get(cid, np.nan) for cid in category_ids], dtype=np.float64
+    )
+
+    return pd.DataFrame(
+        {
+            constants.COL_CATEGORY_VS30_MEAN: vs30_mean,
+            constants.COL_CATEGORY_VS30_STDV: vs30_stdv,
+        }
+    )
diff --git a/vs30/cli.py b/vs30/cli.py
new file mode 100644
index 00000000..c5f1d0eb
--- /dev/null
+++ b/vs30/cli.py
@@ -0,0 +1,276 @@
+"""Command-line interface for the vs30 package."""
+
+import logging
+import typing
+from pathlib import Path
+
+import typer
+from qcore import cli
+
+from vs30 import constants, pipeline
+from vs30 import config as config_module
+
+logger = logging.getLogger(__name__)
+
+# Create Typer app for CLI
+app = typer.Typer(name="vs30", help="VS30 map generation and categorical model updates")
+
+_cli_config: config_module.Vs30Config | None = None
+
+
+def get_config() -> config_module.Vs30Config:
+    """
+    Get the current CLI configuration.
+
+    Returns the config set by the --config option, or the default
+    package config if no custom config was specified.
+
+    Returns
+    -------
+    Vs30Config
+        The current CLI configuration object.
+    """
+    global _cli_config
+    if _cli_config is None:
+        _cli_config = config_module.Vs30Config.default()
+    return _cli_config
+
+
+@app.callback()
+def main(
+    config: typing.Annotated[
+        Path | None,
+        typer.Option(
+            exists=True,
+            dir_okay=False,
+            help="Path to config.yaml file (default: package config.yaml)",
+        ),
+    ] = None,
+    verbose: typing.Annotated[
+        bool,
+        typer.Option(help="Enable verbose logging"),
+    ] = False,
+) -> None:
+    """
+    VS30 map generation and categorical model updates.
+
+    This is the main entry point for the VS30 CLI. Global options like config
+    file path and verbosity are specified here and apply to all subcommands.
+
+    Parameters
+    ----------
+    config : Path, optional
+        Path to config.yaml file. If not specified, uses the default
+        configuration bundled with the package.
+    verbose : bool, optional
+        Enable verbose (DEBUG level) logging output.
+    """
+    global _cli_config
+
+    if verbose:
+        logging.basicConfig(level=logging.DEBUG)
+
+    # Load config from specified path or default
+    # Note: typer's exists=True already validates the file exists before reaching here
+    if config is not None:
+        _cli_config = config_module.Vs30Config.from_yaml(config)
+        logger.info(f"Loaded config from {config}")
+    else:
+        _cli_config = config_module.Vs30Config.default()
+        logger.debug(
+            f"Using default config from {config_module.Vs30Config.default_config_path()}"
+        )
+
+
+@cli.from_docstring(app)
+def update_priors(
+    categorical_model_csv: typing.Annotated[
+        Path, typer.Argument(exists=True, dir_okay=False)
+    ],
+    output_dir: typing.Annotated[Path, typer.Argument(file_okay=False)],
+    model_type: typing.Annotated[constants.ModelType, typer.Option()],
+    clustered_observations_csv: typing.Annotated[
+        Path | None,
+        typer.Option(exists=True, dir_okay=False),
+    ] = None,
+    independent_observations_csv: typing.Annotated[
+        Path | None,
+        typer.Option(exists=True, dir_okay=False),
+    ] = None,
+    nproc: typing.Annotated[int | None, typer.Option()] = None,
+) -> None:
+    """
+    Update categorical model values using Bayesian updates and save to CSV files.
+
+    Parameters
+    ----------
+    categorical_model_csv : Path
+        Path to CSV file with categorical Vs30 mean and standard deviation values.
+    output_dir : Path
+        Path to output directory. Will be created if it does not exist.
+    model_type : str
+        Model type: either 'geology' or 'terrain'.
+    clustered_observations_csv : Path, optional
+        Path to CSV file with clustered observations (e.g., measured_vs30_cpt.csv).
+    independent_observations_csv : Path, optional
+        Path to CSV file with independent observations.
+    nproc : int, optional
+        Number of processes for DBSCAN clustering. Use -1 for all available cores.
+        Default from config.
+    """
+    cfg = get_config()
+    pipeline.update_categorical_vs30_models(
+        categorical_model_csv=categorical_model_csv,
+        output_dir=output_dir,
+        model_type=model_type,
+        clustered_observations_csv=clustered_observations_csv,
+        independent_observations_csv=independent_observations_csv,
+        nproc=nproc if nproc is not None else cfg.n_proc,
+    )
+
+
+@cli.from_docstring(app)
+def grid(
+    geology_categorical_csv: typing.Annotated[
+        Path | None, typer.Option("--geology-csv", exists=True, dir_okay=False)
+    ] = None,
+    terrain_categorical_csv: typing.Annotated[
+        Path | None, typer.Option("--terrain-csv", exists=True, dir_okay=False)
+    ] = None,
+    clustered_observations_csv: typing.Annotated[
+        Path | None,
+        typer.Option(exists=True, dir_okay=False),
+    ] = None,
+    independent_observations_csv: typing.Annotated[
+        Path | None,
+        typer.Option(exists=True, dir_okay=False),
+    ] = None,
+    output_dir: typing.Annotated[
+        Path | None, typer.Option(file_okay=False)
+    ] = None,
+    nproc: typing.Annotated[int | None, typer.Option()] = None,
+    combination_method: typing.Annotated[str | None, typer.Option()] = None,
+    n_proc: typing.Annotated[int | None, typer.Option()] = None,
+) -> None:
+    """
+    Run the full VS30 generation pipeline for both geology and terrain models.
+
+    Parameters
+    ----------
+    geology_categorical_csv : Path, optional
+        Path to geology categorical CSV. Default from config/resources.
+    terrain_categorical_csv : Path, optional
+        Path to terrain categorical CSV. Default from config/resources.
+    clustered_observations_csv : Path, optional
+        Path to CSV file with clustered observations (e.g., CPT data).
+    independent_observations_csv : Path, optional
+        Path to CSV file with independent observations (e.g., measured filtered).
+    output_dir : Path, optional
+        Directory to save all pipeline outputs. Default from config.
+    nproc : int, optional
+        Number of processes for clustering.
+    combination_method : str, optional
+        Method for combining models. Either a ratio (float) or
+        'standard_deviation_weighting'.
+    n_proc : int, optional
+        Number of parallel processes for spatial adjustment. Use -1 for all cores.
+        Default from config.
+    """
+    cfg = get_config()
+    pipeline.run_full_pipeline(
+        cfg=cfg,
+        geology_categorical_csv=geology_categorical_csv,
+        terrain_categorical_csv=terrain_categorical_csv,
+        clustered_observations_csv=clustered_observations_csv,
+        independent_observations_csv=independent_observations_csv,
+        output_dir=output_dir,
+        nproc=nproc,
+        combination_method=combination_method,
+        n_proc=n_proc,
+    )
+
+
+@cli.from_docstring(app)
+def points(
+    locations_csv: typing.Annotated[
+        Path | None, typer.Option(exists=True, dir_okay=False)
+    ] = None,
+    output_csv: typing.Annotated[
+        Path | None, typer.Option(dir_okay=False)
+    ] = None,
+    lon_column: typing.Annotated[str | None, typer.Option()] = None,
+    lat_column: typing.Annotated[str | None, typer.Option()] = None,
+    geology_categorical_csv: typing.Annotated[
+        Path | None, typer.Option("--geology-csv", exists=True, dir_okay=False)
+    ] = None,
+    terrain_categorical_csv: typing.Annotated[
+        Path | None, typer.Option("--terrain-csv", exists=True, dir_okay=False)
+    ] = None,
+    clustered_observations_csv: typing.Annotated[
+        Path | None,
+        typer.Option(exists=True, dir_okay=False),
+    ] = None,
+    independent_observations_csv: typing.Annotated[
+        Path | None,
+        typer.Option(exists=True, dir_okay=False),
+    ] = None,
+    coast_distance_raster: typing.Annotated[
+        Path | None,
+        typer.Option(exists=True, dir_okay=False),
+    ] = None,
+    include_intermediate: typing.Annotated[
+        bool, typer.Option("--include-intermediate/--final-only")
+    ] = True,
+    combination_method: typing.Annotated[str | None, typer.Option()] = None,
+    n_proc: typing.Annotated[int | None, typer.Option()] = None,
+) -> None:
+    """
+    Compute Vs30 values at specific latitude/longitude locations.
+
+    Parameters
+    ----------
+    locations_csv : Path, optional
+        CSV file with latitude/longitude columns (WGS84). Default from config.
+    output_csv : Path, optional
+        Output CSV file path. Default from config.
+    lon_column : str, optional
+        Name of longitude column in input CSV.
+    lat_column : str, optional
+        Name of latitude column in input CSV.
+    geology_categorical_csv : Path, optional
+        Path to geology categorical CSV (default from config/resources).
+    terrain_categorical_csv : Path, optional
+        Path to terrain categorical CSV (default from config/resources).
+    clustered_observations_csv : Path, optional
+        Path to CSV file with clustered observations (e.g., CPT).
+    independent_observations_csv : Path, optional
+        Path to CSV file with independent observations.
+    coast_distance_raster : Path, optional
+        Path to coastal distance raster (required for hybrid geology modifications).
+    include_intermediate : bool
+        Include intermediate values (geology/terrain separately) in output.
+    combination_method : str, optional
+        Method for combining: ratio (float) or 'standard_deviation_weighting'.
+    n_proc : int, optional
+        Number of parallel processes (default from config, -1 for all cores).
+    """
+    cfg = get_config()
+    pipeline.compute_at_locations(
+        cfg=cfg,
+        locations_csv=locations_csv,
+        output_csv=output_csv,
+        lon_column=lon_column,
+        lat_column=lat_column,
+        geology_categorical_csv=geology_categorical_csv,
+        terrain_categorical_csv=terrain_categorical_csv,
+        clustered_observations_csv=clustered_observations_csv,
+        independent_observations_csv=independent_observations_csv,
+        coast_distance_raster=coast_distance_raster,
+        include_intermediate=include_intermediate,
+        combination_method=combination_method,
+        n_proc=n_proc,
+    )
+
+
+if __name__ == "__main__":  # pragma: no cover
+    app()
diff --git a/vs30/config.py b/vs30/config.py
new file mode 100644
index 00000000..53b399b1
--- /dev/null
+++ b/vs30/config.py
@@ -0,0 +1,194 @@
+"""Pydantic configuration model for loading and validating settings from config.yaml."""
+
+from pathlib import Path
+
+import pydantic
+import yaml
+
+
+class Vs30Config(pydantic.BaseModel):
+    """
+    User-configurable settings for VS30 calculations.
+
+    All fields correspond to entries in config.yaml. See that file for
+    detailed descriptions of each parameter's meaning and units.
+
+    For scientific/algorithmic constants, see vs30/constants.py.
+
+    Attributes
+    ----------
+    n_proc : int
+        Number of processors for parallel processing (-1 for all cores).
+    grid_xmin : int
+        Grid minimum X coordinate (NZTM, meters).
+    grid_xmax : int
+        Grid maximum X coordinate (NZTM, meters).
+    grid_ymin : int
+        Grid minimum Y coordinate (NZTM, meters).
+    grid_ymax : int
+        Grid maximum Y coordinate (NZTM, meters).
+    grid_dx : int
+        Grid X spacing (meters).
+    grid_dy : int
+        Grid Y spacing (meters).
+    locations_csv : str or None
+        Path to input CSV with locations for compute-at-locations.
+    locations_output_csv : str or None
+        Path to output CSV for compute-at-locations results.
+    noisy : bool
+        Whether measurements are noisy (affects uncertainty weighting).
+    max_spatial_boolean_array_memory_gb : float
+        Maximum memory (GB) for spatial boolean arrays per process.
+    obs_subsample_step_for_clustered : int
+        Subsampling step for clustered observations in affected pixel search.
+    independent_observations_file : str or None
+        Path to independent observations CSV (relative to resources).
+    clustered_observations_file : str or None
+        Path to clustered observations CSV (relative to resources).
+    output_dir : str
+        Output directory path.
+    combination_method : str or float
+        Method for combining models: ratio (float) or 'standard_deviation_weighting'.
+    do_bayesian_update_of_geology_and_terrain_categorical_vs30_values : bool
+        Whether to perform Bayesian update of categorical values.
+    """
+
+    # --- Processor settings ---
+    n_proc: int = pydantic.Field(
+        default=1,
+        description="Number of processors for parallel processing (-1 for all cores)",
+    )
+
+    # --- Grid parameters ---
+    grid_xmin: int = pydantic.Field(
+        description="Grid minimum X coordinate (NZTM, meters)"
+    )
+    grid_xmax: int = pydantic.Field(
+        description="Grid maximum X coordinate (NZTM, meters)"
+    )
+    grid_ymin: int = pydantic.Field(
+        description="Grid minimum Y coordinate (NZTM, meters)"
+    )
+    grid_ymax: int = pydantic.Field(
+        description="Grid maximum Y coordinate (NZTM, meters)"
+    )
+    grid_dx: int = pydantic.Field(description="Grid X spacing (meters)")
+    grid_dy: int = pydantic.Field(description="Grid Y spacing (meters)")
+
+    # --- Compute-at-locations parameters (only used by compute-at-locations) ---
+    locations_csv: str | None = pydantic.Field(
+        default=None,
+        description="Path to input CSV with locations for compute-at-locations",
+    )
+    locations_output_csv: str | None = pydantic.Field(
+        default=None, description="Path to output CSV for compute-at-locations results"
+    )
+
+    # --- General configuration ---
+    noisy: bool = pydantic.Field(
+        description="Whether measurements are noisy (affects uncertainty weighting)"
+    )
+    max_spatial_boolean_array_memory_gb: float = pydantic.Field(
+        description="Maximum memory (GB) for spatial boolean arrays per process"
+    )
+    obs_subsample_step_for_clustered: int = pydantic.Field(
+        description="Subsampling step for clustered observations in affected pixel search"
+    )
+
+    # --- File paths (relative to resources directory) ---
+    independent_observations_file: str | None = pydantic.Field(
+        default=None,
+        description="Path to independent observations CSV (relative to resources)",
+    )
+    clustered_observations_file: str | None = pydantic.Field(
+        default=None,
+        description="Path to clustered observations CSV (relative to resources)",
+    )
+    output_dir: str = pydantic.Field(description="Output directory path")
+
+    # --- Combination settings ---
+    combination_method: str | float = pydantic.Field(
+        description="Method for combining models: ratio (float) or 'standard_deviation_weighting'"
+    )
+    do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: bool = (
+        pydantic.Field(
+            description="Whether to perform Bayesian update of categorical values"
+        )
+    )
+
+    # =========================================================================
+    # Class methods for loading
+    # =========================================================================
+
+    @classmethod
+    def from_yaml(cls, path: Path) -> "Vs30Config":
+        """
+        Load configuration from a YAML file.
+
+        Parameters
+        ----------
+        path : Path
+            Path to the YAML configuration file.
+
+        Returns
+        -------
+        Vs30Config
+            Validated configuration object.
+
+        Raises
+        ------
+        FileNotFoundError
+            If the configuration file does not exist.
+        pydantic.ValidationError
+            If the configuration file is missing required fields or has invalid values.
+        """
+        if not path.exists():
+            raise FileNotFoundError(f"Configuration file not found: {path}")
+        with open(path, encoding="utf-8") as f:
+            data = yaml.safe_load(f)
+        return cls(**data)
+
+    @classmethod
+    def default_config_path(cls) -> Path:
+        """
+        Get the path to the package's default config.yaml.
+
+        Returns
+        -------
+        Path
+            Path to the bundled config.yaml file.
+        """
+        return Path(__file__).parent / "config.yaml"
+
+    @classmethod
+    def default(cls) -> "Vs30Config":
+        """
+        Load from the package's default config.yaml.
+
+        Returns
+        -------
+        Vs30Config
+            Configuration loaded from the package's bundled config.yaml.
+        """
+        return cls.from_yaml(cls.default_config_path())
+
+
+_default_config: Vs30Config | None = None
+
+
+def get_default_config() -> Vs30Config:
+    """
+    Get the default configuration, loading it on first access.
+
+    This provides lazy loading of the default config, so it's only
+    read from disk when first needed.
+
+    Returns
+    -------
+    Vs30Config
+        The default configuration object.
+    """
+    global _default_config
+    if _default_config is None:
+        _default_config = Vs30Config.default()
+    return _default_config
diff --git a/vs30/config.yaml b/vs30/config.yaml
new file mode 100644
index 00000000..a8bf907c
--- /dev/null
+++ b/vs30/config.yaml
@@ -0,0 +1,99 @@
+# ============================================================================
+# Vs30 CONFIGURATION FILE
+# This file defines all inputs, outputs, and parameters for Vs30 calculations.
+# Parameters are ordered from most frequently changed (top) to rarely
+# modified (bottom).
+# ============================================================================
+
+# Output directory for all output files
+output_dir: /tmp/refactored_vs30_output
+
+# ============================================================================
+# RASTER GRID DOMAIN
+# Domain bounds and resolution for 2D Vs30 raster map production.
+# Only required for raster operations (full-pipeline, make-initial-vs30-raster, etc.).
+# These have no effect when computing Vs30 at a list of specified locations.
+# This example defines a small 100m x 100m domain.
+# ============================================================================
+grid_xmin: 1570050
+grid_xmax: 1571050
+grid_ymin: 5180050
+grid_ymax: 5181050
+grid_dx: 100
+grid_dy: 100
+
+# ============================================================================
+# COMPUTE-AT-LOCATIONS PARAMETERS
+# These parameters are only used by the compute-at-locations command, which
+# computes Vs30 at specific latitude/longitude locations without generating
+# raster grids. They have no effect on other commands (full-pipeline, etc.).
+# All can be overridden by CLI arguments (--locations-csv, --output-csv, etc.).
+# ============================================================================
+
+# Path to input CSV file containing locations (latitude/longitude in WGS84).
+# Set to null if not using compute-at-locations,
+# or if providing the path via CLI argument instead.
+locations_csv: null
+
+# Path to output CSV file for computed Vs30 results.
+# Set to null if not using compute-at-locations,
+# or if providing the path via CLI argument instead.
+locations_output_csv: null
+
+# ============================================================================
+# OBSERVATION FILES
+# ============================================================================
+
+# Input files containing the observed Vs30 values.
+# Paths are relative to the resources directory.
+
+# Path to file containing observed Vs30 values that
+# should be treated independently (without clustering).
+# Set to null to run without independent observations.
+independent_observations_file: observations/measured_vs30_independent_observations.csv
+
+# Path to a file containing observed Vs30 values that
+# should be treated with spatial clustering (e.g. Vs30 inferred from dense CPT measurements).
+# Set to null to run without clustered observations.
+clustered_observations_file: observations/measured_vs30_cpt.csv
+
+# ============================================================================
+# GENERAL PIPELINE SETTINGS
+# ============================================================================
+
+# Number of processors to use for finding affected pixels, spatial updates, and DBSCAN clustering.
+# -1 uses all available cores.
+n_proc: -1
+
+# Whether to perform Bayesian update of geology and terrain categorical Vs30 values
+do_bayesian_update_of_geology_and_terrain_categorical_vs30_values: true
+
+# ============================================================================
+# MODEL COMBINATION PARAMETERS
+# ============================================================================
+
+# Method for combining geology and terrain models
+# value can be:
+# - float: Ratio of geology weight to terrain weight (e.g. 1.0 = equal weight, 2.0 = geology has 2x weight)
+# - string: "standard_deviation_weighting" (uses K_VALUE constant)
+combination_method: 1.0
+
+# ============================================================================
+# SPATIAL UPDATE PARAMETERS
+# ============================================================================
+
+# Whether measurements are "noisy" (affects noise weighting in the spatial multivariate normal (MVN) update).
+# When true, observations with higher uncertainty contribute less to pixel updates.
+noisy: true
+
+# Maximum memory in GB for spatial Boolean arrays during chunked processing per process.
+# Note: Total memory usage per process will be several GB more than this limit.
+max_spatial_boolean_array_memory_gb: 1
+
+# Observation subsampling step for find_affected_pixels when using clustered observations.
+# When observations are spatially clustered, nearby observations affect the same pixels.
+# Using every Nth observation (e.g., 100) significantly reduces computation time
+# while producing nearly identical results for the affected pixel mask.
+# Set to 1 for no subsampling (use all observations).
+# Only applies to clustered observations; independent observations always use step=1.
+obs_subsample_step_for_clustered: 100
\ No newline at end of file
diff --git a/vs30/constants.py b/vs30/constants.py
new file mode 100644
index 00000000..50eeb8e6
--- /dev/null
+++ b/vs30/constants.py
@@ -0,0 +1,400 @@
+"""Scientific and algorithmic constants for Vs30 calculations."""
+
+from dataclasses import dataclass
+from enum import StrEnum
+from pathlib import Path
+
+# Path to the data directory containing shapefiles, rasters, and other input data
+DATA_DIR = Path(__file__).parent / "data"
+
+# Path to the resources directory containing CSV files with categorical model parameters
+RESOURCE_PATH = Path(__file__).parent / "resources"
+
+# Covariance reduction factor for dissimilar Vs30 values (dimensionless).
+# Controls how much the correlation between two points is reduced when their
+# model Vs30 values differ. Higher values = more reduction for dissimilar values.
+COV_REDUC: float = 1.5
+
+# Correlation length parameters (phi) in meters.
+# Phi represents the distance at which spatial correlation decays to ~37% (1/e).
+# Larger phi = smoother spatial interpolation, smaller phi = more localized updates.
+# These values were calibrated for New Zealand geology and terrain data.
+PHI_GEOLOGY: int = 1407
+PHI_TERRAIN: int = 993
+
+# Dictionary for convenient access by model type
+# Note: PHI uses string keys for now; will be updated after ModelType is defined below.
+
+# Minimum distance (meters) enforced in correlation calculations to prevent
+# division by zero or correlation=1 when points are exactly co-located.
+# The correlation function uses exp(-distance/phi), so distance=0 gives correlation=1.
+MIN_DIST_ENFORCED: float = 0.1
+
+# Maximum distance (meters) for considering observations in multivariate normal
+# (MVN) spatial adjustment. Observations further than this distance from a pixel
+# will not influence its update.
+MAX_DIST_M: int = 10000
+
+# Maximum number of observations to consider per pixel during multivariate normal
+# (MVN) spatial adjustment. If a pixel is within MAX_DIST_M of more than MAX_POINTS
+# observations, only the MAX_POINTS closest observations will be considered.
+MAX_POINTS: int = 500
+
+# K value for standard deviation based weighting when combining geology and
+# terrain models. Represents the exponent for inverse variance weighting:
+# weight ~ (sigma^2)^-k. Only used when combination_method is "standard_deviation_weighting".
+K_VALUE: float = 3.0
+
+# Combination method string for standard deviation weighting
+COMBINATION_METHOD_STDV_WEIGHTING: str = "standard_deviation_weighting"
+
+# Small epsilon value added to variance when computing inverse-variance weights
+# for combining geology and terrain models. Prevents division by zero when
+# standard deviation is exactly zero.
+WEIGHT_EPSILON_DIV_BY_ZERO: float = 1.0e-10
+
+# Assumed initial number of prior observations (n0 in Bayesian formulas)
+# for Bayesian update of Vs30 mean and standard deviation values
+# for each geology and terrain category.
+N_PRIOR: int = 3
+
+# Minimum standard deviation (log-space) allowed after Bayesian update.
+# Prevents over-confidence when many observations are available.
+MIN_SIGMA: float = 0.5
+
+# DBSCAN clustering parameters for spatially clustered observations
+# such as Vs30 inferred from dense CPT measurements.
+
+# Minimum number of observations to form a cluster (DBSCAN min_samples parameter)
+MIN_GROUP: int = 5
+
+# Maximum distance (meters) between observations to be in the same cluster
+# (DBSCAN epsilon parameter). Points further apart will be in separate clusters.
+EPS: float = 15000.0
+
+# CATEGORICAL MODEL FILES
+# Mean and standard deviation of Vs30 values for each geology and terrain category.
+# Paths are relative to the resources directory.
+
+GEOLOGY_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE: Path = Path(
+    "categorical_vs30_mean_and_stddev/geology/"
+    "geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv"
+)
+TERRAIN_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE: Path = Path(
+    "categorical_vs30_mean_and_stddev/terrain/"
+    "terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv"
+)
+
+# INTERNAL DATA FILES
+# Filenames for input data files bundled with the package.
+# These are relative to the vs30/data directory.
+
+# Terrain classification raster (IwahashiPike terrain categories)
+TERRAIN_RASTER_FILENAME: str = "IwahashiPike.tif"
+
+# Geology shapefile (QMAP geology polygons)
+GEOLOGY_SHAPEFILE_PATH: str = "qmap/qmap.shp"
+
+# Coastline shapefile for coastal distance calculations
+COASTLINE_SHAPEFILE_PATH: str = (
+    "coast/nz-coastlines-and-islands-polygons-topo-1500k.shp"
+)
+
+# Source slope raster (used if slope.tif needs to be generated)
+SLOPE_SOURCE_RASTER_FILENAME: str = "slope.tif"
+
+# Archive containing shapefiles (extracted on first use)
+SHAPEFILES_ARCHIVE_FILENAME: str = "shapefiles.tar.xz"
+
+# Prefix used to indicate that Bayesian updates have been performed
+POSTERIOR_PREFIX: str = "posterior_"
+
+# Initial Vs30 map constructed from terrain classifications
+TERRAIN_INITIAL_VS30_FILENAME: str = "initial_terrain_vs30_with_uncertainty.tif"
+
+# Initial Vs30 map constructed from geology classifications
+GEOLOGY_INITIAL_VS30_FILENAME: str = "initial_geology_vs30_with_uncertainty.tif"
+
+# Slopes used for hybrid geology Vs30 model
+SLOPE_RASTER_FILENAME: str = "slope.tif"
+
+# Coastal distance used for hybrid geology Vs30 model
+COAST_DISTANCE_RASTER_FILENAME: str = "coast_distance.tif"
+
+# Geology Vs30 map adjusted for slope and coastal distance
+GEOLOGY_VS30_SLOPE_AND_COASTAL_DISTANCE_ADJUSTED_FILENAME: str = (
+    "geology_vs30_slope_and_coastal_distance_adjusted_with_uncertainty.tif"
+)
+
+# Map showing the geology category ID for each pixel
+GEOLOGY_ID_FILENAME: str = "gid.tif"
+
+# Map showing the terrain category ID for each pixel
+TERRAIN_ID_FILENAME: str = "tid.tif"
+
+# Final terrain Vs30 after spatial adjustment
+TERRAIN_VS30_MEAN_STDDEV_FILENAME: str = (
+    "terrain_vs30_spatially_adjusted_with_uncertainty.tif"
+)
+
+# Final geology Vs30 after slope, coastal distance, and spatial adjustment
+GEOLOGY_VS30_MEAN_STDDEV_FILENAME: str = "geology_vs30_slope_and_coastal_distance_and_spatially_adjusted_with_uncertainty.tif"
+
+# Combined weighted average of geology and terrain Vs30
+COMBINED_VS30_FILENAME: str = "combined_vs30.tif"
+
+# OUTPUT_FILENAMES and PHI dictionaries are defined after ModelType class below
+
+# HYBRID GEOLOGY Vs30 MODEL PARAMETERS
+# (Adjusts according to slope and coastal distance)
+
+HYBRID_MOD6_DIST_MIN: float = 8000.0
+HYBRID_MOD6_DIST_MAX: float = 20000.0
+HYBRID_MOD6_VS30_MIN: float = 240.0
+HYBRID_MOD6_VS30_MAX: float = 500.0
+
+HYBRID_MOD13_DIST_MIN: float = 8000.0
+HYBRID_MOD13_DIST_MAX: float = 20000.0
+HYBRID_MOD13_VS30_MIN: float = 197.0
+HYBRID_MOD13_VS30_MAX: float = 500.0
+
+
+@dataclass
+class HybridVs30Param:
+    """
+    Parameters for slope-based Vs30 interpolation per geology group.
+
+    Attributes
+    ----------
+    gid : int
+        Geology group ID.
+    slope_limits : list[float]
+        Log10(slope) limits for interpolation [min, max].
+    vs30_values : list[float]
+        Vs30 values (m/s) at the slope limits [at_min_slope, at_max_slope].
+    """
+
+    gid: int
+    slope_limits: list[float]
+    vs30_values: list[float]
+
+
+# Hybrid slope-based Vs30 interpolation parameters
+# Each entry contains: geology group ID, log10(slope) limits, Vs30 values
+HYBRID_VS30_PARAMS: list[HybridVs30Param] = [
+    HybridVs30Param(gid=2, slope_limits=[-1.85, -1.22], vs30_values=[242, 418]),
+    HybridVs30Param(gid=3, slope_limits=[-2.70, -1.35], vs30_values=[171, 228]),
+    HybridVs30Param(gid=4, slope_limits=[-3.44, -0.88], vs30_values=[252, 275]),
+    HybridVs30Param(gid=6, slope_limits=[-3.56, -0.93], vs30_values=[183, 239]),
+]
+
+# Hybrid standard deviation reduction factors for specific geology groups
+# Maps geology group ID to sigma reduction factor
+HYBRID_SIGMA_REDUCTION_FACTORS: dict[int, float] = {
+    2: 0.4888,
+    3: 0.7103,
+    4: 0.9988,
+    6: 0.9348,
+}
+
+# Minimum slope value used to prevent log10(0) when calculating hybrid Vs30
+MIN_SLOPE_FOR_LOG: float = 1.0e-9
+
+# No data value in the provided categorical rasters
+RASTER_ID_NODATA_VALUE: int = 255
+
+# No-data value used for slope rasters, model outputs, and CSV placeholders
+# -32767 is a traditional no-data value in many GIS applications
+# (minimum value for signed 16-bit integers)
+NODATA_VALUE: int = -32767
+
+# FULL NEW ZEALAND LAND EXTENT BOUNDS
+# (For coastal distance calculations)
+# IMPORTANT: These values define the full extent of New Zealand land coverage
+# and MUST NOT be changed. They are used to ensure coastal distance calculations
+# are computed on the full NZ land extent, regardless of the configured study
+# domain bounds.
+FULL_NZ_LAND_XMIN: int = 1060050
+FULL_NZ_LAND_XMAX: int = 2120050
+FULL_NZ_LAND_YMIN: int = 4730050
+FULL_NZ_LAND_YMAX: int = 6250050
+
+# Default column names for longitude and latitude in location input CSV files.
+LOCATIONS_LON_COLUMN: str = "longitude"
+LOCATIONS_LAT_COLUMN: str = "latitude"
+
+# Number of chunks to split locations into for parallel processing progress updates.
+N_PROGRESS_CHUNKS: int = 1000
+
+# Column names used in categorical model DataFrames for Bayesian updates.
+# These are used to identify posterior/prior values at different stages.
+COL_POSTERIOR_MEAN_INDEPENDENT: str = (
+    "posterior_mean_vs30_km_per_s_independent_observations"
+)
+COL_POSTERIOR_STDV_INDEPENDENT: str = (
+    "posterior_standard_deviation_vs30_km_per_s_independent_observations"
+)
+COL_POSTERIOR_NOBS_INDEPENDENT: str = (
+    "posterior_num_observations_independent_observations"
+)
+COL_POSTERIOR_MEAN_CLUSTERED: str = (
+    "posterior_mean_vs30_km_per_s_clustered_observations"
+)
+COL_POSTERIOR_STDV_CLUSTERED: str = (
+    "posterior_standard_deviation_vs30_km_per_s_clustered_observations"
+)
+COL_POSTERIOR_MEAN: str = "posterior_mean_vs30_km_per_s"
+COL_POSTERIOR_STDV: str = "posterior_standard_deviation_vs30_km_per_s"
+COL_PRIOR_MEAN: str = "prior_mean_vs30_km_per_s"
+COL_PRIOR_STDV: str = "prior_standard_deviation_vs30_km_per_s"
+COL_MEAN: str = "mean_vs30_km_per_s"
+COL_STDV: str = "standard_deviation_vs30_km_per_s"
+COL_ASSUMED_NUM_PRIOR_OBS: str = "assumed_num_prior_observations"
+COL_ENFORCED_MIN_SIGMA: str = "enforced_min_sigma"
+
+# Standard column name for category ID in DataFrames.
+STANDARD_ID_COLUMN: str = "id"
+
+# Coordinate Reference System for New Zealand Transverse Mercator 2000
+NZTM_CRS: str = "EPSG:2193"
+
+PLOT_FIGSIZE: list[int] = [12, 8]
+PLOT_DPI: int = 300
+
+# OBSERVATION DATA COLUMN NAMES
+# Standard column names for observation DataFrames used throughout the package.
+
+COL_EASTING: str = "easting"
+COL_NORTHING: str = "northing"
+COL_VS30: str = "vs30"
+COL_UNCERTAINTY: str = "uncertainty"
+COL_CLUSTER: str = "cluster"
+
+# Column names for the DataFrame returned by get_vs30_for_ids,
+# which maps category IDs to their categorical model Vs30 values.
+COL_CATEGORY_VS30_MEAN: str = "category_vs30_mean"
+COL_CATEGORY_VS30_STDV: str = "category_vs30_stdv"
+
+# Cluster label for unclustered/noise points in DBSCAN output
+CLUSTER_UNCLUSTERED_LABEL: int = -1
+
+# OUTPUT CSV COLUMN NAMES
+# Column names for compute-at-locations output CSV files.
+
+COL_GEOLOGY_ID: str = "geology_id"
+COL_GEOLOGY_VS30: str = "geology_vs30"
+COL_GEOLOGY_STDV: str = "geology_stdv"
+COL_GEOLOGY_VS30_HYBRID: str = "geology_vs30_hybrid"
+COL_GEOLOGY_STDV_HYBRID: str = "geology_stdv_hybrid"
+COL_GEOLOGY_MVN_VS30: str = "geology_mvn_vs30"
+COL_GEOLOGY_MVN_STDV: str = "geology_mvn_stdv"
+COL_TERRAIN_ID: str = "terrain_id"
+COL_TERRAIN_VS30: str = "terrain_vs30"
+COL_TERRAIN_STDV: str = "terrain_stdv"
+COL_TERRAIN_MVN_VS30: str = "terrain_mvn_vs30"
+COL_TERRAIN_MVN_STDV: str = "terrain_mvn_stdv"
+COL_COMBINED_STDV: str = "stdv"
+
+# PARALLEL PROCESSING DICTIONARY KEYS
+# Keys used in dictionaries for multiprocessing data transfer.
+
+KEY_LOCATIONS: str = "locations"
+KEY_MODEL_VS30: str = "model_vs30"
+KEY_MODEL_STDV: str = "model_stdv"
+KEY_RESIDUALS: str = "residuals"
+KEY_OMEGA: str = "omega"
+KEY_LOCATION: str = "location"
+KEY_STDV: str = "stdv"
+KEY_INDEX: str = "index"
+KEY_MODEL_TYPE: str = "model_type"
+KEY_MAX_DIST_M: str = "max_dist_m"
+KEY_MAX_POINTS: str = "max_points"
+KEY_NOISY: str = "noisy"
+KEY_COV_REDUC: str = "cov_reduc"
+
+# Required columns for observation DataFrames
+REQUIRED_OBSERVATION_COLUMNS: list[str] = [
+    COL_EASTING,
+    COL_NORTHING,
+    COL_VS30,
+    COL_UNCERTAINTY,
+]
+REQUIRED_OBSERVATION_COLUMNS_BASIC: list[str] = [
+    COL_EASTING,
+    COL_NORTHING,
+    COL_VS30,
+]
+
+# MODEL TYPE IDENTIFIERS
+# String identifiers for the two model types used in the Vs30 pipeline.
+
+
+class ModelType(StrEnum):
+    """Valid model types for VS30 calculations."""
+
+    GEOLOGY = "geology"
+    TERRAIN = "terrain"
+
+
+# Dictionaries for convenient access by model type
+PHI: dict[ModelType, int] = {
+    ModelType.GEOLOGY: PHI_GEOLOGY,
+    ModelType.TERRAIN: PHI_TERRAIN,
+}
+
+OUTPUT_FILENAMES: dict[ModelType, str] = {
+    ModelType.GEOLOGY: GEOLOGY_VS30_MEAN_STDDEV_FILENAME,
+    ModelType.TERRAIN: TERRAIN_VS30_MEAN_STDDEV_FILENAME,
+}
+
+# RASTER BAND INDICES
+# Band numbers for multi-band VS30 rasters (1-indexed as per rasterio convention).
+
+RASTER_BAND_VS30: int = 1
+RASTER_BAND_STDV: int = 2
+
+# GEOTIFF OPTIONS
+# Standard options for writing GeoTIFF raster files.
+
+GEOTIFF_DRIVER: str = "GTiff"
+GEOTIFF_COMPRESSION: str = "deflate"
+GEOTIFF_TILED: bool = True
+GEOTIFF_BIGTIFF: str = "yes"
+
+# RASTER BAND DESCRIPTIONS
+# Standard descriptions for raster bands.
+
+BAND_DESCRIPTION_ID_INDEX: str = "Model ID Index"
+BAND_DESCRIPTION_VS30: str = "Vs30"
+BAND_DESCRIPTION_STDV: str = "Standard Deviation"
+BAND_DESCRIPTION_VS30_HYBRID: str = "Vs30 (Hybrid)"
+BAND_DESCRIPTION_STDV_HYBRID: str = "Standard Deviation (Hybrid)"
+BAND_DESCRIPTION_VS30_COMBINED: str = "Vs30 (Combined Average)"
+BAND_DESCRIPTION_STDV_COMBINED: str = "Standard Deviation (Combined Average)"
+BAND_DESCRIPTION_COAST_DISTANCE: str = "Distance to Coast (m)"
+BAND_DESCRIPTION_SLOPE: str = "Slope"
+
+# SHAPEFILE COLUMN NAMES
+# Column names used in input shapefiles.
+
+SHAPEFILE_GEOLOGY_ID_COLUMN: str = "gid"
+SHAPEFILE_GEOMETRY_COLUMN: str = "geometry"
+
+# SPATIAL PROCESSING CONSTANTS
+# Constants used in spatial coordinate and pixel calculations.
+
+# Offset to convert pixel indices to pixel centers (0.5 = center of pixel)
+PIXEL_CENTER_OFFSET: float = 0.5
+
+# PLOT STYLING CONSTANTS
+# Standard styling parameters for matplotlib plots.
+
+PLOT_X_OFFSET: float = 0.2
+PLOT_ERRORBAR_CAPSIZE: int = 5
+PLOT_ERRORBAR_CAPTHICK: float = 1.5
+PLOT_MARKER_SIZE: int = 6
+PLOT_ALPHA: float = 0.7
+PLOT_GRID_ALPHA: float = 0.3
+PLOT_LABEL_FONTSIZE: int = 12
+PLOT_TITLE_FONTSIZE: int = 14
+PLOT_LEGEND_FONTSIZE: int = 11
diff --git a/vs30/model.py b/vs30/model.py
deleted file mode 100644
index 9999e0a7..00000000
--- a/vs30/model.py
+++ /dev/null
@@ -1,228 +0,0 @@
-"""
-Shared model functions for processing GIS data and processing/combining models.
-"""
-
-from math import exp, log, sqrt
-import os
-
-import numpy as np
-from osgeo import gdal, gdalconst
-
-gdal.UseExceptions()
-
-ID_NODATA = 255
-
-
-def interpolate_raster(points, raster, band=1):
-    """
-    Returns values of raster at points (nearneighbour).
-    points: 2D numpy array of coords in raster srs.
-    """
-    r = gdal.Open(raster, gdal.GA_ReadOnly)
-    t = r.GetGeoTransform()
-    b = r.GetRasterBand(band)
-    n = b.GetNoDataValue()
-
-    x = np.floor((points[:, 0] - t[0]) / t[1]).astype(np.int32)
-    y = np.floor((points[:, 1] - t[3]) / t[5]).astype(np.int32)
-    valid = np.where((x >= 0) & (x < r.RasterXSize) & (y >= 0) & (y < r.RasterYSize))
-    v = np.full(
-        len(points), ID_NODATA, dtype=b.ReadAsArray(win_xsize=1, win_ysize=1).dtype
-    )
-    v[valid] = b.ReadAsArray()[y[valid], x[valid]]
-    # defined nodata in case nodata in tif is different, so model_val() understands
-    if n != ID_NODATA:
-        v = np.where(v == n, ID_NODATA, v)
-
-    b = None
-    r = None
-    return v
-
-
-def resample_raster(
-    src, dst, xmin, xmax, ymin, ymax, xd, yd, alg=gdalconst.GRIORA_NearestNeighbour
-):
-    """
-    Resample Tif File.
-    """
-    gdal.Warp(
-        dst,
-        src,
-        creationOptions=["COMPRESS=DEFLATE", "BIGTIFF=YES"],
-        outputBounds=[xmin, ymin, xmax, ymax],
-        xRes=xd,
-        yRes=yd,
-        resampleAlg=alg,
-    )
-
-
-def combine_models(opts, vs30a, stdva, vs30b, stdvb):
-    """
-    Combine 2 models.
-    """
-    if opts.stdv_weight:
-        m_a = (stdva**2) ** -opts.k
-        m_b = (stdvb**2) ** -opts.k
-        w_a = m_a / (m_a + m_b)
-        w_b = m_b / (m_a + m_b)
-    else:
-        w_a = 0.5
-        w_b = 0.5
-
-    log_ab = np.log(vs30a) * w_a + np.log(vs30b) * w_b
-    stdv = np.sqrt(
-        w_a * ((np.log(vs30a) - log_ab) ** 2 + stdva**2)
-        + w_b * ((np.log(vs30b) - log_ab) ** 2 + stdvb**2)
-    )
-
-    return np.exp(log_ab), stdv
-
-
-def combine_tiff(out_dir, filename, grid, opts, a, b):
-    """
-    Combine geology and terrain models (given path to geotiff files).
-    """
-    # model a
-    ads = gdal.Open(a, gdal.GA_ReadOnly)
-    a_vs30 = ads.GetRasterBand(1)
-    a_stdv = ads.GetRasterBand(2)
-    # model b
-    bds = gdal.Open(b, gdal.GA_ReadOnly)
-    b_vs30 = bds.GetRasterBand(1)
-    b_stdv = bds.GetRasterBand(2)
-    # output
-    driver = gdal.GetDriverByName("GTiff")
-    ods = driver.Create(
-        os.path.join(out_dir, filename),
-        xsize=grid.nx,
-        ysize=grid.ny,
-        bands=2,
-        eType=gdal.GDT_Float32,
-        options=["COMPRESS=DEFLATE", "BIGTIFF=YES"],
-    )
-    ods.SetGeoTransform(ads.GetGeoTransform())
-    ods.SetProjection(ads.GetProjection())
-    o_vs30 = ods.GetRasterBand(1)
-    o_stdv = ods.GetRasterBand(2)
-    o_vs30.SetDescription("Vs30")
-    o_stdv.SetDescription("Standard Deviation")
-    vnd = a_vs30.GetNoDataValue()
-    snd = a_stdv.GetNoDataValue()
-    o_vs30.SetNoDataValue(vnd)
-    o_stdv.SetNoDataValue(snd)
-
-    # processing chunk/block sizing
-    block = o_vs30.GetBlockSize()
-    nxb = (int)((grid.nx + block[0] - 1) / block[0])
-    nyb = (int)((grid.ny + block[1] - 1) / block[1])
-
-    for x in range(nxb):
-        xoff = x * block[0]
-        # last block may be smaller
-        if x == nxb - 1:
-            block[0] = grid.nx - x * block[0]
-        # reset y block size
-        block_y = block[1]
-
-        for y in range(nyb):
-            yoff = y * block[1]
-            # last block may be smaller
-            if y == nyb - 1:
-                block_y = grid.ny - y * block[1]
-
-            # determine results on block
-            avv = a_vs30.ReadAsArray(
-                xoff=xoff, yoff=yoff, win_xsize=block[0], win_ysize=block_y
-            )
-            avv[avv == vnd] = np.nan
-            asv = a_stdv.ReadAsArray(
-                xoff=xoff, yoff=yoff, win_xsize=block[0], win_ysize=block_y
-            )
-            asv[asv == snd] = np.nan
-            bvv = b_vs30.ReadAsArray(
-                xoff=xoff, yoff=yoff, win_xsize=block[0], win_ysize=block_y
-            )
-            bvv[bvv == vnd] = np.nan
-            bsv = b_stdv.ReadAsArray(
-                xoff=xoff, yoff=yoff, win_xsize=block[0], win_ysize=block_y
-            )
-            bsv[bsv == snd] = np.nan
-            vs30, stdv = combine_models(opts, avv, asv, bvv, bsv)
-
-            # write results
-            o_vs30.WriteArray(vs30, xoff=xoff, yoff=yoff)
-            o_stdv.WriteArray(stdv, xoff=xoff, yoff=yoff)
-    # close
-    o_vs30 = None
-    o_stdv = None
-    ods = None
-
-
-def cluster_update(prior, sites, letter):
-    # creates a model from the distribution of measured sites as clustered
-    # prior: prior model, values only taken if no measurements available for ID
-    posterior = np.copy(prior)
-    # looping through model IDs
-    for m in range(len(posterior)):
-        vs_sum = 0
-        # add 1 because IDs being used start at 1 in tiffs
-        idtable = sites[sites[f"{letter}id"] == m + 1]
-        clusters = idtable[f"{letter}cluster"].value_counts()
-        # overall N is one per cluster, clusters labeled -1 are individual clusters
-        n = len(clusters)
-        if -1 in clusters.index:
-            n += clusters[-1] - 1
-        if n == 0:
-            continue
-        w = np.repeat(1 / n, len(idtable))
-        for c in clusters.index:
-            cidx = idtable[f"{letter}cluster"] == c
-            ctable = idtable[cidx]
-            if c == -1:
-                # values not part of cluster, weight = 1 per value
-                vs_sum += sum(np.log(ctable.vs30.values))
-            else:
-                # values in cluster, weight = 1 / cluster_size per value
-                vs_sum += sum(np.log(ctable.vs30)) / len(ctable)
-                w[cidx] /= len(ctable)
-        posterior[m, 0] = exp(vs_sum / n)
-        posterior[m, 1] = np.sqrt(
-            sum(w * (np.log(idtable.vs30.values) - vs_sum / n) ** 2)
-        )
-
-    return posterior
-
-
-def _new_mean(mu_0, n0, var, y):
-    return exp((n0 / var * log(mu_0) + log(y) / var) / (n0 / var + 1 / var))
-
-
-def _new_var(sigma_0, n0, uncertainty):
-    return (n0 * sigma_0 * sigma_0 + uncertainty * uncertainty) / (n0 + 1)
-
-
-def posterior(model, sites, idcol, n_prior=3, min_sigma=0.5):
-    """
-    model: prior model
-    sites: sites containing vs30 and uncertainty
-    idcol: model ID column in sites
-    n_prior: assume prior model made up of n_prior measurements
-    min_sigma: minimum model_stdv allowed
-    """
-
-    # new model
-    vs30 = model[:, 0]
-    stdv = np.maximum(model[:, 1], min_sigma)
-
-    # loop through observed
-    n0 = np.repeat(n_prior, len(model))
-    for _, r in sites.iterrows():
-        m = r[idcol]
-        if m == ID_NODATA:
-            continue
-        var = _new_var(stdv[m], n0[m], r.uncertainty)
-        vs30[m] = _new_mean(vs30[m], n0[m], var, r.vs30)
-        stdv[m] = sqrt(var)
-        n0[m] += 1
-
-    return np.column_stack((vs30, stdv))
diff --git a/vs30/model_geology.py b/vs30/model_geology.py
deleted file mode 100644
index 8abb9b34..00000000
--- a/vs30/model_geology.py
+++ /dev/null
@@ -1,385 +0,0 @@
-"""
-Geology model functions based on GNS QMAP geology categories and AhdiAk values.
-"""
-
-from copy import deepcopy
-import os
-
-import numpy as np
-from osgeo import gdal, ogr, osr
-import geopandas as gpd
-import shapely
-
-from vs30.model import ID_NODATA, interpolate_raster, resample_raster
-
-gdal.UseExceptions()
-
-data = os.path.join(os.path.dirname(__file__), "data")
-QMAP = os.path.join(data, "qmap", "qmap.shp")
-COAST = os.path.join(data, "coast", "nz-coastlines-and-islands-polygons-topo-1500k.shp")
-SLOPE = os.path.join(data, "slope.tif")
-SLOPE_NODATA = -9999
-MODEL_NODATA = -32767
-# hybrid model vs30 based on interpolation of slope
-# group ID, log10(slope) array, vs30 array
-HYBRID_VS30 = [
-    [2, [-1.85, -1.22], np.log10(np.array([242, 418]))],
-    [3, [-2.70, -1.35], np.log10(np.array([171, 228]))],
-    [4, [-3.44, -0.88], np.log10(np.array([252, 275]))],
-    [6, [-3.56, -0.93], np.log10(np.array([183, 239]))],
-]
-# hybrid model sigma reduction factors
-HYBRID_SRF = np.array([2, 3, 4, 6]), np.array([0.4888, 0.7103, 0.9988, 0.9348])
-
-
-def model_prior():
-    """
-    ID NAME (id in datasource)
-    0  00_water (not used)
-    1  01_peat
-    2  04_fill
-    3  05_fluvialEstuarine
-    4  06_alluvium
-    5  08_lacustrine
-    6  09_beachBarDune
-    7  10_fan
-    8  11_loess
-    9  12_outwash
-    10 13_floodplain
-    11 14_moraineTill
-    12 15_undifSed
-    13 16_terrace
-    14 17_volcanic
-    15 18_crystalline
-    """
-    # fmt: off
-    return np.array([[161, 0.522],
-                     [198, 0.314],
-                     [239, 0.867],
-                     [323, 0.365],
-                     [326, 0.135],
-                     [339, 0.647],
-                     [360, 0.338],
-                     [376, 0.380],
-                     [399, 0.305],
-                     [448, 0.432],
-                     [453, 0.512],
-                     [455, 0.545],
-                     [458, 0.761],
-                     [635, 0.995],
-                     [750, 0.641]], dtype=np.float32)
-    # fmt: on
-
-
-def model_posterior_paper():
-    """
-    Posterior model from the paper.
-    """
-    # fmt: off
-    return np.array([[162.892120019443, 0.301033220758108],
-                     [272.512711921894, 0.280305955290694],
-                     [199.502400319993, 0.438753673163297],
-                     [271.050623687222, 0.243486579272276],
-                     [326, 0.5],
-                     [204.373998521848, 0.232196981798137],
-                     [246.6138371423, 0.344601218802256],
-                     [472.749388885179, 0.354562104171167],
-                     [399, 0.5],
-                     [197.472849225266, 0.202629769603115],
-                     [453, 0.512],
-                     [455, 0.545],
-                     [335.279503497185, 0.602886888230288],
-                     [635, 0.995],
-                     [690.974348966211, 0.446036993981441]], dtype=np.float32)
-    # fmt: on
-
-def model_id(points: np.ndarray) -> np.ndarray:
-    """
-    Returns the category ID index (including 0 for water) for given locations.
-    points: 2D numpy array of NZTM coords
-
-    Uses geopandas/shapely spatial join.
-    """
-    # load QMAP polygons (keeps CRS from file)
-    gdf = gpd.read_file(QMAP)[["gid", "geometry"]]
-
-    # Build point GeoDataFrame (ensure float64)
-    points = shapely.points(points)
-    points_gdf = gpd.GeoDataFrame(geometry=points, crs=gdf.crs)
-
-    # Spatial join
-    joined = gpd.sjoin(points_gdf, gdf, how="left", predicate="within")
-
-    # Default to ID_NODATA, fill with gid where available
-    values = np.full(len(points), ID_NODATA, dtype=np.uint8)
-    value_mask = ~joined["gid"].isna()
-    values[value_mask] = joined.loc[value_mask, "gid"].values
-
-    return values
-
-
-def model_id_fast(points, paths, grid):
-    """
-    Faster version of model_id that uses rasterisation instead of polygons.
-    points: 2D numpy array of NZTM coords
-    """
-    gid_tif = model_id_map(paths, grid)
-    return interpolate_raster(points, gid_tif)
-
-
-def model_id_map(paths, grid):
-    """
-    Optimised polygon search using geotiff rasterisation.
-    """
-    path = os.path.join(paths.out, "gid.tif")
-    if os.path.isfile(path):
-        return path
-    # make sure output raster has a nicely defined projection
-    srs = osr.SpatialReference()
-    srs.ImportFromEPSG(2193)
-    # calling Rasterize without saving result to variable will fail
-    ds = gdal.Rasterize(
-        path,
-        QMAP,
-        creationOptions=["COMPRESS=DEFLATE", "BIGTIFF=YES"],
-        outputSRS=srs,
-        outputBounds=[grid.xmin, grid.ymin, grid.xmax, grid.ymax],
-        xRes=grid.dx,
-        yRes=grid.dy,
-        noData=ID_NODATA,
-        attribute="gid",
-        outputType=gdal.GDT_Byte,
-    )
-    band = ds.GetRasterBand(1)
-    band.SetDescription("Geology ID Index")
-    band = None
-    ds = None
-    return path
-
-
-def _full_land_grid(grid):
-    """
-    Extends grid for full land coverage.
-    """
-    landgrid = deepcopy(grid)
-    gridmod = False
-    if grid.xmin > 1060050:
-        landgrid.xmin -= np.ceil((grid.xmin - 1060050) / grid.dx) * grid.dx
-        gridmod = True
-    if grid.xmax < 2120050:
-        landgrid.xmax += np.ceil((2120050 - grid.xmax) / grid.dx) * grid.dx
-        gridmod = True
-    if grid.ymin > 4730050:
-        landgrid.ymin -= np.ceil((grid.ymin - 4730050) / grid.dy) * grid.dy
-        gridmod = True
-    if grid.ymax < 6250050:
-        landgrid.ymax += np.ceil((6250050 - grid.ymax) / grid.dy) * grid.dy
-        gridmod = True
-
-    return landgrid, gridmod
-
-
-def coast_distance_map(paths, grid):
-    """
-    Calculate coast distance needed for G06 and G13 mods.
-    """
-    path = os.path.join(paths.out, "coast.tif")
-    if os.path.isfile(path):
-        return path
-
-    # algorithm requires full land coverage
-    landgrid, gridmod = _full_land_grid(grid)
-    # only need UInt16 (~65k max val) because coast only used 8->20k
-    ds = gdal.Rasterize(
-        path,
-        COAST,
-        creationOptions=["COMPRESS=DEFLATE", "BIGTIFF=YES"],
-        outputBounds=[landgrid.xmin, landgrid.ymin, landgrid.xmax, landgrid.ymax],
-        xRes=landgrid.dx,
-        yRes=landgrid.dy,
-        noData=0,
-        burnValues=1,
-        outputType=gdal.GetDataTypeByName("UInt16"),
-    )
-    # distance calculation from outside polygons (0 value)
-    band = ds.GetRasterBand(1)
-    band.SetDescription("Distance to Coast (m)")
-    # ComputeProximity doesn't respect any NODATA options (writing into self though)
-    ds = gdal.ComputeProximity(band, band, ["VALUES=0", "DISTUNITS=GEO"])
-    band = None
-    ds = None
-
-    if gridmod:
-        # had to extend for land coverage, cut down to wanted size
-        resample_raster(
-            path, path, grid.xmin, grid.xmax, grid.ymin, grid.ymax, grid.dx, grid.dy
-        )
-
-    return path
-
-
-def slope_map(paths, grid):
-    """
-    Calculate slope at map points by resampling / resizing origin slope map.
-    """
-    dst = os.path.join(paths.out, "slope.tif")
-    if os.path.isfile(dst):
-        return dst
-    resample_raster(
-        SLOPE, dst, grid.xmin, grid.xmax, grid.ymin, grid.ymax, grid.dx, grid.dy
-    )
-    return dst
-
-
-def _hyb_calc(
-    geol, model, gid, slope=None, cdist=None, nan=np.nan, s_nodata=SLOPE_NODATA
-):
-    """
-    Return model values given inputs.
-    """
-    # sigma reduction factors
-    if geol.hybrid:
-        model = np.copy(model)
-        # still updating g06 even if using coast function instead of hybrid
-        model[HYBRID_SRF[0] - 1, 1] *= HYBRID_SRF[1]
-    # output
-    vs30 = np.full(gid.shape, nan, dtype=np.float32)
-    stdv = np.copy(vs30)
-    # water points are NaN
-    w_mask = (gid != ID_NODATA) & (gid != 0)
-    vs30[w_mask], stdv[w_mask] = model[gid[w_mask] - 1].T
-    if geol.hybrid:
-        # prevent -Inf warnings
-        slope[np.where((slope == 0) | (slope == s_nodata))] = 1e-9
-        for spec in HYBRID_VS30:
-            if spec[0] == 4 and geol.mod6:
-                continue
-            w_mask = np.where(gid == spec[0])
-            vs30[w_mask] = 10 ** np.interp(np.log10(slope[w_mask]), spec[1], spec[2])
-    if geol.mod6:
-        w_mask = np.where(gid == 4)
-        # explicitly set cdist as float32 which can propagate through
-        # keeping it as uint16 causes overflows with integer multiplication
-        vs30[w_mask] = np.maximum(
-            240,
-            np.minimum(
-                500,
-                240
-                + (500 - 240)
-                * (cdist[w_mask].astype(np.float32) - 8000)
-                / (20000 - 8000),
-            ),
-        )
-    if geol.mod13:
-        w_mask = np.where(gid == 10)
-        vs30[w_mask] = np.maximum(
-            197,
-            np.minimum(
-                500,
-                197
-                + (500 - 197)
-                * (cdist[w_mask].astype(np.float32) - 8000)
-                / (20000 - 8000),
-            ),
-        )
-
-    return vs30, stdv
-
-
-def model_val(ids, model, opts, paths=None, points=None, grid=None):
-    """
-    Return model values for IDs (vs30, stdv).
-    """
-    # collect inputs
-    ids = model_id(points) if ids is None else ids
-    cdist, slope = None, None
-    # coastline distances and slope rough enough to keep as rasters (for now)
-    if opts.mod6 or opts.mod13:
-        cdist = interpolate_raster(points, coast_distance_map(paths, grid))
-    if opts.hybrid:
-        slope = interpolate_raster(points, slope_map(paths, grid))
-    # run
-    return np.column_stack(_hyb_calc(opts, model, ids, slope=slope, cdist=cdist))
-
-
-def model_val_map(paths, grid, model, opts):
-    """
-    Make a tif map of model values.
-    """
-    path = os.path.join(paths.out, "geology.tif")
-    # geology grid
-    gid_tif = model_id_map(paths, grid)
-    gds = gdal.Open(gid_tif, gdal.GA_ReadOnly)
-    g_band = gds.GetRasterBand(1)
-    # coastline distances
-    c_val = None
-    if opts.mod6 or opts.mod13:
-        cdist_tif = coast_distance_map(paths, grid)
-        cds = gdal.Open(cdist_tif, gdal.GA_ReadOnly)
-        c_band = cds.GetRasterBand(1)
-    s_val = None
-    if opts.hybrid:
-        slope_tif = slope_map(paths, grid)
-        sds = gdal.Open(slope_tif, gdal.GA_ReadOnly)
-        s_band = sds.GetRasterBand(1)
-    # output
-    driver = gdal.GetDriverByName("GTiff")
-    ods = driver.Create(
-        path,
-        xsize=grid.nx,
-        ysize=grid.ny,
-        bands=2,
-        eType=gdal.GDT_Float32,
-        options=["COMPRESS=DEFLATE", "BIGTIFF=YES"],
-    )
-    ods.SetGeoTransform(gds.GetGeoTransform())
-    ods.SetProjection(gds.GetProjection())
-    band_vs30 = ods.GetRasterBand(1)
-    band_stdv = ods.GetRasterBand(2)
-    band_vs30.SetDescription("Vs30")
-    band_stdv.SetDescription("Standard Deviation")
-    band_vs30.SetNoDataValue(MODEL_NODATA)
-    band_stdv.SetNoDataValue(MODEL_NODATA)
-
-    # processing chunk/block sizing
-    block = band_vs30.GetBlockSize()
-    nxb = (int)((grid.nx + block[0] - 1) / block[0])
-    nyb = (int)((grid.ny + block[1] - 1) / block[1])
-
-    for x in range(nxb):
-        xoff = x * block[0]
-        # last block may be smaller
-        if x == nxb - 1:
-            block[0] = grid.nx - x * block[0]
-        # reset y block size
-        block_y = block[1]
-
-        for y in range(nyb):
-            yoff = y * block[1]
-            # last block may be smaller
-            if y == nyb - 1:
-                block_y = grid.ny - y * block[1]
-
-            # determine results on block
-            g_val = g_band.ReadAsArray(
-                xoff=xoff, yoff=yoff, win_xsize=block[0], win_ysize=block_y
-            )
-            if opts.hybrid:
-                s_val = s_band.ReadAsArray(
-                    xoff=xoff, yoff=yoff, win_xsize=block[0], win_ysize=block_y
-                )
-            if opts.mod6 or opts.mod13:
-                c_val = c_band.ReadAsArray(
-                    xoff=xoff, yoff=yoff, win_xsize=block[0], win_ysize=block_y
-                ).astype(np.float32)
-            result_vs30, result_stdv = _hyb_calc(
-                opts, model, g_val, slope=s_val, cdist=c_val, nan=MODEL_NODATA
-            )
-            # write results
-            band_vs30.WriteArray(result_vs30, xoff=xoff, yoff=yoff)
-            band_stdv.WriteArray(result_stdv, xoff=xoff, yoff=yoff)
-    # close
-    band_vs30 = None
-    band_stdv = None
-    ods = None
-    return path
diff --git a/vs30/model_terrain.py b/vs30/model_terrain.py
deleted file mode 100644
index ead6a980..00000000
--- a/vs30/model_terrain.py
+++ /dev/null
@@ -1,180 +0,0 @@
-"""
-Terrain model functions based on IwahashiPike terrain categories and YongCA values.
-"""
-
-import os
-from subprocess import call
-
-import numpy as np
-from osgeo import gdal
-
-from vs30.model import ID_NODATA, interpolate_raster, resample_raster
-
-gdal.UseExceptions()
-
-# input location
-MODEL_RASTER = os.path.join(os.path.dirname(__file__), "data", "IwahashiPike.tif")
-# for output model
-MODEL_NODATA = -32767
-
-
-def model_prior():
-    """
-    ID in datasource, name of level
-    01 - Well dissected alpine summits, mountains, etc.
-    02 - Large volcano, high block plateaus, etc.
-    03 - Well dissected, low mountains, etc.
-    04 - Volcanic fan, foot slope of high block plateaus, etc.
-    05 - Dissected plateaus, etc.
-    06 - Basalt lava plain, glaciated plateau, etc.
-    07 - Moderately eroded mountains, lava flow, etc.
-    08 - Desert alluvial slope, volcanic fan, etc.
-    09 - Well eroded plain of weak rocks, etc.
-    10 - Valley, till plain, etc.
-    11 - Eroded plain of weak rocks, etc.
-    12 - Desert plain, delta plain, etc.
-    13 - Incised terrace, etc.
-    14 - Eroded alluvial fan, till plain, etc.
-    15 - Dune, incised terrace, etc.
-    16 - Fluvial plain, alluvial fan, low-lying flat plains, etc.
-    """
-    # position 13 (idx 12) was a guess - Kevin
-    # fmt: off
-    return np.array([[519, 0.3521],
-                     [393, 0.4161],
-                     [547, 0.4695],
-                     [459, 0.3540],
-                     [402, 0.3136],
-                     [345, 0.2800],
-                     [388, 0.4161],
-                     [374, 0.3249],
-                     [497, 0.3516],
-                     [349, 0.2800],
-                     [328, 0.2736],
-                     [297, 0.2931],
-                     [500, 0.5],
-                     [209, 0.1749],
-                     [363, 0.2800],
-                     [246, 0.2206]], dtype=np.float32)
-    # fmt: on
-
-
-def model_posterior_paper():
-    """
-    Posterior model from the paper.
-    """
-    # fmt: off
-    return np.array([[519, 0.5],
-                     [393, 0.5],
-                     [547, 0.5],
-                     [459, 0.5],
-                     [323.504047697085, 0.407430975749267],
-                     [300.639538792274, 0.306594194335118],
-                     [535.786417756242, 0.380788655293195],
-                     [514.970575501527, 0.380788655293195],
-                     [284.470950299338, 0.360555127546399],
-                     [317.368444092771, 0.33166247903554],
-                     [266.870166096091, 0.4],
-                     [297, 0.5],
-                     [216.62368022053, 0.254950975679639],
-                     [241.80869962047, 0.307482445914323],
-                     [198.64481157494, 0.205256370217328],
-                     [202.125866002814, 0.206820130727133]], dtype=np.float32)
-    # fmt: on
-
-
-def model_id(points):
-    """
-    Returns the category ID index for given locations.
-    points: 2D numpy array of NZTM coords
-    """
-    return interpolate_raster(points, MODEL_RASTER)
-
-
-def model_id_map(paths, grid):
-    """
-    Calculate id at map points by resampling / resizing origin id map.
-    """
-    dst = os.path.join(paths.out, "tid.tif")
-    if os.path.isfile(dst):
-        return dst
-    resample_raster(
-        MODEL_RASTER, dst, grid.xmin, grid.xmax, grid.ymin, grid.ymax, grid.dx, grid.dy
-    )
-    r = gdal.Open(dst)
-    b = r.GetRasterBand(1)
-    b.SetDescription("Terrain ID Index")
-    b = None
-    r = None
-    return dst
-
-
-def model_val(ids, model, opts, paths=None, points=None, grid=None):
-    """
-    Return model values for IDs (vs30, stdv).
-    """
-    # allow just giving locations if ids not wanted
-    if ids is None:
-        ids = model_id(points)
-
-    idx = ids != ID_NODATA
-    result = np.full((len(ids), 2), np.nan, dtype=np.float32)
-    result[idx] = model[ids[idx] - 1]
-    return result
-
-
-def model_val_map(paths, grid, model, opts):
-    """
-    Make a tif map of model values.
-    """
-    path = os.path.join(paths.out, "terrain.tif")
-    # terrain IDs for given map spec
-    tid_tif = model_id_map(paths, grid)
-    raster = gdal.Open(tid_tif, gdal.GA_ReadOnly)
-    band = raster.GetRasterBand(1)
-    nodata = band.GetNoDataValue()
-    band = None
-    raster = None
-
-    # model version for indexing (index 0 for NODATA)
-    vs30 = np.append(MODEL_NODATA, model[:, 0]).astype(np.float32)
-    stdv = np.append(MODEL_NODATA, model[:, 1]).astype(np.float32)
-    # string array
-    vs30 = ",".join([f"{x:.8f}" for x in vs30])
-    stdv = ",".join([f"{x:.8f}" for x in stdv])
-    # string expression to pass into calc
-    vs30 = (
-        f"numpy.array([{vs30}], dtype=numpy.float32)[numpy.where(A == {nodata}, 0, A)]"
-    )
-    stdv = (
-        f"numpy.array([{stdv}], dtype=numpy.float32)[numpy.where(A == {nodata}, 0, A)]"
-    )
-
-    # simple formula so just call command line
-    call(
-        [
-            "gdal_calc.py",
-            "-A",
-            tid_tif,
-            "--outfile",
-            path,
-            f"--calc={vs30}",
-            f"--calc={stdv}",
-            f"--NoDataValue={MODEL_NODATA}",
-            "--type=Float32",
-            "--co=COMPRESS=DEFLATE",
-            "--co=BIGTIFF=YES",
-            "--quiet",
-        ]
-    )
-    # name bands
-    raster = gdal.Open(path)
-    bvs30 = raster.GetRasterBand(1)
-    bstdv = raster.GetRasterBand(2)
-    bvs30.SetDescription("Vs30")
-    bstdv.SetDescription("Standard Deviation")
-    bvs30 = None
-    bstdv = None
-    raster = None
-
-    return path
diff --git a/vs30/mvn.py b/vs30/mvn.py
deleted file mode 100644
index a1eb2c1e..00000000
--- a/vs30/mvn.py
+++ /dev/null
@@ -1,280 +0,0 @@
-"""
-MVN (multivariate normal distribution)
-for modifying vs30 values based on proximity to measured values.
-"""
-import os
-import logging
-
-from functools import partial
-from multiprocessing import Pool
-import os
-from shutil import copyfile
-
-import numpy as np
-from osgeo import gdal
-
-
-logger = logging.getLogger(__name__)
-
-
-def _corr_func(distances, model):
-    """
-    Correlation function by distance.
-    phi is 993 for terrain model, 1407 for geology
-    """
-    if model == "geology":
-        phi = 1407
-    elif model == "terrain":
-        phi = 993
-    else:
-        raise ValueError("unknown model")
-    # r code linearly interpolated from logarithmically spaced distances:
-    # d = np.exp(np.linspace(np.log(0.1), np.log(2000e3), 128))
-    # c = 1 / np.e ** (d / phi)
-    # return np.interp(distances, d, c)
-    # minimum distance of 0.1 metres enforced
-    return 1 / np.e ** (np.maximum(0.1, distances) / phi)
-
-
-def _tcrossprod(x):
-    """
-    Matrix cross product (or outer product) from a 1d numpy array.
-    Same functionality as the R function tcrossprod(x) with y = NULL.
-    https://stat.ethz.ch/R-manual/R-devel/library/base/html/crossprod.html
-    """
-    return x[:, np.newaxis] * x
-
-
-def _dists(x):
-    """
-    Euclidean distance from 2d diff array.
-    """
-    # return np.linalg.norm(x, axis=1)
-    # alternative, may be faster
-    return np.sqrt(np.einsum("ij,ij->i", x, x))
-
-
-def _xy2complex(x):
-    """
-    Convert array of 2D coordinates to array of 1D complex numbers.
-    """
-    c = x[:, 0].astype(np.complex64)
-    c.imag += x[:, 1]
-    return c
-
-
-def _dist_mat(x):
-    """
-    Distance matrix between coordinates (complex numbers) or simple values.
-    """
-    return np.abs(x[:, np.newaxis] - x)
-
-
-def _mvn(
-    model_locs,
-    model_vs30,
-    model_stdv,
-    sites,
-    model_name,
-    cov_reduc=1.5,
-    noisy=True,
-    max_dist=10000,
-    max_points=500,
-):
-    """
-    Modify model with observed locations.
-    noisy: whether measurements are noisy True/False
-    max_dist: only consider observed locations within this many metres
-    max_points: limit observed locations to closest N points within max_dist
-    """
-    # cut not-applicable sites to prevent nan propagation
-    sites = sites[~np.isnan(sites[f"{model_name}_vs30"])]
-
-    obs_locs = np.column_stack((sites.easting.values, sites.northing.values))
-    obs_model_stdv = sites[f"{model_name}_stdv"].values
-    obs_residuals = np.log(sites.vs30.values / sites[f"{model_name}_vs30"].values)
-
-    # Wea equation 33, 40, 41
-    if noisy:
-        omega_obs = np.sqrt(
-            obs_model_stdv**2 / (obs_model_stdv**2 + sites.uncertainty.values**2)
-        )
-        obs_residuals *= omega_obs
-
-    # default outputs if no sites closeby
-    pred = np.log(model_vs30)
-    var = model_stdv**2 * _corr_func(0, model_name)
-
-    # model point to observations
-    for i, model_loc in enumerate(model_locs):
-        if np.isnan(model_vs30[i]):
-            # input of nan is always output of nan
-            continue
-        # don't recalculate distances if delta distance is too small anyway
-        # useful when calculating accross grids or close points
-        try:
-            movement = _dists(np.atleast_2d(model_loc - prev_model_loc))[0]
-            if min_dist - movement > max_dist:
-                continue
-        except NameError:
-            pass
-        distances = _dists(obs_locs - model_loc)
-        max_points_i = min(max_points, len(distances)) - 1
-        min_dist, cutoff_dist = np.partition(distances, [0, max_points_i])[
-            [0, max_points_i]
-        ]
-        prev_model_loc = model_loc
-        if min_dist > max_dist:
-            # not close enough to any observed locations
-            continue
-        loc_mask = distances <= min(max_dist, cutoff_dist)
-
-        # distances between interesting points
-        cov_matrix = _dist_mat(_xy2complex(np.vstack((model_loc, obs_locs[loc_mask]))))
-        # correlation
-        cov_matrix = _corr_func(cov_matrix, model_name)
-        # uncertainties
-        cov_matrix *= _tcrossprod(np.insert(obs_model_stdv[loc_mask], 0, model_stdv[i]))
-
-        if noisy:
-            omega = _tcrossprod(np.insert(omega_obs[loc_mask], 0, 1))
-            np.fill_diagonal(omega, 1)
-            cov_matrix *= omega
-
-        # covariance reduction factors
-        if cov_reduc > 0:
-            cov_matrix *= np.exp(
-                -cov_reduc
-                * _dist_mat(
-                    np.insert(
-                        np.log(sites.loc[loc_mask, f"{model_name}_vs30"].values),
-                        0,
-                        pred[i],
-                    )
-                )
-            )
-
-        inv_matrix = np.linalg.inv(cov_matrix[1:, 1:])
-        pred[i] += np.dot(
-            np.dot(cov_matrix[0, 1:], inv_matrix), obs_residuals[loc_mask]
-        )
-        var[i] = cov_matrix[0, 0] - np.dot(
-            np.dot(cov_matrix[0, 1:], inv_matrix), cov_matrix[1:, 0]
-        )
-
-    return model_vs30 * np.exp(pred - np.log(model_vs30)), np.sqrt(var)
-
-
-def mvn_table(table, sites, model_name):
-    """
-    Run MVN over DataFrame. multiprocessing.Pool.map friendly.
-    """
-    # reset indexes for this instance to prevent index errors with split table
-    ix0_table = table.reset_index(drop=True)
-    logger.debug(f"Running MVN on {len(ix0_table)} points for "
-                 f"model {model_name} on process {os.getpid()}")
-    result =  np.column_stack(
-        _mvn(
-            ix0_table[["easting", "northing"]].values,
-            ix0_table[f"{model_name}_vs30"],
-            ix0_table[f"{model_name}_stdv"],
-            sites,
-            model_name,
-        )
-    )
-    logger.debug(f"Completed MVN on {len(ix0_table)} points for "
-                 f"model {model_name} on process {os.getpid()}")
-    return result
-
-
-def _mvn_tiff_worker(tif_path, x_offset, y_offset, x_size, y_size, sites, model_name):
-    """
-    Works on single tif block as split by mvn_tiff.
-    """
-    # load tif
-    tif_ds = gdal.Open(tif_path, gdal.GA_ReadOnly)
-    tif_trans = tif_ds.GetGeoTransform()
-    vs30_band = tif_ds.GetRasterBand(1)
-    stdv_band = tif_ds.GetRasterBand(2)
-    vs30_nd = vs30_band.GetNoDataValue()
-    stdv_nd = stdv_band.GetNoDataValue()
-
-    # read pre-mvn data from tif
-    vs30_val = vs30_band.ReadAsArray(
-        xoff=x_offset, yoff=y_offset, win_xsize=x_size, win_ysize=y_size
-    ).flatten()
-    vs30_val[vs30_val == vs30_nd] = np.nan
-    stdv_val = stdv_band.ReadAsArray(
-        xoff=x_offset, yoff=y_offset, win_xsize=x_size, win_ysize=y_size
-    ).flatten()
-    stdv_val[stdv_val == stdv_nd] = np.nan
-    # coordinates for tif data
-    locs = np.vstack(
-        np.mgrid[
-            tif_trans[0] + (x_offset + 0.5) * tif_trans[1] : tif_trans[0]
-            + (x_offset + 0.5 + x_size) * tif_trans[1] : tif_trans[1],
-            tif_trans[3] + (y_offset + 0.5) * tif_trans[5] : tif_trans[3]
-            + (y_offset + 0.5 + y_size) * tif_trans[5] : tif_trans[5],
-        ].T
-    ).astype(np.float32)
-
-    # close tif
-    vs30_band = None
-    stdv_band = None
-    tif_ds = None
-
-    # calculate mvn
-    vs30_mvn, stdv_mvn = _mvn(locs, vs30_val, stdv_val, sites, model_name)
-    return (
-        x_offset,
-        y_offset,
-        vs30_mvn.reshape(y_size, x_size),
-        stdv_mvn.reshape(y_size, x_size),
-    )
-
-
-def mvn_tiff(out_dir, model_name, sites, nproc=1):
-    """
-    Run MVN over GeoTIFF.
-    """
-    # mvn based on original model, modified if in proximity to measured sites
-    in_tiff = os.path.join(out_dir, f"{model_name}.tif")
-    out_tiff = os.path.join(out_dir, f"{model_name}_mvn.tif")
-    copyfile(in_tiff, out_tiff)
-    tif_ds = gdal.Open(out_tiff, gdal.GA_Update)
-    nx = tif_ds.RasterXSize
-    ny = tif_ds.RasterYSize
-    vs30_band = tif_ds.GetRasterBand(1)
-    stdv_band = tif_ds.GetRasterBand(2)
-
-    # processing chunk/block sizing
-    # usually just lines of nx=nx, ny=1 which is a good size for multiprocessing
-    block = vs30_band.GetBlockSize()
-    nxb = (int)((nx + block[0] - 1) / block[0])
-    nyb = (int)((ny + block[1] - 1) / block[1])
-
-    job_args = []
-    for x in range(nxb):
-        xoff = x * block[0]
-        # last block may be smaller
-        if x == nxb - 1:
-            block[0] = nx - x * block[0]
-        # reset y block size
-        block_y = block[1]
-
-        for y in range(nyb):
-            yoff = y * block[1]
-            # last block may be smaller
-            if y == nyb - 1:
-                block_y = ny - y * block[1]
-
-            job_args.append((in_tiff, xoff, yoff, block[0], block_y, sites, model_name))
-
-    with Pool(nproc) as pool:
-        results = pool.map(_mvn_tiff_worker, job_args)
-
-    for xoff, yoff, vs30_mvn, stdv_mvn in results:
-        vs30_band.WriteArray(vs30_mvn, xoff=xoff, yoff=yoff)
-        stdv_band.WriteArray(stdv_mvn, xoff=xoff, yoff=yoff)
-
-    return out_tiff
diff --git a/vs30/parallel.py b/vs30/parallel.py
new file mode 100644
index 00000000..51817497
--- /dev/null
+++ b/vs30/parallel.py
@@ -0,0 +1,613 @@
+"""Multiprocessing support for parallel spatial adjustment."""
+
+import contextlib
+import multiprocessing as mp
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import threadpoolctl
+from qcore import coordinates
+from tqdm import tqdm
+
+from vs30 import category, constants, raster, spatial, utils
+
+
+@contextlib.contextmanager
+def single_threaded_blas():
+    """Restrict BLAS to single-threaded operation to prevent oversubscription during multiprocessing."""
+    with threadpoolctl.threadpool_limits(limits=1, user_api="blas"):
+        yield
+
+
+def resolve_n_proc(n_proc: int | None) -> int:
+    """
+    Convert user input to actual process count.
+
+    Parameters
+    ----------
+    n_proc : int or None
+        User-specified number of processes.
+        None or 1 = single-threaded
+        -1 = use all available CPU cores
+        > 1 = use that many processes
+
+    Returns
+    -------
+    int
+        Actual number of processes to use (always >= 1)
+
+    Raises
+    ------
+    ValueError
+        If n_proc is 0 or less than -1
+    """
+    if n_proc is None or n_proc == 1:
+        return 1
+    if n_proc == -1:
+        return mp.cpu_count()
+    if n_proc < -1 or n_proc == 0:
+        raise ValueError(f"n_proc must be -1, 1, or > 1, got {n_proc}")
+    return min(n_proc, mp.cpu_count())
+
+
+# Use spawn context to avoid GDAL fork issues
+# GDAL is not fork-safe; using spawn starts fresh processes without inheriting
+# the parent's GDAL state, which prevents deadlocks
+_spawn_context = mp.get_context("spawn")
+
+
+def process_geology_at_points(
+    points: np.ndarray,
+    model_df: pd.DataFrame,
+    observations_df: pd.DataFrame,
+    coast_distance_raster: Path | None = None,
+    noisy: bool = False,
+    progress_bar: tqdm | None = None,
+) -> tuple[
+    np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray
+]:
+    """
+    Process geology model at points, including hybrid modifications and spatial adjustment.
+
+    This function encapsulates the full geology processing pipeline:
+    1. Get initial Vs30 values from categorical model
+    2. Apply hybrid modifications (slope and coastal distance) if coast raster provided
+    3. Apply spatial adjustment using observations
+
+    Parameters
+    ----------
+    points : ndarray
+        Array of shape (n_points, 2) with (easting, northing) coordinates.
+    model_df : DataFrame
+        Categorical geology model with Vs30 mean and standard deviation per category.
+    observations_df : DataFrame
+        Observation data with columns: easting, northing, vs30, uncertainty.
+    coast_distance_raster : Path, optional
+        Path to coastal distance raster. If None, hybrid modifications are skipped.
+    noisy : bool
+        Whether to apply noise weighting in spatial adjustment.
+    progress_bar : tqdm, optional
+        External progress bar to update per point during spatial adjustment.
+
+    Returns
+    -------
+    geol_ids : ndarray
+        Geology category IDs at each point.
+    geol_vs30 : ndarray
+        Initial geology Vs30 values (before hybrid mods).
+    geol_stdv : ndarray
+        Initial geology standard deviation (before hybrid mods).
+    geol_vs30_hybrid : ndarray
+        Geology Vs30 after hybrid modifications.
+    geol_stdv_hybrid : ndarray
+        Geology standard deviation after hybrid modifications.
+    geol_mvn_vs30 : ndarray
+        Final geology Vs30 after spatial adjustment.
+    geol_mvn_stdv : ndarray
+        Final geology standard deviation after spatial adjustment.
+    """
+    # Assign geology category IDs to points
+    geol_ids = category.assign_to_category_geology(points)
+
+    # Get initial Vs30 values from categorical model
+    geol_vs30_df = category.get_vs30_for_ids(geol_ids, model_df)
+
+    # Apply hybrid modifications (slope and coastal distance)
+    if coast_distance_raster is not None and coast_distance_raster.exists():
+        geol_vs30_hybrid, geol_stdv_hybrid = (
+            raster.apply_hybrid_modifications_at_points(
+                points,
+                geol_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values,
+                geol_vs30_df[constants.COL_CATEGORY_VS30_STDV].values,
+                geol_ids,
+                slope_raster_path=None,  # Uses default from constants
+                coast_distance_raster_path=coast_distance_raster,
+            )
+        )
+    else:
+        geol_vs30_hybrid = geol_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values
+        geol_stdv_hybrid = geol_vs30_df[constants.COL_CATEGORY_VS30_STDV].values
+
+    # Apply spatial adjustment if observations are available
+    if len(observations_df) > 0:
+        obs_locs = observations_df[
+            [constants.COL_EASTING, constants.COL_NORTHING]
+        ].values
+        obs_geol_ids = category.assign_to_category_geology(obs_locs)
+        obs_geol_vs30_df = category.get_vs30_for_ids(obs_geol_ids, model_df)
+        geol_mvn_vs30, geol_mvn_stdv = spatial.compute_spatial_adjustment_at_points(
+            points=points,
+            model_vs30=geol_vs30_hybrid,
+            model_stdv=geol_stdv_hybrid,
+            obs_locations=obs_locs,
+            obs_vs30=observations_df[constants.COL_VS30].values,
+            obs_model_vs30=obs_geol_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values,
+            obs_model_stdv=obs_geol_vs30_df[constants.COL_CATEGORY_VS30_STDV].values,
+            obs_uncertainty=observations_df[constants.COL_UNCERTAINTY].values,
+            model_type=constants.ModelType.GEOLOGY,
+            noisy=noisy,
+            progress_bar=progress_bar,
+        )
+    else:
+        geol_mvn_vs30 = geol_vs30_hybrid
+        geol_mvn_stdv = geol_stdv_hybrid
+
+    return (
+        geol_ids,
+        geol_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values,
+        geol_vs30_df[constants.COL_CATEGORY_VS30_STDV].values,
+        geol_vs30_hybrid,
+        geol_stdv_hybrid,
+        geol_mvn_vs30,
+        geol_mvn_stdv,
+    )
+
+
+def process_terrain_at_points(
+    points: np.ndarray,
+    model_df: pd.DataFrame,
+    observations_df: pd.DataFrame,
+    noisy: bool = False,
+    progress_bar: tqdm | None = None,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Process terrain model at points, including spatial adjustment.
+
+    This function encapsulates the full terrain processing pipeline:
+    1. Get initial Vs30 values from categorical model
+    2. Apply spatial adjustment using observations (no hybrid modifications for terrain)
+
+    Parameters
+    ----------
+    points : ndarray
+        Array of shape (n_points, 2) with (easting, northing) coordinates.
+    model_df : DataFrame
+        Categorical terrain model with Vs30 mean and standard deviation per category.
+    observations_df : DataFrame
+        Observation data with columns: easting, northing, vs30, uncertainty.
+    noisy : bool
+        Whether to apply noise weighting in spatial adjustment.
+    progress_bar : tqdm, optional
+        External progress bar to update per point during spatial adjustment.
+
+    Returns
+    -------
+    terr_ids : ndarray
+        Terrain category IDs at each point.
+    terr_vs30 : ndarray
+        Initial terrain Vs30 values.
+    terr_stdv : ndarray
+        Initial terrain standard deviation.
+    terr_mvn_vs30 : ndarray
+        Final terrain Vs30 after spatial adjustment.
+    terr_mvn_stdv : ndarray
+        Final terrain standard deviation after spatial adjustment.
+    """
+    # Assign terrain category IDs to points
+    terr_ids = category.assign_to_category_terrain(points)
+
+    # Get initial Vs30 values from categorical model
+    terr_vs30_df = category.get_vs30_for_ids(terr_ids, model_df)
+
+    # Apply spatial adjustment if observations are available
+    if len(observations_df) > 0:
+        obs_locs = observations_df[
+            [constants.COL_EASTING, constants.COL_NORTHING]
+        ].values
+        obs_terr_ids = category.assign_to_category_terrain(obs_locs)
+        obs_terr_vs30_df = category.get_vs30_for_ids(obs_terr_ids, model_df)
+        terr_mvn_vs30, terr_mvn_stdv = spatial.compute_spatial_adjustment_at_points(
+            points=points,
+            model_vs30=terr_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values,
+            model_stdv=terr_vs30_df[constants.COL_CATEGORY_VS30_STDV].values,
+            obs_locations=obs_locs,
+            obs_vs30=observations_df[constants.COL_VS30].values,
+            obs_model_vs30=obs_terr_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values,
+            obs_model_stdv=obs_terr_vs30_df[constants.COL_CATEGORY_VS30_STDV].values,
+            obs_uncertainty=observations_df[constants.COL_UNCERTAINTY].values,
+            model_type=constants.ModelType.TERRAIN,
+            noisy=noisy,
+            progress_bar=progress_bar,
+        )
+    else:
+        terr_mvn_vs30 = terr_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values
+        terr_mvn_stdv = terr_vs30_df[constants.COL_CATEGORY_VS30_STDV].values
+
+    return (
+        terr_ids,
+        terr_vs30_df[constants.COL_CATEGORY_VS30_MEAN].values,
+        terr_vs30_df[constants.COL_CATEGORY_VS30_STDV].values,
+        terr_mvn_vs30,
+        terr_mvn_stdv,
+    )
+
+
+@dataclass
+class LocationsChunkConfig:
+    """
+    Configuration parameters for processing a locations chunk.
+
+    Attributes
+    ----------
+    lon_column : str
+        Name of longitude column in input CSV.
+    lat_column : str
+        Name of latitude column in input CSV.
+    include_intermediate : bool
+        Whether to include intermediate values in output.
+    combination_method : str or float
+        Method for combining geology and terrain models.
+    coast_distance_raster : Path or None
+        Path to coastal distance raster for hybrid modifications.
+    noisy : bool
+        Whether to apply noise weighting in spatial adjustment.
+    """
+
+    lon_column: str
+    lat_column: str
+    include_intermediate: bool
+    combination_method: str | float
+    coast_distance_raster: Path | None
+    noisy: bool
+
+
+def process_locations_chunk(
+    args: tuple,
+) -> tuple[int, pd.DataFrame]:  # pragma: no cover
+    """
+    Worker function: process a chunk of locations through the full pipeline.
+
+    This function runs in a separate process and processes a subset of
+    locations through the complete VS30 pipeline (geology + terrain + combine).
+
+    Note: This function is excluded from coverage because it runs in a
+    spawned subprocess which cannot be tracked by pytest-cov.
+
+    Parameters
+    ----------
+    args : tuple
+        (chunk_df, chunk_id, observations_df, geol_model_df, terr_model_df, config)
+
+    Returns
+    -------
+    tuple
+        (chunk_id, result_df) where result_df has all computed columns
+    """
+    (
+        chunk_df,
+        chunk_id,
+        observations_df,
+        geol_model_df,
+        terr_model_df,
+        config,
+    ) = args
+
+    # Make a copy to avoid modifying the original
+    chunk_df = chunk_df.copy()
+
+    # Convert coordinates to NZTM
+    nztm_coords = coordinates.wgs_depth_to_nztm(
+        chunk_df[[config.lat_column, config.lon_column]].values
+    )
+    chunk_df[constants.COL_EASTING] = nztm_coords[:, 1]
+    chunk_df[constants.COL_NORTHING] = nztm_coords[:, 0]
+    points = nztm_coords[:, ::-1]
+
+    # Process geology model
+    (
+        geol_ids,
+        geol_vs30,
+        geol_stdv,
+        geol_vs30_hybrid,
+        geol_stdv_hybrid,
+        geol_mvn_vs30,
+        geol_mvn_stdv,
+    ) = process_geology_at_points(
+        points,
+        geol_model_df,
+        observations_df,
+        config.coast_distance_raster,
+        config.noisy,
+    )
+
+    chunk_df[constants.COL_GEOLOGY_ID] = geol_ids
+    if config.include_intermediate:
+        chunk_df[constants.COL_GEOLOGY_VS30] = geol_vs30
+        chunk_df[constants.COL_GEOLOGY_STDV] = geol_stdv
+        chunk_df[constants.COL_GEOLOGY_VS30_HYBRID] = geol_vs30_hybrid
+        chunk_df[constants.COL_GEOLOGY_STDV_HYBRID] = geol_stdv_hybrid
+    chunk_df[constants.COL_GEOLOGY_MVN_VS30] = geol_mvn_vs30
+    chunk_df[constants.COL_GEOLOGY_MVN_STDV] = geol_mvn_stdv
+
+    # Process terrain model
+    (
+        terr_ids,
+        terr_vs30,
+        terr_stdv,
+        terr_mvn_vs30,
+        terr_mvn_stdv,
+    ) = process_terrain_at_points(points, terr_model_df, observations_df, config.noisy)
+
+    chunk_df[constants.COL_TERRAIN_ID] = terr_ids
+    if config.include_intermediate:
+        chunk_df[constants.COL_TERRAIN_VS30] = terr_vs30
+        chunk_df[constants.COL_TERRAIN_STDV] = terr_stdv
+    chunk_df[constants.COL_TERRAIN_MVN_VS30] = terr_mvn_vs30
+    chunk_df[constants.COL_TERRAIN_MVN_STDV] = terr_mvn_stdv
+
+    # Combine models
+    combined_vs30, combined_stdv = utils.combine_vs30_models(
+        geol_mvn_vs30,
+        geol_mvn_stdv,
+        terr_mvn_vs30,
+        terr_mvn_stdv,
+        config.combination_method,
+    )
+
+    chunk_df[constants.COL_VS30] = combined_vs30
+    chunk_df[constants.COL_COMBINED_STDV] = combined_stdv
+
+    return chunk_id, chunk_df
+
+
+def process_pixels_chunk(
+    args: tuple,
+) -> tuple[int, list[spatial.SpatialAdjustmentResult]]:  # pragma: no cover
+    """
+    Worker function: compute spatial adjustments for a chunk of affected pixels.
+
+    This function runs in a separate process and computes spatial adjustments
+    for a subset of affected pixels.
+
+    Note: This function is excluded from coverage because it runs in a
+    spawned subprocess which cannot be tracked by pytest-cov.
+
+    Parameters
+    ----------
+    args : tuple
+        (pixel_indices, chunk_id, pixel_data_dict, obs_data_dict, config_params)
+
+    Returns
+    -------
+    tuple
+        (chunk_id, list of spatial.SpatialAdjustmentResult)
+    """
+    pixel_indices, chunk_id, pixel_data_dict, obs_data_dict, config_params = args
+
+    # Reconstruct ObservationData from dict (dataclasses can't always be pickled cleanly)
+    obs_data = spatial.ObservationData(
+        locations=obs_data_dict[constants.KEY_LOCATIONS],
+        vs30=obs_data_dict[constants.COL_VS30],
+        model_vs30=obs_data_dict[constants.KEY_MODEL_VS30],
+        model_stdv=obs_data_dict[constants.KEY_MODEL_STDV],
+        residuals=obs_data_dict[constants.KEY_RESIDUALS],
+        omega=obs_data_dict[constants.KEY_OMEGA],
+        uncertainty=obs_data_dict[constants.COL_UNCERTAINTY],
+    )
+
+    updates = []
+    for idx in pixel_indices:
+        # Get pixel data from the prepared dict
+        pixel_info = pixel_data_dict[idx]
+        pixel = spatial.PixelData(
+            location=pixel_info[constants.KEY_LOCATION],
+            vs30=pixel_info[constants.COL_VS30],
+            stdv=pixel_info[constants.KEY_STDV],
+            index=pixel_info[constants.KEY_INDEX],
+        )
+
+        update = spatial.compute_spatial_adjustment_for_pixel(
+            pixel,
+            obs_data,
+            config_params[constants.KEY_MODEL_TYPE],
+            max_dist_m=config_params[constants.KEY_MAX_DIST_M],
+            max_points=config_params[constants.KEY_MAX_POINTS],
+            noisy=config_params[constants.KEY_NOISY],
+            cov_reduc=config_params[constants.KEY_COV_REDUC],
+        )
+
+        if update is not None:
+            updates.append(update)
+
+    return chunk_id, updates
+
+
+def run_parallel_locations(
+    locations_df: pd.DataFrame,
+    observations_df: pd.DataFrame,
+    geol_model_df: pd.DataFrame,
+    terr_model_df: pd.DataFrame,
+    config: LocationsChunkConfig,
+    n_proc: int,
+) -> pd.DataFrame:
+    """
+    Process locations in parallel.
+
+    Divides the locations DataFrame into chunks and processes each chunk
+    in a separate process using the full VS30 pipeline.
+
+    Parameters
+    ----------
+    locations_df : DataFrame
+        Input locations with lon/lat columns (not yet converted to NZTM)
+    observations_df : DataFrame
+        Observation data for spatial adjustment (must have easting, northing, vs30, uncertainty)
+    geol_model_df : DataFrame
+        Geology categorical model
+    terr_model_df : DataFrame
+        Terrain categorical model
+    config : LocationsChunkConfig
+        Configuration parameters for processing
+    n_proc : int
+        Number of processes to use (must be > 1)
+
+    Returns
+    -------
+    DataFrame
+        Results with vs30, stdv, and intermediate columns (if requested)
+    """
+    # Split into many small chunks for smooth progress bar updates.
+    # pool.imap distributes chunks to n_proc workers automatically.
+    n_chunks = min(len(locations_df), constants.N_PROGRESS_CHUNKS)
+    split_indices = np.array_split(range(len(locations_df)), n_chunks)
+    chunk_args = [
+        (
+            locations_df.iloc[idx].reset_index(drop=True),
+            i,
+            observations_df,
+            geol_model_df,
+            terr_model_df,
+            config,
+        )
+        for i, idx in enumerate(split_indices)
+        if len(idx) > 0
+    ]
+
+    # Process in parallel using spawn context (avoids GDAL fork issues)
+    # Use single_threaded_blas to prevent BLAS oversubscription
+    with single_threaded_blas():
+        with _spawn_context.Pool(processes=n_proc) as pool:
+            results = []
+            with tqdm(total=len(locations_df), unit="point") as pbar:
+                for chunk_id, result_df in pool.imap(
+                    process_locations_chunk, chunk_args
+                ):
+                    results.append((chunk_id, result_df))
+                    pbar.update(len(result_df))
+
+    # Merge: concatenate in order
+    results.sort(key=lambda x: x[0])
+    return pd.concat([r[1] for r in results], ignore_index=True)
+
+
+def run_parallel_spatial_fit(
+    affected_flat_indices: np.ndarray,
+    raster_data,  # RasterData - avoid import cycle
+    obs_data: spatial.ObservationData,
+    model_type: str,
+    max_dist_m: float,
+    max_points: int,
+    noisy: bool,
+    cov_reduc: float,
+    n_proc: int,
+) -> list[spatial.SpatialAdjustmentResult]:
+    """
+    Compute spatial adjustments for affected pixels in parallel.
+
+    Divides the affected pixels into chunks and processes each chunk
+    in a separate process.
+
+    Parameters
+    ----------
+    affected_flat_indices : ndarray
+        Flat indices of affected pixels in the raster
+    raster_data : RasterData
+        Raster data object with vs30, stdv, and coordinate info
+    obs_data : ObservationData
+        Observation data for spatial adjustment
+    model_type : constants.ModelType
+        Model type (ModelType.GEOLOGY or ModelType.TERRAIN)
+    max_dist_m : float
+        Maximum distance for considering observations
+    max_points : int
+        Maximum number of observations per pixel
+    noisy : bool
+        Whether to apply noise weighting
+    cov_reduc : float
+        Covariance reduction factor
+    n_proc : int
+        Number of processes to use (must be > 1)
+
+    Returns
+    -------
+    list[spatial.SpatialAdjustmentResult]
+        Updates for all affected pixels
+    """
+    # Prepare pixel data as a dict (for pickling)
+    grid_locs = raster_data.get_coordinates()
+    pixel_data_dict = {}
+    for i, flat_idx in enumerate(affected_flat_indices):
+        # Map flat index to valid index for coordinates
+        valid_idx = np.searchsorted(raster_data.valid_flat_indices, flat_idx)
+        if valid_idx < len(grid_locs):
+            pixel_data_dict[i] = {
+                constants.KEY_LOCATION: grid_locs[valid_idx],
+                constants.COL_VS30: float(raster_data.vs30.flat[flat_idx]),
+                constants.KEY_STDV: float(raster_data.stdv.flat[flat_idx]),
+                constants.KEY_INDEX: int(flat_idx),
+            }
+
+    # Convert ObservationData to dict for pickling
+    obs_data_dict = {
+        constants.KEY_LOCATIONS: obs_data.locations,
+        constants.COL_VS30: obs_data.vs30,
+        constants.KEY_MODEL_VS30: obs_data.model_vs30,
+        constants.KEY_MODEL_STDV: obs_data.model_stdv,
+        constants.KEY_RESIDUALS: obs_data.residuals,
+        constants.KEY_OMEGA: obs_data.omega,
+        constants.COL_UNCERTAINTY: obs_data.uncertainty,
+    }
+
+    # Config params
+    config_params = {
+        constants.KEY_MODEL_TYPE: model_type,
+        constants.KEY_MAX_DIST_M: max_dist_m,
+        constants.KEY_MAX_POINTS: max_points,
+        constants.KEY_NOISY: noisy,
+        constants.KEY_COV_REDUC: cov_reduc,
+    }
+
+    # Split into many small chunks for smooth progress bar updates.
+    # pool.imap distributes chunks to n_proc workers automatically.
+    pixel_indices_list = list(range(len(affected_flat_indices)))
+    n_chunks = min(len(pixel_indices_list), constants.N_PROGRESS_CHUNKS)
+    chunks = np.array_split(pixel_indices_list, n_chunks)
+    chunk_args = [
+        (list(chunk), i, pixel_data_dict, obs_data_dict, config_params)
+        for i, chunk in enumerate(chunks)
+        if len(chunk) > 0
+    ]
+
+    actual_n_proc = min(n_proc, len(chunk_args))
+
+    # Process in parallel using spawn context (avoids GDAL fork issues)
+    # Use single_threaded_blas to prevent BLAS oversubscription
+    with single_threaded_blas():
+        with _spawn_context.Pool(processes=actual_n_proc) as pool:
+            results = []
+            label = str(model_type).capitalize()
+            with tqdm(total=len(pixel_indices_list), desc=f"{label}: spatial adjustment", unit="pixel") as pbar:
+                for chunk_id, chunk_updates in pool.imap(
+                    process_pixels_chunk, chunk_args
+                ):
+                    results.append((chunk_id, chunk_updates))
+                    pbar.update(len(chunks[chunk_id]))
+
+    # Merge: concatenate update lists (order does not matter; each update carries its pixel_index)
+    all_updates = []
+    for _, chunk_updates in results:
+        all_updates.extend(chunk_updates)
+
+    return all_updates
diff --git a/vs30/params.py b/vs30/params.py
deleted file mode 100644
index a41c4899..00000000
--- a/vs30/params.py
+++ /dev/null
@@ -1,251 +0,0 @@
-"""
-Parameters used throughout the vs30 package.
-Also parses command line arguments for vs30calc.py.
-"""
-
-from argparse import ArgumentParser
-from dataclasses import dataclass
-from multiprocessing import cpu_count
-
-
-@dataclass
-class PathsParams:
-    """
-    Locations to resources, ouputs and anything else.
-    """
-
-    out: str = "./vs30map"
-    overwrite: bool = False
-
-
-@dataclass
-class SitesParams:
-    """
-    Parameters for measured sites.
-    """
-
-    choices = ["original", "cpt"]
-
-    source: str = "original"
-
-
-@dataclass
-class GridParams:
-    """
-    Grid parameters for rasterization.
-    """
-
-    xmin: int = 1060050
-    xmax: int = 2120050
-    dx: int = 100
-    ymin: int = 4730050
-    ymax: int = 6250050
-    dy: int = 100
-
-    def __post_init__(self):
-        self.update()
-
-    def update(self):
-        """
-        Updates number of columns and rows given other parameters.
-        """
-        self.nx = round((self.xmax - self.xmin) / self.dx)
-        self.ny = round((self.ymax - self.ymin) / self.dy)
-
-
-@dataclass
-class LLFileParams:
-    """
-    Parameters for loading long/lat locations of interest file.
-    """
-
-    ll_path: str
-    lon_col_ix: int = 0
-    lat_col_ix: int = 1
-    col_sep: str = " "
-    skip_rows: int = 0
-
-
-@dataclass
-class GeologyParams:
-    """
-    Geology model parameters.
-    """
-
-    letter = "g"
-    name = "geology"
-    choices = ["off", "prior", "posterior", "posterior_paper"]
-
-    update: str = "posterior_paper"
-    hybrid: bool = True
-    mod6: bool = True
-    mod13: bool = True
-
-
-@dataclass
-class TerrainParams:
-    """
-    Terrain model parameters.
-    """
-
-    letter = "t"
-    name = "terrain"
-    choices = ["off", "prior", "posterior", "posterior_paper"]
-
-    update: str = "posterior_paper"
-
-
-@dataclass
-class CombinationParams:
-    """
-    Model combination parameters.
-    """
-
-    stdv_weight: bool = False
-    k: float = -1.0
-
-
-def load_args():
-    """
-    Load arguments from command line.
-    """
-    parser = ArgumentParser()
-    arg = parser.add_argument
-    arg("--nproc", help="number of processes to use", type=int, default=cpu_count())
-    arg(
-        "--out",
-        help="output location",
-        type=type(PathsParams.out),
-        default=PathsParams.out,
-    )
-    arg("--overwrite", help="overwrite output location", action="store_true")
-    # point options
-    arg(
-        "--ll-path",
-        help="locations from file instead of running over a grid, space separated longitude latitude columns",
-    )
-    arg(
-        "--lon-col-ix",
-        help="ll file column containing longitude",
-        type=type(LLFileParams.lon_col_ix),
-        default=LLFileParams.lon_col_ix,
-    )
-    arg(
-        "--lat-col-ix",
-        help="ll file column containing latitude",
-        type=type(LLFileParams.lat_col_ix),
-        default=LLFileParams.lat_col_ix,
-    )
-    arg(
-        "--col-sep",
-        help="ll file column separator",
-        type=type(LLFileParams.col_sep),
-        default=LLFileParams.col_sep,
-    )
-    arg(
-        "--skip-rows",
-        help="ll file rows to skip",
-        type=type(LLFileParams.skip_rows),
-        default=LLFileParams.skip_rows,
-    )
-    # grid options (used with points as well)
-    arg(
-        "--xmin",
-        help="minimum easting",
-        type=type(GridParams.xmin),
-        default=GridParams.xmin,
-    )
-    arg(
-        "--xmax",
-        help="maximum easting",
-        type=type(GridParams.xmax),
-        default=GridParams.xmax,
-    )
-    arg(
-        "--dx",
-        help="horizontal spacing",
-        type=type(GridParams.dx),
-        default=GridParams.dx,
-    )
-    arg(
-        "--ymin",
-        help="minimum northing",
-        type=type(GridParams.ymin),
-        default=GridParams.ymin,
-    )
-    arg(
-        "--ymax",
-        help="maximum northing",
-        type=type(GridParams.ymax),
-        default=GridParams.ymax,
-    )
-    arg(
-        "--dy", help="vertical spacing", type=type(GridParams.dy), default=GridParams.dy
-    )
-    # model update options
-    arg(
-        "--gupdate",
-        help="geology model updating",
-        choices=GeologyParams.choices,
-        default=GeologyParams.update,
-    )
-    arg(
-        "--tupdate",
-        help="terrain model updating",
-        choices=TerrainParams.choices,
-        default=TerrainParams.update,
-    )
-    # geology model has a few parametric processing options
-    parser.add_argument("--no-g6mod", dest="g6mod", action="store_false")
-    parser.add_argument("--no-g13mod", dest="g13mod", action="store_false")
-    parser.add_argument("--no-ghybrid", dest="ghybrid", action="store_false")
-    # combination arguments
-    parser.add_argument(
-        "--stdv-weight",
-        help="use standard deviation for model combination",
-        action="store_true",
-    )
-    parser.add_argument(
-        "--k",
-        help="k factor for stdv based weight combination",
-        type=type(CombinationParams.k),
-        default=CombinationParams.k,
-    )
-    # measured site arguments
-    arg(
-        "--source",
-        help="measured site dataset",
-        choices=SitesParams.choices,
-        default=SitesParams.source,
-    )
-
-    # process arguments
-    args = parser.parse_args()
-    # argument sets
-    p_paths = PathsParams(out=args.out, overwrite=args.overwrite)
-    p_sites = SitesParams(source=args.source)
-    p_grid = GridParams(
-        xmin=args.xmin,
-        xmax=args.xmax,
-        dx=args.dx,
-        ymin=args.ymin,
-        ymax=args.ymax,
-        dy=args.dy,
-    )
-    if args.ll_path is not None:
-        p_ll = LLFileParams(
-            args.ll_path,
-            lon_col_ix=args.lon_col_ix,
-            lat_col_ix=args.lat_col_ix,
-            col_sep=args.col_sep,
-            skip_rows=args.skip_rows,
-        )
-    else:
-        p_ll = None
-    p_geol = GeologyParams(
-        hybrid=args.ghybrid, mod6=args.g6mod, mod13=args.g13mod, update=args.gupdate
-    )
-    p_terr = TerrainParams(update=args.tupdate)
-    p_comb = CombinationParams(stdv_weight=args.stdv_weight, k=args.k)
-
-    return p_paths, p_sites, p_grid, p_ll, p_geol, p_terr, p_comb, args.nproc
diff --git a/vs30/pipeline.py b/vs30/pipeline.py
new file mode 100644
index 00000000..81bc37ef
--- /dev/null
+++ b/vs30/pipeline.py
@@ -0,0 +1,1153 @@
+"""
+Pipeline functions for generating Vs30 models.
+"""
+
+import logging
+import shutil
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import rasterio
+from qcore import coordinates
+from tqdm import tqdm
+
+from vs30 import category, constants, parallel, raster, spatial, utils
+from vs30 import config as config_module
+
+logger = logging.getLogger(__name__)
+
+
+def update_categorical_vs30_models(
+    categorical_model_csv: Path,
+    output_dir: Path,
+    model_type: constants.ModelType,
+    clustered_observations_csv: Path | None = None,
+    independent_observations_csv: Path | None = None,
+    nproc: int = 1,
+) -> None:
+    """
+    Update categorical model values using Bayesian updates and save to CSV files.
+
+    Loads observations and categorical model values, applies Bayesian updates to the
+    categorical model values (mean and standard deviation per category), and writes
+    the updated values back to CSV files.
+
+    Can process clustered observations (with spatial clustering) and/or independent
+    observations (without clustering). If both are provided, clustered observations
+    are processed first, and the resulting posterior is used as the prior for
+    independent observations.
+
+    The order (clustered first, then independent) is scientifically motivated:
+    clustered observations (e.g., CPT data) may have spatial sampling biases from
+    geotechnical investigations, so clustering corrects for over-weighting dense
+    samples. Independent observations (e.g., direct Vs30 measurements) are typically
+    higher-quality and more representative, so they refine the bias-corrected model
+    from clustered data.
+
+    Parameters
+    ----------
+    categorical_model_csv : Path
+        Path to CSV file with categorical Vs30 mean and standard deviation values
+        (e.g., geology_model_prior_mean_and_standard_deviation.csv).
+    output_dir : Path
+        Path to output directory. Will be created if it does not exist.
+    model_type : str
+        Model type: either 'geology' or 'terrain'.
+    clustered_observations_csv : Path, optional
+        Path to CSV file with clustered observations (e.g., measured_vs30_cpt.csv).
+        These will be processed with spatial clustering.
+    independent_observations_csv : Path, optional
+        Path to CSV file with independent observations
+        (e.g., measured_vs30_independent_observations.csv).
+        These will be processed without clustering.
+    nproc : int, optional
+        Number of processes for DBSCAN clustering. Use -1 for all available cores.
+
+    Raises
+    ------
+    ValueError
+        If neither observations CSV is provided, if model_type is invalid, or if
+        required CSV columns are missing.
+    """
+    if clustered_observations_csv is None and independent_observations_csv is None:
+        raise ValueError(
+            "At least one of clustered_observations_csv or "
+            "independent_observations_csv must be provided"
+        )
+
+    if model_type not in constants.ModelType:
+        raise ValueError(f"model_type must be a valid ModelType, got '{model_type}'")
+
+    logger.info(f"Model type: {model_type}")
+    logger.info(f"Loading categorical model from: {categorical_model_csv}")
+
+    categorical_model_df = pd.read_csv(categorical_model_csv, skipinitialspace=True)
+
+    # Drop rows with placeholder values for excluded categories (e.g., water)
+    categorical_model_df = categorical_model_df[
+        categorical_model_df[constants.COL_MEAN] != constants.NODATA_VALUE
+    ]
+
+    utils.validate_csv_columns(
+        categorical_model_df,
+        [constants.COL_MEAN, constants.COL_STDV],
+        "Categorical model CSV",
+    )
+
+    # Current prior (will be updated as we process observations)
+    current_prior_df = categorical_model_df.copy()
+
+    # Load clustered observations if provided
+    clustered_observations_df = None
+    if clustered_observations_csv is not None:
+        logger.info(
+            f"Loading clustered observations from: {clustered_observations_csv}"
+        )
+        clustered_observations_df = pd.read_csv(
+            clustered_observations_csv, skipinitialspace=True
+        )
+
+        utils.validate_csv_columns(
+            clustered_observations_df,
+            constants.REQUIRED_OBSERVATION_COLUMNS_BASIC,
+            "Clustered observations CSV",
+        )
+
+        logger.info(f"Loaded {len(clustered_observations_df)} clustered observations")
+
+        # Assign category IDs
+        obs_locs = clustered_observations_df[
+            [constants.COL_EASTING, constants.COL_NORTHING]
+        ].values
+        if model_type == constants.ModelType.GEOLOGY:
+            model_ids = category.assign_to_category_geology(obs_locs)
+        else:  # terrain
+            model_ids = category.assign_to_category_terrain(obs_locs)
+
+        clustered_observations_df[constants.STANDARD_ID_COLUMN] = model_ids
+
+        # Log assignment statistics
+        unique_assigned_ids = clustered_observations_df[
+            constants.STANDARD_ID_COLUMN
+        ].unique()
+        n_valid = np.count_nonzero(
+            clustered_observations_df[constants.STANDARD_ID_COLUMN]
+            != constants.RASTER_ID_NODATA_VALUE
+        )
+        logger.info(
+            f"Assigned category IDs: {n_valid} valid observations "
+            f"(out of {len(clustered_observations_df)} total)"
+        )
+        logger.info(
+            f"Unique category IDs in observations: {sorted(unique_assigned_ids[unique_assigned_ids != constants.RASTER_ID_NODATA_VALUE])[:20]}"
+        )
+        logger.info(
+            f"Category IDs in prior model: {sorted(current_prior_df[constants.STANDARD_ID_COLUMN].unique())}"
+        )
+
+        # Perform clustering
+        logger.info("Performing spatial clustering...")
+        clustered_observations_df = category.perform_clustering(
+            clustered_observations_df, nproc
+        )
+
+    # Load independent observations if provided
+    independent_observations_df = None
+    if independent_observations_csv is not None:
+        logger.info(
+            f"Loading independent observations from: {independent_observations_csv}"
+        )
+        independent_observations_df = pd.read_csv(
+            independent_observations_csv, skipinitialspace=True
+        )
+
+        utils.validate_csv_columns(
+            independent_observations_df,
+            constants.REQUIRED_OBSERVATION_COLUMNS,
+            "Independent observations CSV",
+        )
+
+        logger.info(
+            f"Loaded {len(independent_observations_df)} independent observations"
+        )
+
+        # Assign category IDs
+        obs_locs = independent_observations_df[
+            [constants.COL_EASTING, constants.COL_NORTHING]
+        ].values
+        if model_type == constants.ModelType.GEOLOGY:
+            model_ids = category.assign_to_category_geology(obs_locs)
+        else:  # terrain
+            model_ids = category.assign_to_category_terrain(obs_locs)
+
+        independent_observations_df[constants.STANDARD_ID_COLUMN] = model_ids
+
+    # Perform Bayesian update(s)
+    logger.info("Applying Bayesian updates...")
+    if clustered_observations_df is not None:
+        current_prior_df = category.update_with_clustered_data(
+            current_prior_df, clustered_observations_df
+        )
+
+    if independent_observations_df is not None:
+        current_prior_df = category.update_with_independent_data(
+            current_prior_df, independent_observations_df
+        )
+
+    # Create output directory if it doesn't exist
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    output_filename = constants.POSTERIOR_PREFIX + categorical_model_csv.name
+    output_path = output_dir / output_filename
+
+    current_prior_df.to_csv(output_path, index=False)
+
+
+def make_initial_vs30_raster(
+    output_dir: Path,
+    cfg: config_module.Vs30Config,
+    terrain: bool = False,
+    geology: bool = False,
+    geology_csv: Path | None = None,
+    terrain_csv: Path | None = None,
+) -> None:
+    """
+    Create initial VS30 mean and standard deviation rasters from category IDs.
+
+    Generates initial VS30 rasters by:
+
+    1. Creating category ID rasters (from terrain raster or geology shapefile)
+    2. Mapping category IDs to VS30 mean and standard deviation values from CSV files
+    3. Writing 2-band GeoTIFFs with VS30 mean (band 1) and standard deviation (band 2)
+
+    Output files are saved as terrain_initial_vs30_with_uncertainty.tif and/or
+    geology_initial_vs30_with_uncertainty.tif in output_dir.
+
+    Parameters
+    ----------
+    output_dir : Path
+        Output directory. Created if it does not exist.
+    cfg : Vs30Config
+        Configuration object providing grid parameters (grid_xmin, grid_xmax,
+        grid_ymin, grid_ymax, grid_dx, grid_dy).
+    terrain : bool, optional
+        Create terrain VS30 raster.
+    geology : bool, optional
+        Create geology VS30 raster.
+    geology_csv : Path, optional
+        Custom geology model CSV file. Defaults to bundled resource.
+    terrain_csv : Path, optional
+        Custom terrain model CSV file. Defaults to bundled resource.
+
+    Raises
+    ------
+    ValueError
+        If neither terrain nor geology is True.
+    """
+    if not terrain and not geology:
+        raise ValueError("At least one of terrain or geology must be True")
+
+    output_dir = output_dir.resolve()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    logger.info(f"Output directory: {output_dir}")
+
+    grid_params = {
+        "xmin": cfg.grid_xmin,
+        "xmax": cfg.grid_xmax,
+        "ymin": cfg.grid_ymin,
+        "ymax": cfg.grid_ymax,
+        "dx": cfg.grid_dx,
+        "dy": cfg.grid_dy,
+    }
+    logger.info(f"Using grid parameters: {grid_params}")
+
+    if terrain:
+        logger.info("Processing terrain model...")
+        terrain_model_csv = (
+            terrain_csv
+            if terrain_csv
+            else constants.TERRAIN_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE
+        )
+        logger.info(f"Using terrain model values from {terrain_model_csv}")
+
+        logger.info("Creating terrain category ID raster...")
+        id_raster = raster.create_category_id_raster(
+            constants.ModelType.TERRAIN, output_dir, **grid_params
+        )
+
+        logger.info("Creating terrain VS30 raster...")
+        vs30_raster = output_dir / constants.TERRAIN_INITIAL_VS30_FILENAME
+        raster.create_vs30_raster_from_ids(
+            id_raster, terrain_model_csv, vs30_raster,
+            model_type=constants.ModelType.TERRAIN,
+        )
+
+    if geology:
+        logger.info("Processing geology model...")
+        geology_model_csv = (
+            geology_csv
+            if geology_csv
+            else constants.GEOLOGY_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE
+        )
+        logger.info(f"Using geology model values from {geology_model_csv}")
+
+        logger.info("Creating geology category ID raster...")
+        id_raster = raster.create_category_id_raster(
+            constants.ModelType.GEOLOGY, output_dir, **grid_params
+        )
+
+        logger.info("Creating geology VS30 raster...")
+        vs30_raster = output_dir / constants.GEOLOGY_INITIAL_VS30_FILENAME
+        raster.create_vs30_raster_from_ids(
+            id_raster, geology_model_csv, vs30_raster,
+            model_type=constants.ModelType.GEOLOGY,
+        )
+
+
+def adjust_geology_vs30_by_slope_and_coastal_distance(
+    input_raster: Path,
+    id_raster: Path,
+    output_dir: Path,
+) -> None:
+    """
+    Apply hybrid geology modifications to an initial VS30 raster.
+
+    Adds slope-based and coast-distance-based modifications to the geology model.
+    Generates intermediate slope and coast distance rasters in the output directory.
+
+    Parameters
+    ----------
+    input_raster : Path
+        Path to initial geology VS30 raster (created by make_initial_vs30_raster).
+        Must be a 2-band raster with Vs30 and standard deviation.
+    id_raster : Path
+        Path to category ID raster (e.g., gid.tif used to create the input raster).
+    output_dir : Path
+        Directory to save output hybrid raster and intermediate files.
+
+    Raises
+    ------
+    ValueError
+        If raster dimensions do not match.
+    """
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    logger.info(
+        f"Processing slope and coastal distance adjusted model for: {input_raster}"
+    )
+
+    with rasterio.open(input_raster) as src:
+        vs30_array = src.read(1)
+        stdv_array = src.read(2)
+        profile = src.profile.copy()
+
+        with rasterio.open(id_raster) as id_src:
+            if id_src.width != src.width or id_src.height != src.height:
+                raise ValueError(
+                    f"Dimension mismatch! Input raster: {src.width}x{src.height}, "
+                    f"ID raster: {id_src.width}x{id_src.height}"
+                )
+            id_array = id_src.read(1)
+
+    slope_path = output_dir / constants.SLOPE_RASTER_FILENAME
+    logger.info(f"Generating slope raster: {slope_path}")
+    slope_array, _ = raster.create_slope_raster(slope_path, profile)
+
+    coast_path = output_dir / constants.COAST_DISTANCE_RASTER_FILENAME
+    logger.info(f"Generating coast distance raster: {coast_path}")
+    coast_dist_array, _ = raster.create_coast_distance_raster(coast_path, profile)
+
+    logger.info("Applying slope and coastal distance based geology modifications...")
+    mod_vs30, mod_stdv = raster.apply_hybrid_geology_modifications(
+        vs30_array,
+        stdv_array,
+        id_array,
+        slope_array,
+        coast_dist_array,
+    )
+
+    output_path = (
+        output_dir / constants.GEOLOGY_VS30_SLOPE_AND_COASTAL_DISTANCE_ADJUSTED_FILENAME
+    )
+
+    profile.update({"dtype": "float32", "compress": "deflate"})
+
+    logger.info(f"Saving hybrid raster to: {output_path}")
+    with rasterio.open(output_path, "w", **profile) as dst:
+        dst.write(mod_vs30, 1)
+        dst.write(mod_stdv, 2)
+        dst.descriptions = (
+            constants.BAND_DESCRIPTION_VS30_HYBRID,
+            constants.BAND_DESCRIPTION_STDV_HYBRID,
+        )
+
+
+def spatial_fit(
+    input_raster: Path,
+    observations_csv: Path,
+    model_values_csv: Path,
+    output_dir: Path,
+    model_type: constants.ModelType,
+    cfg: config_module.Vs30Config,
+    n_proc: int | None = None,
+) -> None:
+    """
+    Adjust a VS30 raster based on measurements using spatial conditioning.
+
+    Performs a spatial adjustment of an input raster by:
+
+    1. Loading the 2-band input raster (VS30 mean and stdv)
+    2. Loading measurements and mapping them to categories
+    3. Computing spatial fits to update pixels affected by measurements
+    4. Applying updates and saving the resulting 2-band GeoTIFF
+
+    Parameters
+    ----------
+    input_raster : Path
+        Path to input 2-band VS30 raster (Vs30 mean and standard deviation).
+    observations_csv : Path
+        Path to CSV file with measured VS30 values. Must contain columns:
+        easting, northing, vs30, uncertainty.
+    model_values_csv : Path
+        Path to CSV file with updated categorical Vs30 values
+        (e.g., updated_geology_model.csv).
+    output_dir : Path
+        Directory to save the adjusted raster.
+    model_type : str
+        Model type: either 'geology' or 'terrain'.
+    cfg : Vs30Config
+        Configuration object.
+    n_proc : int, optional
+        Number of parallel processes. Use -1 for all cores. Defaults to cfg.n_proc.
+    """
+    output_dir = output_dir.resolve()
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    n_proc_resolved = parallel.resolve_n_proc(
+        n_proc if n_proc is not None else cfg.n_proc
+    )
+
+    logger.info(f"Starting spatial fit for {model_type} model")
+    logger.info(f"Input raster: {input_raster}")
+    logger.info(f"Observations: {observations_csv}")
+    logger.info(f"Model values: {model_values_csv}")
+
+    # 1. Load Raster Data
+    logger.info("Loading raster data...")
+    raster_data = spatial.RasterData.from_file(input_raster)
+    spatial.validate_raster_data(raster_data)
+
+    # 2. Load Observations
+    logger.info("Loading observations...")
+    observations = pd.read_csv(observations_csv, skipinitialspace=True)
+    spatial.validate_observations(observations)
+
+    # Check if this is clustered observations file (for subsampling optimization)
+    clustered_obs_file = cfg.clustered_observations_file
+    is_clustered_obs = (
+        clustered_obs_file is not None
+        and observations_csv.resolve()
+        == (constants.RESOURCE_PATH / clustered_obs_file).resolve()
+    )
+
+    # 3. Load Model Values (updated categorical table)
+    logger.info("Loading updated model table...")
+    model_df = pd.read_csv(model_values_csv, skipinitialspace=True)
+    mean_col, std_col = raster.select_vs30_columns_by_priority(list(model_df.columns))
+
+    # Build table indexed by category ID
+    max_id = model_df[constants.STANDARD_ID_COLUMN].max()
+    updated_model_table = np.full((max_id, 2), np.nan)
+    for _, row in model_df.iterrows():
+        idx = int(row[constants.STANDARD_ID_COLUMN]) - 1
+        if 0 <= idx < max_id:
+            updated_model_table[idx, 0] = row[mean_col]
+            updated_model_table[idx, 1] = row[std_col]
+
+    # 4. Prepare Observation Data for Spatial Adjustment
+    logger.info("Preparing observation data for spatial adjustment...")
+    obs_data = spatial.prepare_observation_data(
+        observations,
+        raster_data,
+        updated_model_table,
+        model_type,
+        output_dir,
+        noisy=cfg.noisy,
+    )
+    logger.info(f"Prepared {len(obs_data.locations)} valid observations")
+
+    if len(obs_data.locations) == 0:
+        logger.warning(
+            "No valid observations found within model bounds. Copying input raster to output."
+        )
+        output_filename = constants.OUTPUT_FILENAMES[model_type]
+        output_path = output_dir / output_filename
+        shutil.copyfile(input_raster, output_path)
+        return
+
+    # 5. Find Affected Pixels (with optional clustered subsampling)
+    obs_data_for_bbox = spatial.apply_clustered_subsampling(
+        obs_data,
+        is_clustered_obs,
+        n_proc_resolved,
+        cfg.obs_subsample_step_for_clustered,
+    )
+
+    logger.info("Finding pixels affected by observations...")
+    bbox_result = spatial.find_affected_pixels(
+        raster_data,
+        obs_data_for_bbox,
+        max_spatial_boolean_array_memory_gb=cfg.max_spatial_boolean_array_memory_gb,
+        model_type=model_type,
+        max_dist_m=constants.MAX_DIST_M,
+        n_proc=n_proc_resolved,
+    )
+    logger.info(f"Found {bbox_result.n_affected_pixels:,} affected pixels")
+
+    # 6. Compute Spatial Adjustments
+    logger.info("Computing spatial updates...")
+    if n_proc_resolved > 1:
+        logger.info(f"Using {n_proc_resolved} parallel workers")
+        affected_flat_indices = np.where(bbox_result.mask)[0]
+        updates = parallel.run_parallel_spatial_fit(
+            affected_flat_indices=affected_flat_indices,
+            raster_data=raster_data,
+            obs_data=obs_data,
+            model_type=model_type,
+            max_dist_m=constants.MAX_DIST_M,
+            max_points=constants.MAX_POINTS,
+            noisy=cfg.noisy,
+            cov_reduc=constants.COV_REDUC,
+            n_proc=n_proc_resolved,
+        )
+    else:
+        updates = spatial.compute_spatial_adjustments(
+            raster_data,
+            obs_data,
+            bbox_result,
+            model_type,
+            max_spatial_boolean_array_memory_gb=cfg.max_spatial_boolean_array_memory_gb,
+            max_dist_m=constants.MAX_DIST_M,
+            max_points=constants.MAX_POINTS,
+            noisy=cfg.noisy,
+            cov_reduc=constants.COV_REDUC,
+        )
+
+    # 7. Apply Updates and Write Output
+    logger.info("Applying updates and writing output...")
+    spatial.apply_and_write_updates(raster_data, updates, model_type, output_dir)
+
+
+def combine(
+    geology_tif: Path,
+    terrain_tif: Path,
+    output_path: Path,
+    combination_method: str | float,
+) -> None:
+    """
+    Combine geology and terrain VS30 rasters using a weighted average.
+
+    Combines the two model outputs in log-space using the specified weighting
+    method. The output is a 2-band GeoTIFF with combined Vs30 mean and standard
+    deviation.
+
+    Parameters
+    ----------
+    geology_tif : Path
+        Path to geology VS30 raster.
+    terrain_tif : Path
+        Path to terrain VS30 raster.
+    output_path : Path
+        Path to combined output raster.
+    combination_method : str or float
+        Method for combining models. Either a ratio (float, e.g., 1.0 for equal
+        weighting) or 'standard_deviation_weighting'.
+    """
+    logger.info(f"Averaging {geology_tif} and {terrain_tif}")
+
+    with rasterio.open(geology_tif) as src_g, rasterio.open(terrain_tif) as src_t:
+        profile = src_g.profile.copy()
+        geol_data = src_g.read()
+        terr_data = src_t.read()
+
+        # Use nodata from geology (should be same for terrain)
+        nodata = src_g.nodata
+
+        # Set nodata values to NaN for easier calculation
+        geol_data[geol_data == nodata] = np.nan
+        terr_data[terr_data == nodata] = np.nan
+
+        # Combine models using shared function (log-space mixture)
+        combined_vs30, combined_stdv = utils.combine_vs30_models(
+            geol_vs30=geol_data[0],
+            geol_stdv=geol_data[1],
+            terr_vs30=terr_data[0],
+            terr_stdv=terr_data[1],
+            combination_method=combination_method,
+        )
+
+        # Create output array correctly restoring nodata where things are NaN
+        combined_data = np.stack([combined_vs30, combined_stdv])
+        combined_data[np.isnan(combined_data)] = nodata
+
+        profile.update(
+            {
+                "dtype": "float32",
+                "count": 2,
+                "nodata": nodata,
+                "compress": "deflate",
+            }
+        )
+
+        logger.info(f"Saving combined raster to: {output_path}")
+        with rasterio.open(output_path, "w", **profile) as dst:
+            dst.write(combined_data)
+            dst.descriptions = (
+                constants.BAND_DESCRIPTION_VS30_COMBINED,
+                constants.BAND_DESCRIPTION_STDV_COMBINED,
+            )
+
+
+def run_pipeline_for_model_type(
+    model_type: constants.ModelType,
+    categorical_model_csv: Path,
+    output_dir: Path,
+    cfg: config_module.Vs30Config,
+    clustered_observations_csv: Path | None = None,
+    independent_observations_csv: Path | None = None,
+    nproc: int | None = None,
+    n_proc: int | None = None,
+) -> None:
+    """
+    Run the full VS30 generation pipeline for a single model type.
+
+    Executes the complete pipeline in sequence:
+
+    1. update_categorical_vs30_models: (Conditional) Updates categorical priors
+       with observations if do_bayesian_update is enabled in config.
+    2. make_initial_vs30_raster: Creates initial 2-band VS30 raster using
+       posteriors (or priors if updates skipped).
+    3. adjust_geology_vs30_by_slope_and_coastal_distance: (Geology only) Applies
+       slope/coastal modifications.
+    4. spatial_fit: Final spatial adjustment using observations.
+
+    Parameters
+    ----------
+    model_type : str
+        Model type: either 'geology' or 'terrain'.
+    categorical_model_csv : Path
+        Path to CSV file with categorical Vs30 values.
+    output_dir : Path
+        Directory to save all pipeline outputs.
+    cfg : Vs30Config
+        Configuration object.
+    clustered_observations_csv : Path, optional
+        Path to CSV file with clustered observations (e.g., CPT data).
+    independent_observations_csv : Path, optional
+        Path to CSV file with independent observations (e.g., measured filtered).
+    nproc : int, optional
+        Number of processes for clustering. Defaults to cfg.n_proc.
+    n_proc : int, optional
+        Number of parallel processes for spatial adjustment. Defaults to cfg.n_proc.
+
+    Raises
+    ------
+    FileNotFoundError
+        If a pipeline step fails to produce its expected output file.
+    """
+    output_dir = output_dir.resolve()
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    logger.info(f"Starting full pipeline for {model_type}")
+    logger.info(f"Output directory: {output_dir}")
+
+    nproc_resolved = nproc if nproc is not None else cfg.n_proc
+
+    do_bayesian_update = (
+        cfg.do_bayesian_update_of_geology_and_terrain_categorical_vs30_values
+    )
+
+    # Resolve observations from config if not provided
+    clustered_observations_csv = utils.resolve_observation_csv(
+        clustered_observations_csv,
+        cfg.clustered_observations_file,
+        constants.RESOURCE_PATH,
+    )
+    independent_observations_csv = utils.resolve_observation_csv(
+        independent_observations_csv,
+        cfg.independent_observations_file,
+        constants.RESOURCE_PATH,
+    )
+
+    # --- Step 1: Update Categorical Models (conditional) ---
+    if do_bayesian_update:
+        logger.info("\n=== STEP 1: Updating Categorical Models ===")
+        update_categorical_vs30_models(
+            categorical_model_csv=categorical_model_csv,
+            clustered_observations_csv=clustered_observations_csv,
+            independent_observations_csv=independent_observations_csv,
+            output_dir=output_dir,
+            model_type=model_type,
+            nproc=nproc_resolved,
+        )
+
+        posterior_csv = (
+            output_dir / f"{constants.POSTERIOR_PREFIX}{categorical_model_csv.name}"
+        )
+        if not posterior_csv.exists():
+            raise FileNotFoundError(f"Step 1 failed to produce {posterior_csv}")
+    else:
+        logger.info(
+            "\n=== STEP 1: SKIPPED - Using prior categorical models directly ==="
+        )
+        posterior_csv = categorical_model_csv
+
+    # --- Step 2: Make Initial Raster ---
+    logger.info("\n=== STEP 2: Creating Initial Raster ===")
+    make_initial_vs30_raster(
+        output_dir=output_dir,
+        cfg=cfg,
+        terrain=(model_type == constants.ModelType.TERRAIN),
+        geology=(model_type == constants.ModelType.GEOLOGY),
+        geology_csv=posterior_csv
+        if model_type == constants.ModelType.GEOLOGY
+        else None,
+        terrain_csv=posterior_csv
+        if model_type == constants.ModelType.TERRAIN
+        else None,
+    )
+
+    initial_raster = output_dir / (
+        constants.GEOLOGY_INITIAL_VS30_FILENAME
+        if model_type == constants.ModelType.GEOLOGY
+        else constants.TERRAIN_INITIAL_VS30_FILENAME
+    )
+    id_raster_name = (
+        constants.GEOLOGY_ID_FILENAME
+        if model_type == constants.ModelType.GEOLOGY
+        else constants.TERRAIN_ID_FILENAME
+    )
+    id_raster = output_dir / id_raster_name
+
+    if not initial_raster.exists():
+        raise FileNotFoundError(f"Step 2 failed to produce {initial_raster}")
+
+    # --- Step 3: Hybrid Modification (Geology Only) ---
+    current_raster = initial_raster
+
+    if model_type == constants.ModelType.GEOLOGY:
+        logger.info(
+            "\n=== STEP 3: Creating Slope and Coastal Distance Adjusted Geology Raster ==="
+        )
+
+        adjust_geology_vs30_by_slope_and_coastal_distance(
+            input_raster=initial_raster,
+            id_raster=id_raster,
+            output_dir=output_dir,
+        )
+        current_raster = (
+            output_dir
+            / constants.GEOLOGY_VS30_SLOPE_AND_COASTAL_DISTANCE_ADJUSTED_FILENAME
+        )
+        if not current_raster.exists():
+            raise FileNotFoundError(f"Step 3 failed to produce {current_raster}")
+
+    # --- Step 4: Spatial Fit ---
+    logger.info("\n=== STEP 4: Spatial Adjustment ===")
+
+    # Prefer independent observations for spatial fit; fall back to clustered
+    spatial_obs_csv = independent_observations_csv or clustered_observations_csv
+
+    if spatial_obs_csv is None:
+        raise ValueError(
+            "No observation CSVs provided for spatial fit. "
+            "At least one of clustered or independent observations must be specified."
+        )
+
+    spatial_fit(
+        input_raster=current_raster,
+        observations_csv=spatial_obs_csv,
+        model_values_csv=posterior_csv,
+        output_dir=output_dir,
+        model_type=model_type,
+        cfg=cfg,
+        n_proc=n_proc,
+    )
+
+    logger.info(f"\n✓ Full pipeline for {model_type} completed successfully")
+    logger.info(f"  Final output available in: {output_dir}")
+
+
+def run_full_pipeline(
+    cfg: config_module.Vs30Config,
+    geology_categorical_csv: Path | None = None,
+    terrain_categorical_csv: Path | None = None,
+    clustered_observations_csv: Path | None = None,
+    independent_observations_csv: Path | None = None,
+    output_dir: Path | None = None,
+    nproc: int | None = None,
+    combination_method: str | float | None = None,
+    n_proc: int | None = None,
+) -> None:
+    """
+    Run the full VS30 generation pipeline for both geology and terrain models.
+
+    Executes the complete pipeline for both geology and terrain models, then
+    combines the results into a final averaged raster. This is the main entry
+    point for generating VS30 maps.
+
+    Parameters
+    ----------
+    cfg : Vs30Config
+        Configuration object.
+    geology_categorical_csv : Path, optional
+        Path to geology categorical CSV. Default from config/resources.
+    terrain_categorical_csv : Path, optional
+        Path to terrain categorical CSV. Default from config/resources.
+    clustered_observations_csv : Path, optional
+        Path to CSV file with clustered observations (e.g., CPT data).
+    independent_observations_csv : Path, optional
+        Path to CSV file with independent observations (e.g., measured filtered).
+    output_dir : Path, optional
+        Directory to save all pipeline outputs. Default from cfg.output_dir.
+    nproc : int, optional
+        Number of processes for clustering. Default from cfg.n_proc.
+    combination_method : str or float, optional
+        Method for combining models. Either a ratio (float) or
+        'standard_deviation_weighting'. Default from cfg.combination_method.
+    n_proc : int, optional
+        Number of parallel processes for spatial adjustment. Use -1 for all cores.
+        Default from cfg.n_proc.
+    """
+    start_time = time.time()
+
+    if output_dir is None:
+        output_dir = Path(cfg.output_dir)
+    output_dir = output_dir.resolve()
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    nproc = nproc if nproc is not None else cfg.n_proc
+    combination_method = (
+        combination_method if combination_method is not None else cfg.combination_method
+    )
+
+    n_proc_resolved = parallel.resolve_n_proc(
+        n_proc if n_proc is not None else cfg.n_proc
+    )
+
+    # Resolve CSV paths if not provided
+    if geology_categorical_csv is None:
+        geology_categorical_csv = (
+            constants.RESOURCE_PATH
+            / constants.GEOLOGY_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE
+        )
+    if terrain_categorical_csv is None:
+        terrain_categorical_csv = (
+            constants.RESOURCE_PATH
+            / constants.TERRAIN_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE
+        )
+
+    # 1. Run Geology Pipeline
+    logger.info("\n" + "=" * 80 + "\nRUNNING GEOLOGY PIPELINE\n" + "=" * 80)
+    run_pipeline_for_model_type(
+        model_type=constants.ModelType.GEOLOGY,
+        categorical_model_csv=geology_categorical_csv,
+        clustered_observations_csv=clustered_observations_csv,
+        independent_observations_csv=independent_observations_csv,
+        output_dir=output_dir,
+        cfg=cfg,
+        nproc=nproc,
+        n_proc=n_proc_resolved,
+    )
+
+    # 2. Run Terrain Pipeline
+    logger.info("\n" + "=" * 80 + "\nRUNNING TERRAIN PIPELINE\n" + "=" * 80)
+    run_pipeline_for_model_type(
+        model_type=constants.ModelType.TERRAIN,
+        categorical_model_csv=terrain_categorical_csv,
+        clustered_observations_csv=clustered_observations_csv,
+        independent_observations_csv=independent_observations_csv,
+        output_dir=output_dir,
+        cfg=cfg,
+        nproc=nproc,
+        n_proc=n_proc_resolved,
+    )
+
+    # 3. Combine Results
+    logger.info(
+        "\n" + "=" * 80 + "\nCOMBINING GEOLOGY AND TERRAIN RESULTS\n" + "=" * 80
+    )
+
+    geol_tif = output_dir / constants.OUTPUT_FILENAMES[constants.ModelType.GEOLOGY]
+    terr_tif = output_dir / constants.OUTPUT_FILENAMES[constants.ModelType.TERRAIN]
+    combined_tif = output_dir / constants.COMBINED_VS30_FILENAME
+
+    combine(
+        geology_tif=geol_tif,
+        terrain_tif=terr_tif,
+        output_path=combined_tif,
+        combination_method=combination_method,
+    )
+
+    elapsed_time = time.time() - start_time
+    logger.info(f"  Total execution time: {elapsed_time:.1f} seconds")
+    logger.info(f"  Combined output available at: {combined_tif}")
+
+
+def compute_at_locations(
+    cfg: config_module.Vs30Config,
+    locations_csv: Path | None = None,
+    output_csv: Path | None = None,
+    lon_column: str | None = None,
+    lat_column: str | None = None,
+    geology_categorical_csv: Path | None = None,
+    terrain_categorical_csv: Path | None = None,
+    clustered_observations_csv: Path | None = None,
+    independent_observations_csv: Path | None = None,
+    coast_distance_raster: Path | None = None,
+    include_intermediate: bool = True,
+    combination_method: str | float | None = None,
+    n_proc: int | None = None,
+) -> None:
+    """
+    Compute Vs30 values at specific latitude/longitude locations.
+
+    Runs the full Vs30 pipeline but only at the specified query points,
+    without generating raster grids. This is efficient for querying
+    Vs30 at a small number of locations.
+
+    The input CSV must have columns for longitude and latitude (WGS84).
+    Column names can be specified with lon_column and lat_column.
+
+    Parameters
+    ----------
+    cfg : Vs30Config
+        Configuration object.
+    locations_csv : Path, optional
+        CSV file with latitude/longitude columns (WGS84). Default from config.
+    output_csv : Path, optional
+        Output CSV file path. Default from config.
+    lon_column : str, optional
+        Name of longitude column in input CSV. Defaults to constants.LOCATIONS_LON_COLUMN.
+    lat_column : str, optional
+        Name of latitude column in input CSV. Defaults to constants.LOCATIONS_LAT_COLUMN.
+    geology_categorical_csv : Path, optional
+        Path to geology categorical CSV (default from config/resources).
+    terrain_categorical_csv : Path, optional
+        Path to terrain categorical CSV (default from config/resources).
+    clustered_observations_csv : Path, optional
+        Path to CSV file with clustered observations (e.g., CPT).
+    independent_observations_csv : Path, optional
+        Path to CSV file with independent observations.
+    coast_distance_raster : Path, optional
+        Path to coastal distance raster (required for hybrid geology modifications).
+    include_intermediate : bool, optional
+        Include intermediate values (geology/terrain separately) in output.
+    combination_method : str or float, optional
+        Method for combining: ratio (float) or 'standard_deviation_weighting'.
+        Defaults to cfg.combination_method.
+    n_proc : int, optional
+        Number of parallel processes (default from cfg.n_proc, -1 for all cores).
+
+    Raises
+    ------
+    ValueError
+        If required columns are missing from the locations CSV.
+    """
+    if locations_csv is None:
+        if cfg.locations_csv is None:
+            raise ValueError(
+                "Missing required locations_csv. "
+                "Set in config.yaml or provide --locations-csv."
+            )
+        locations_csv = Path(cfg.locations_csv)
+    if output_csv is None:
+        if cfg.locations_output_csv is None:
+            raise ValueError(
+                "Missing required locations_output_csv. "
+                "Set in config.yaml or provide --output-csv."
+            )
+        output_csv = Path(cfg.locations_output_csv)
+
+    if lon_column is None:
+        lon_column = constants.LOCATIONS_LON_COLUMN
+    if lat_column is None:
+        lat_column = constants.LOCATIONS_LAT_COLUMN
+
+    if combination_method is None:
+        combination_method = cfg.combination_method
+
+    logger.info(f"Loading locations from {locations_csv}...")
+    df = pd.read_csv(locations_csv)
+
+    if lon_column not in df.columns:
+        raise ValueError(f"Column '{lon_column}' not found in {locations_csv}")
+    if lat_column not in df.columns:
+        raise ValueError(f"Column '{lat_column}' not found in {locations_csv}")
+
+    # Convert to NZTM
+    nztm_coords = coordinates.wgs_depth_to_nztm(
+        np.column_stack([df[lat_column].values, df[lon_column].values])
+    )
+    northing, easting = nztm_coords[:, 0], nztm_coords[:, 1]
+    df[constants.COL_EASTING] = easting
+    df[constants.COL_NORTHING] = northing
+    points = np.column_stack([easting, northing])
+    logger.info(f"Loaded {len(points)} locations")
+
+    # Resolve CSV paths if not provided
+    if geology_categorical_csv is None:
+        geology_categorical_csv = (
+            constants.RESOURCE_PATH
+            / constants.GEOLOGY_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE
+        )
+    if terrain_categorical_csv is None:
+        terrain_categorical_csv = (
+            constants.RESOURCE_PATH
+            / constants.TERRAIN_MEAN_AND_STANDARD_DEVIATION_PER_CATEGORY_FILE
+        )
+
+    # Load observations for spatial adjustment
+    clustered_observations_csv = utils.resolve_observation_csv(
+        clustered_observations_csv,
+        cfg.clustered_observations_file,
+        constants.RESOURCE_PATH,
+    )
+    independent_observations_csv = utils.resolve_observation_csv(
+        independent_observations_csv,
+        cfg.independent_observations_file,
+        constants.RESOURCE_PATH,
+    )
+
+    # Load and combine all available observation files
+    observation_csvs = [
+        csv
+        for csv in [clustered_observations_csv, independent_observations_csv]
+        if csv is not None and csv.exists()
+    ]
+
+    if observation_csvs:
+        observations_df = pd.concat(
+            [pd.read_csv(csv) for csv in observation_csvs],
+            ignore_index=True,
+        )
+    else:
+        observations_df = pd.DataFrame(columns=constants.REQUIRED_OBSERVATION_COLUMNS)
+
+    logger.info(f"Loaded {len(observations_df)} observations for spatial adjustment")
+
+    # Load categorical models (skipinitialspace handles spaces after commas)
+    geol_model_df = pd.read_csv(geology_categorical_csv, skipinitialspace=True)
+    terr_model_df = pd.read_csv(terrain_categorical_csv, skipinitialspace=True)
+
+    # Resolve n_proc from arg or config
+    n_proc_resolved = parallel.resolve_n_proc(
+        n_proc if n_proc is not None else cfg.n_proc
+    )
+
+    # ================================================================
+    # Parallel Processing Path
+    # ================================================================
+    if n_proc_resolved > 1:
+        logger.info(f"\nProcessing with {n_proc_resolved} parallel workers...")
+
+        # Re-read the original CSV (without NZTM conversion - workers will do it)
+        locations_df_raw = pd.read_csv(locations_csv)
+
+        loc_config = parallel.LocationsChunkConfig(
+            lon_column=lon_column,
+            lat_column=lat_column,
+            include_intermediate=include_intermediate,
+            combination_method=combination_method,
+            coast_distance_raster=coast_distance_raster,
+            noisy=cfg.noisy,
+        )
+
+        df = parallel.run_parallel_locations(
+            locations_df=locations_df_raw,
+            observations_df=observations_df,
+            geol_model_df=geol_model_df,
+            terr_model_df=terr_model_df,
+            config=loc_config,
+            n_proc=n_proc_resolved,
+        )
+
+        output_csv.parent.mkdir(parents=True, exist_ok=True)
+        df.to_csv(output_csv, index=False)
+        logger.info(f"\nResults written to {output_csv}")
+        logger.info(f"  Total locations: {len(df)}")
+        return
+
+    # ================================================================
+    # Sequential Processing Path
+    # ================================================================
+    if coast_distance_raster is None or not coast_distance_raster.exists():
+        logger.warning(
+            "No coastal distance raster provided, skipping hybrid modifications"
+        )
+
+    with tqdm(total=len(points), desc="Geology: spatial adjustment", unit="point") as pbar:
+        (
+            geol_ids,
+            geol_vs30,
+            geol_stdv,
+            geol_vs30_hybrid,
+            geol_stdv_hybrid,
+            geol_mvn_vs30,
+            geol_mvn_stdv,
+        ) = parallel.process_geology_at_points(
+            points,
+            geol_model_df,
+            observations_df,
+            coast_distance_raster,
+            noisy=cfg.noisy,
+            progress_bar=pbar,
+        )
+
+    df[constants.COL_GEOLOGY_ID] = geol_ids
+    if include_intermediate:
+        df[constants.COL_GEOLOGY_VS30] = geol_vs30
+        df[constants.COL_GEOLOGY_STDV] = geol_stdv
+        df[constants.COL_GEOLOGY_VS30_HYBRID] = geol_vs30_hybrid
+        df[constants.COL_GEOLOGY_STDV_HYBRID] = geol_stdv_hybrid
+    df[constants.COL_GEOLOGY_MVN_VS30] = geol_mvn_vs30
+    df[constants.COL_GEOLOGY_MVN_STDV] = geol_mvn_stdv
+
+    with tqdm(total=len(points), desc="Terrain: spatial adjustment", unit="point") as pbar:
+        (
+            terr_ids,
+            terr_vs30,
+            terr_stdv,
+            terr_mvn_vs30,
+            terr_mvn_stdv,
+        ) = parallel.process_terrain_at_points(
+            points, terr_model_df, observations_df, noisy=cfg.noisy,
+            progress_bar=pbar,
+        )
+
+    df[constants.COL_TERRAIN_ID] = terr_ids
+    if include_intermediate:
+        df[constants.COL_TERRAIN_VS30] = terr_vs30
+        df[constants.COL_TERRAIN_STDV] = terr_stdv
+    df[constants.COL_TERRAIN_MVN_VS30] = terr_mvn_vs30
+    df[constants.COL_TERRAIN_MVN_STDV] = terr_mvn_stdv
+
+    logger.info("Combining models...")
+    combined_vs30, combined_stdv = utils.combine_vs30_models(
+        geol_mvn_vs30,
+        geol_mvn_stdv,
+        terr_mvn_vs30,
+        terr_mvn_stdv,
+        combination_method,
+    )
+
+    df[constants.COL_VS30] = combined_vs30
+    df[constants.COL_COMBINED_STDV] = combined_stdv
+
+    # Write output
+    output_csv.parent.mkdir(parents=True, exist_ok=True)
+    df.to_csv(output_csv, index=False)
+    logger.info(f"\nResults written to {output_csv}")
+    logger.info(f"  Total locations: {len(df)}")
diff --git a/vs30/raster.py b/vs30/raster.py
new file mode 100644
index 00000000..115842b6
--- /dev/null
+++ b/vs30/raster.py
@@ -0,0 +1,920 @@
+"""Categorical VS30 raster creation and hybrid geology modifications."""
+
+import logging
+import tarfile
+from pathlib import Path
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import rasterio
+import rasterio.enums
+import rasterio.features
+import rasterio.transform
+import rasterio.warp
+from osgeo import gdal
+from tqdm import tqdm
+
+from vs30 import constants
+
+logger = logging.getLogger(__name__)
+
+
+def ensure_shapefile_extracted(shapefile_path: Path, directory_prefix: str) -> None:
+    """
+    Ensure a shapefile is extracted from shapefiles.tar.xz.
+
+    Checks if the shapefile exists. If not, extracts it from shapefiles.tar.xz.
+    This is needed because shapefiles are stored compressed in the archive and
+    may not be present by default.
+
+    Parameters
+    ----------
+    shapefile_path : Path
+        Full path to the shapefile to check/extract.
+    directory_prefix : str
+        Directory prefix within the archive (e.g., "qmap" or "coast").
+
+    Raises
+    ------
+    FileNotFoundError
+        If shapefiles.tar.xz is not found or extraction fails.
+    ValueError
+        If the directory is not found in the archive.
+    """
+    if shapefile_path.exists():
+        return
+
+    archive_path = constants.DATA_DIR / constants.SHAPEFILES_ARCHIVE_FILENAME
+    if not archive_path.exists():
+        raise FileNotFoundError(
+            f"Shapefile archive not found: {archive_path}. "
+            f"Cannot extract {shapefile_path.name}. Please ensure shapefiles.tar.xz exists."
+        )
+
+    with tarfile.open(archive_path, "r:xz") as tar:
+        members = [
+            member
+            for member in tar.getmembers()
+            if member.name.startswith(f"{directory_prefix}/")
+        ]
+        if not members:
+            raise ValueError(
+                f"No '{directory_prefix}' directory found in archive {archive_path}"
+            )
+        tar.extractall(path=constants.DATA_DIR, members=members)
+
+    if not shapefile_path.exists():
+        raise FileNotFoundError(
+            f"Failed to extract {shapefile_path.name} from {archive_path}. "
+            f"Expected file at {shapefile_path} but it was not created."
+        )
+
+
+def load_model_values_from_csv(csv_path: str) -> np.ndarray:
+    """
+    Load model values (vs30 mean and standard deviation) from CSV file.
+
+    Parameters
+    ----------
+    csv_path : str
+        Path to CSV file relative to resources directory.
+
+    Returns
+    -------
+    np.ndarray
+        Array of shape (n_categories, 2) with dtype np.float64.
+        Columns are [mean_vs30_km_per_s, standard_deviation_vs30_km_per_s].
+
+    Raises
+    ------
+    FileNotFoundError
+        If CSV file is not found.
+    ValueError
+        If CSV file is malformed or missing required columns.
+    """
+    csv_file_path = constants.RESOURCE_PATH / csv_path
+    if not csv_file_path.exists():
+        raise FileNotFoundError(
+            f"CSV file not found: {csv_file_path}. "
+            f"Expected path relative to resources directory: {csv_path}"
+        )
+
+    # Read CSV and check what columns are available
+    # Use skipinitialspace=True to handle spaces after commas in CSV
+    df = pd.read_csv(csv_file_path, skipinitialspace=True)
+    required_cols = [constants.COL_MEAN, constants.COL_STDV]
+    missing_cols = [col for col in required_cols if col not in df.columns]
+    if missing_cols:
+        raise ValueError(
+            f"CSV file {csv_file_path} is missing required columns. "
+            f"Missing columns: {missing_cols}. "
+            f"Available columns: {list(df.columns)}"
+        )
+
+    # Return only the required columns as numpy array
+    return df[required_cols].values
+
+
+def create_category_id_raster(
+    model_type: str,
+    output_dir: Path,
+    xmin: float,
+    xmax: float,
+    ymin: float,
+    ymax: float,
+    dx: float,
+    dy: float,
+) -> Path:
+    """
+    Create category ID raster for terrain or geology.
+
+    For terrain: Resamples IwahashiPike.tif to target grid.
+    For geology: Rasterizes qmap.shp shapefile to target grid.
+
+    Parameters
+    ----------
+    model_type : constants.ModelType
+        Either ModelType.TERRAIN or ModelType.GEOLOGY.
+    output_dir : Path
+        Directory where output raster will be saved.
+    xmin : float
+        Grid minimum easting (m, NZTM2000).
+    xmax : float
+        Grid maximum easting (m, NZTM2000).
+    ymin : float
+        Grid minimum northing (m, NZTM2000).
+    ymax : float
+        Grid maximum northing (m, NZTM2000).
+    dx : float
+        Grid cell width (m).
+    dy : float
+        Grid cell height (m).
+
+    Returns
+    -------
+    Path
+        Path to created ID raster file.
+
+    Raises
+    ------
+    ValueError
+        If model_type is not a valid ModelType.
+    FileNotFoundError
+        If input files don't exist.
+    """
+    if model_type not in constants.ModelType:
+        raise ValueError(f"model_type must be a valid ModelType, got '{model_type}'")
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Common setup: calculate grid dimensions and transform
+    nx = round((xmax - xmin) / dx)
+    ny = round((ymax - ymin) / dy)
+    dst_transform = rasterio.transform.from_bounds(xmin, ymin, xmax, ymax, nx, ny)
+    output_filename = (
+        constants.TERRAIN_ID_FILENAME
+        if model_type == constants.ModelType.TERRAIN
+        else constants.GEOLOGY_ID_FILENAME
+    )
+    output_path = output_dir / output_filename
+
+    # Common output raster profile
+    profile = {
+        "driver": constants.GEOTIFF_DRIVER,
+        "width": nx,
+        "height": ny,
+        "count": 1,
+        "dtype": "uint8",
+        "crs": constants.NZTM_CRS,
+        "transform": dst_transform,
+        "nodata": constants.RASTER_ID_NODATA_VALUE,
+        "compress": constants.GEOTIFF_COMPRESSION,
+    }
+
+    if model_type == constants.ModelType.TERRAIN:
+        # Resample terrain raster to target grid
+        terrain_raster_path = constants.DATA_DIR / constants.TERRAIN_RASTER_FILENAME
+        if not terrain_raster_path.exists():
+            raise FileNotFoundError(f"Terrain raster not found: {terrain_raster_path}")
+
+        # Read source raster and reproject
+        with rasterio.open(terrain_raster_path) as src:
+            with rasterio.open(output_path, "w", **profile) as dst:
+                rasterio.warp.reproject(
+                    source=rasterio.band(src, 1),
+                    destination=rasterio.band(dst, 1),
+                    src_transform=src.transform,
+                    src_crs=src.crs,
+                    dst_transform=dst_transform,
+                    dst_crs=constants.NZTM_CRS,
+                    resampling=rasterio.enums.Resampling.nearest,
+                )
+                dst.descriptions = (constants.BAND_DESCRIPTION_ID_INDEX,)
+
+    else:  # geology
+        # Ensure qmap.shp is extracted from shapefiles.tar.xz if needed
+        ensure_shapefile_extracted(
+            constants.DATA_DIR / constants.GEOLOGY_SHAPEFILE_PATH, "qmap"
+        )
+
+        # Rasterize geology shapefile to target grid
+        geology_shapefile_path = constants.DATA_DIR / constants.GEOLOGY_SHAPEFILE_PATH
+        if not geology_shapefile_path.exists():
+            raise FileNotFoundError(
+                f"Geology shapefile not found: {geology_shapefile_path}"
+            )
+
+        # Read shapefile
+        gdf = gpd.read_file(geology_shapefile_path)
+        if constants.SHAPEFILE_GEOLOGY_ID_COLUMN not in gdf.columns:
+            raise ValueError(
+                f"Shapefile {geology_shapefile_path} missing "
+                f"'{constants.SHAPEFILE_GEOLOGY_ID_COLUMN}' column"
+            )
+
+        # Ensure shapefile is in NZTM CRS (EPSG:2193)
+        if gdf.crs is None or str(gdf.crs) != constants.NZTM_CRS:
+            gdf = gdf.to_crs(constants.NZTM_CRS)
+
+        # Create shapes iterator for rasterization
+        shapes = (
+            (geom, value)
+            for geom, value in zip(
+                gdf.geometry, gdf[constants.SHAPEFILE_GEOLOGY_ID_COLUMN]
+            )
+        )
+
+        # Rasterize to output file
+        with rasterio.open(output_path, "w", **profile) as dst:
+            burned = rasterio.features.rasterize(
+                shapes=shapes,
+                out_shape=(ny, nx),
+                transform=dst_transform,
+                fill=constants.RASTER_ID_NODATA_VALUE,
+                dtype=np.uint8,
+                all_touched=False,
+            )
+            dst.write(burned, 1)
+            dst.descriptions = (constants.BAND_DESCRIPTION_ID_INDEX,)
+
+    return output_path
+
+
+def select_vs30_columns_by_priority(columns: list[str]) -> tuple[str, str]:
+    """
+    Determine which columns to use for VS30 mean and standard deviation.
+
+    Prioritizes columns in the following order:
+    1. Independent observations posterior (result of second update step)
+    2. Clustered observations posterior (result of first update step)
+    3. Generic posterior
+    4. Explicit prior
+    5. Standard/Original names
+
+    Parameters
+    ----------
+    columns : list[str]
+        List of available column names in the CSV.
+
+    Returns
+    -------
+    tuple[str, str]
+        (mean_column_name, std_column_name)
+
+    Raises
+    ------
+    ValueError
+        If no suitable column pair is found.
+    """
+    priorities = [
+        # 1. Independent observations posterior
+        (
+            constants.COL_POSTERIOR_MEAN_INDEPENDENT,
+            constants.COL_POSTERIOR_STDV_INDEPENDENT,
+        ),
+        # 2. Clustered observations posterior
+        (
+            constants.COL_POSTERIOR_MEAN_CLUSTERED,
+            constants.COL_POSTERIOR_STDV_CLUSTERED,
+        ),
+        # 3. Generic posterior
+        (constants.COL_POSTERIOR_MEAN, constants.COL_POSTERIOR_STDV),
+        # 4. Explicit prior
+        (constants.COL_PRIOR_MEAN, constants.COL_PRIOR_STDV),
+        # 5. Standard/Original names
+        (constants.COL_MEAN, constants.COL_STDV),
+    ]
+
+    for mean_col, std_col in priorities:
+        if mean_col in columns and std_col in columns:
+            return mean_col, std_col
+
+    raise ValueError(
+        f"Could not find valid VS30 mean and standard deviation columns. "
+        f"Available columns: {columns}"
+    )
+
+
+def create_vs30_raster_from_ids(
+    id_raster_path: Path,
+    csv_path: Path,
+    output_path: Path,
+    model_type: constants.ModelType | None = None,
+) -> Path:
+    """
+    Create VS30 mean and standard deviation raster from category ID raster.
+
+    This function maps category IDs from the spatial raster file (qmap.shp for geology
+    or IwahashiPike.tif for terrain) directly to VS30 values from the CSV file by
+    matching the ID values between the two files.
+
+    For geology case: Ensures qmap.shp is extracted from shapefiles.tar.xz if needed.
+    The shapefile is required to create the ID raster that this function processes.
+
+    Parameters
+    ----------
+    id_raster_path : Path
+        Path to input category ID raster (contains IDs from spatial file).
+    csv_path : Path
+        Path to CSV file (relative to resources directory) containing ID-to-VS30 mapping.
+        CSV must have columns: 'id', 'mean_vs30_km_per_s', 'standard_deviation_vs30_km_per_s'.
+        The 'id' column values must match the ID values in the spatial raster.
+    output_path : Path
+        Path where output 2-band raster will be saved.
+
+    Returns
+    -------
+    Path
+        Path to created VS30 raster file.
+
+    Raises
+    ------
+    FileNotFoundError
+        If CSV file is not found or shapefiles.tar.xz is missing.
+    ValueError
+        If CSV file is missing required columns or IDs don't match.
+    """
+    logger.info(f"Creating VS30 raster: {output_path}")
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    ensure_shapefile_extracted(
+        constants.DATA_DIR / constants.GEOLOGY_SHAPEFILE_PATH, "qmap"
+    )
+
+    # Load CSV and create ID-to-values mapping
+    csv_file_path = constants.RESOURCE_PATH / csv_path
+    if not csv_file_path.exists():
+        raise FileNotFoundError(
+            f"CSV file not found: {csv_file_path}. "
+            f"Expected path relative to resources directory: {csv_path}"
+        )
+
+    df = pd.read_csv(csv_file_path, skipinitialspace=True)
+    df.columns = df.columns.str.strip()
+
+    mean_col, std_col = select_vs30_columns_by_priority(list(df.columns))
+
+    required_cols = [constants.STANDARD_ID_COLUMN, mean_col, std_col]
+    missing_cols = [col for col in required_cols if col not in df.columns]
+    if missing_cols:
+        raise ValueError(
+            f"CSV file {csv_file_path} is missing required columns: {missing_cols}"
+        )
+
+    id_to_vs30_values = {
+        int(row[constants.STANDARD_ID_COLUMN]): (
+            float(row[mean_col]),
+            float(row[std_col]),
+        )
+        for _, row in df.iterrows()
+    }
+
+    # Read ID raster
+    with rasterio.open(id_raster_path) as src:
+        id_array = src.read(1)
+        profile = src.profile.copy()
+
+    # Create output arrays
+    vs30_array = np.full(id_array.shape, constants.NODATA_VALUE, dtype=np.float32)
+    stdv_array = np.full(id_array.shape, constants.NODATA_VALUE, dtype=np.float32)
+
+    # Map pixel IDs to VS30 values
+    unique_ids = np.unique(id_array)
+    valid_ids = unique_ids[
+        (unique_ids != constants.RASTER_ID_NODATA_VALUE) & (unique_ids != 0)
+    ]
+
+    label = str(model_type).capitalize() if model_type else "Model"
+    for pixel_id in tqdm(valid_ids, desc=f"{label}: mapping categories to Vs30", unit="ID"):
+        if pixel_id in id_to_vs30_values:
+            mean_vs30, stddev_vs30 = id_to_vs30_values[pixel_id]
+            mask = id_array == pixel_id
+            vs30_array[mask] = mean_vs30
+            stdv_array[mask] = stddev_vs30
+        else:
+            raise ValueError(
+                f"ID {pixel_id} found in raster {id_raster_path} but not in CSV {csv_path}. "
+                f"Available IDs in CSV: {sorted(id_to_vs30_values.keys())}"
+            )
+
+    profile.update(
+        {
+            "count": 2,
+            "dtype": "float32",
+            "nodata": constants.NODATA_VALUE,
+            "compress": "deflate",
+        }
+    )
+
+    with rasterio.open(output_path, "w", **profile) as dst:
+        dst.write(vs30_array, constants.RASTER_BAND_VS30)
+        dst.write(stdv_array, constants.RASTER_BAND_STDV)
+        dst.descriptions = (
+            constants.BAND_DESCRIPTION_VS30,
+            constants.BAND_DESCRIPTION_STDV,
+        )
+
+    logger.info(f"Completed VS30 raster: {output_path}")
+    return output_path
+
+
+def create_coast_distance_raster(
+    output_path: Path, template_profile: dict
+) -> tuple[np.ndarray, dict]:
+    """
+    Create a raster of distance to the nearest coast (in meters).
+
+    Uses GDAL to rasterize coast shapefile and compute proximity distances,
+    following the legacy implementation for numerical consistency.
+    Computes on full NZ land extent to ensure accurate distances for all
+    observation locations, even those outside the configured study domain.
+
+    Parameters
+    ----------
+    output_path : Path
+        Path where the output coast distance raster will be saved.
+    template_profile : dict
+        Rasterio profile of the reference raster (to match resolution and bounds).
+
+    Returns
+    -------
+    tuple[np.ndarray, dict]
+        A tuple containing:
+        - The distance array (float32).
+        - The updated profile used for saving.
+    """
+    logger.info("Creating coast distance raster...")
+    ensure_shapefile_extracted(
+        constants.DATA_DIR / constants.COASTLINE_SHAPEFILE_PATH, "coast"
+    )
+
+    # Get template bounds for final output extent
+    dx = template_profile["transform"].a
+    dy = abs(template_profile["transform"].e)
+    s_xmin = template_profile["transform"].c
+    s_ymax = template_profile["transform"].f
+    s_xmax = s_xmin + template_profile["width"] * dx
+    s_ymin = s_ymax - template_profile["height"] * dy
+
+    # Extend to full NZ land coverage to ensure accurate distances
+    # (matching legacy _full_land_grid behavior)
+    g_xmin = min(constants.FULL_NZ_LAND_XMIN, s_xmin)
+    g_xmax = max(constants.FULL_NZ_LAND_XMAX, s_xmax)
+    g_ymin = min(constants.FULL_NZ_LAND_YMIN, s_ymin)
+    g_ymax = max(constants.FULL_NZ_LAND_YMAX, s_ymax)
+
+    # Check if grid was extended beyond template bounds (requires cropping later)
+    grid_was_extended = (
+        g_xmin < s_xmin or g_xmax > s_xmax or g_ymin < s_ymin or g_ymax > s_ymax
+    )
+
+    # Rasterize land polygons using GDAL (legacy approach)
+    # Use UInt16 data type as in legacy code (sufficient for distance range)
+    ds = gdal.Rasterize(
+        str(output_path),
+        str(constants.DATA_DIR / constants.COASTLINE_SHAPEFILE_PATH),
+        creationOptions=["COMPRESS=DEFLATE", "BIGTIFF=YES"],
+        outputBounds=[g_xmin, g_ymin, g_xmax, g_ymax],
+        xRes=dx,
+        yRes=dy,
+        noData=0,
+        burnValues=1,
+        outputType=gdal.GetDataTypeByName("UInt16"),
+    )
+
+    # Compute proximity distances using GDAL (legacy approach)
+    # DISTUNITS=GEO ensures distances in georeferenced units (meters)
+    band = ds.GetRasterBand(1)
+    band.SetDescription(constants.BAND_DESCRIPTION_COAST_DISTANCE)
+    # Note: ComputeProximity modifies the raster in-place
+    ds = gdal.ComputeProximity(band, band, ["VALUES=0", "DISTUNITS=GEO"])
+    band = None
+    ds = None
+
+    # Output profile (shared by both branches)
+    profile = template_profile.copy()
+    profile.update(
+        {"dtype": "float32", "count": 1, "nodata": None, "compress": "deflate"}
+    )
+
+    # If grid was extended, crop back to template bounds
+    if grid_was_extended:
+        with rasterio.open(output_path) as src:
+            extended_data = src.read(1)
+
+        col_off = round((s_xmin - g_xmin) / dx)
+        row_off = round((g_ymax - s_ymax) / dy)
+
+        distance_meters = extended_data[
+            row_off : row_off + template_profile["height"],
+            col_off : col_off + template_profile["width"],
+        ].astype(np.float32)
+
+        with rasterio.open(output_path, "w", **profile) as dst:
+            dst.write(distance_meters, 1)
+            dst.descriptions = (constants.BAND_DESCRIPTION_COAST_DISTANCE,)
+    else:
+        with rasterio.open(output_path) as src:
+            distance_meters = src.read(1).astype(np.float32)
+
+    return distance_meters, profile
+
+
+def create_slope_raster(
+    output_path: Path, template_profile: dict
+) -> tuple[np.ndarray, dict]:
+    """
+    Create a slope raster matching the target grid.
+
+    Resamples the source slope raster to the target properties.
+
+    Parameters
+    ----------
+    output_path : Path
+        Path where the output slope raster will be saved.
+    template_profile : dict
+        Rasterio profile of the reference raster.
+
+    Returns
+    -------
+    tuple[np.ndarray, dict]
+        A tuple containing:
+        - The slope array (float32).
+        - The updated profile used for saving.
+    """
+    logger.info("Creating slope raster...")
+    slope_raster_path = constants.DATA_DIR / constants.SLOPE_SOURCE_RASTER_FILENAME
+    if not slope_raster_path.exists():
+        raise FileNotFoundError(f"Slope raster not found: {slope_raster_path}")
+
+    # Reproject the slope raster onto the specified grid
+    destination = np.zeros((template_profile["height"], template_profile["width"]))
+    with rasterio.open(slope_raster_path) as src:
+        rasterio.warp.reproject(
+            source=rasterio.band(src, 1),
+            destination=destination,
+            src_transform=src.transform,
+            src_crs=src.crs,
+            dst_transform=template_profile["transform"],
+            dst_crs=template_profile["crs"],
+            resampling=rasterio.enums.Resampling.nearest,
+        )
+    profile = template_profile.copy()
+    profile.update(
+        {
+            "dtype": "float32",
+            "count": 1,
+            "nodata": constants.NODATA_VALUE,
+            "compress": "deflate",
+        }
+    )
+    with rasterio.open(output_path, "w", **profile) as dst:
+        dst.write(destination, 1)
+        dst.descriptions = (constants.BAND_DESCRIPTION_SLOPE,)
+
+    return destination, profile
+
+
+def apply_coastal_distance_modification(
+    vs30_array: np.ndarray,
+    id_array: np.ndarray,
+    coast_dist_array: np.ndarray,
+    gid: int,
+    dist_min: float,
+    dist_max: float,
+    vs30_min: float,
+    vs30_max: float,
+) -> None:
+    """
+    Apply linear coastal distance interpolation to a specific geology group.
+
+    Modifies vs30_array in-place for pixels matching the given geology group ID.
+    The Vs30 value is linearly interpolated between vs30_min and vs30_max based
+    on distance from coast, clamped to [vs30_min, vs30_max].
+
+    Parameters
+    ----------
+    vs30_array : ndarray
+        VS30 array to modify in-place.
+    id_array : ndarray
+        Category ID array.
+    coast_dist_array : ndarray
+        Coastal distance array (meters).
+    gid : int
+        Geology group ID to modify.
+    dist_min, dist_max : float
+        Distance range (meters) for linear interpolation.
+    vs30_min, vs30_max : float
+        Vs30 range (m/s) for linear interpolation.
+    """
+    mask = id_array == gid
+    if not np.any(mask):
+        return
+
+    dist_vals = coast_dist_array[mask]
+    val = vs30_min + (vs30_max - vs30_min) * (dist_vals - dist_min) / (
+        dist_max - dist_min
+    )
+    vs30_array[mask] = np.clip(val, vs30_min, vs30_max)
+
+
+def apply_hybrid_geology_modifications(
+    vs30_array: np.ndarray,
+    stdv_array: np.ndarray,
+    id_array: np.ndarray,
+    slope_array: np.ndarray,
+    coast_dist_array: np.ndarray,
+    mod6: bool = True,
+    mod13: bool = True,
+    hybrid: bool = True,
+    hybrid_mod6_dist_min: float = constants.HYBRID_MOD6_DIST_MIN,
+    hybrid_mod6_dist_max: float = constants.HYBRID_MOD6_DIST_MAX,
+    hybrid_mod6_vs30_min: float = constants.HYBRID_MOD6_VS30_MIN,
+    hybrid_mod6_vs30_max: float = constants.HYBRID_MOD6_VS30_MAX,
+    hybrid_mod13_dist_min: float = constants.HYBRID_MOD13_DIST_MIN,
+    hybrid_mod13_dist_max: float = constants.HYBRID_MOD13_DIST_MAX,
+    hybrid_mod13_vs30_min: float = constants.HYBRID_MOD13_VS30_MIN,
+    hybrid_mod13_vs30_max: float = constants.HYBRID_MOD13_VS30_MAX,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Apply hybrid model modifications to VS30 and standard deviation arrays.
+
+    Implements slope-based Vs30 interpolation and coastal distance adjustments
+    for specific geology categories. Modifies arrays in-place but also returns
+    them for clarity.
+
+    Parameters
+    ----------
+    vs30_array : np.ndarray
+        Base VS30 array (float).
+    stdv_array : np.ndarray
+        Base Standard Deviation array (float).
+    id_array : np.ndarray
+        Category ID array (int).
+    slope_array : np.ndarray
+        Slope array (float).
+    coast_dist_array : np.ndarray
+        Distance to coast array (float).
+    mod6 : bool, optional
+        Whether to apply modification for Group 6 (Alluvium). Default True.
+    mod13 : bool, optional
+        Whether to apply modification for Group 13 (Floodplain). Default True.
+    hybrid : bool, optional
+        Whether to apply general hybrid slope-based modifications. Default True.
+    hybrid_mod6_dist_min : float
+        Min distance threshold for mod6. Default from constants.
+    hybrid_mod6_dist_max : float
+        Max distance threshold for mod6. Default from constants.
+    hybrid_mod6_vs30_min : float
+        Min Vs30 for mod6. Default from constants.
+    hybrid_mod6_vs30_max : float
+        Max Vs30 for mod6. Default from constants.
+    hybrid_mod13_dist_min : float
+        Min distance threshold for mod13. Default from constants.
+    hybrid_mod13_dist_max : float
+        Max distance threshold for mod13. Default from constants.
+    hybrid_mod13_vs30_min : float
+        Min Vs30 for mod13. Default from constants.
+    hybrid_mod13_vs30_max : float
+        Max Vs30 for mod13. Default from constants.
+
+    Returns
+    -------
+    tuple[np.ndarray, np.ndarray]
+        Modified (vs30_array, stdv_array).
+    """
+    logger.info("Applying slope and coastal distance based geology modifications...")
+
+    # 1. Update Standard Deviation for specific groups
+    if hybrid:
+        # group IDs have reduction factors from constants
+        for gid, factor in constants.HYBRID_SIGMA_REDUCTION_FACTORS.items():
+            # Find pixels with this ID
+            mask = id_array == gid
+            stdv_array[mask] *= factor
+
+    # 2. Hybrid slope-based VS30 calculation
+    if hybrid:
+        # Prevent log10(0) or log10(-NODATA) by capping at constants.MIN_SLOPE_FOR_LOG
+        modified_slope = np.copy(slope_array)
+        modified_slope[
+            (modified_slope <= 0) | (modified_slope == constants.NODATA_VALUE)
+        ] = constants.MIN_SLOPE_FOR_LOG
+        safe_log_slope = np.log10(modified_slope)
+
+        for spec in constants.HYBRID_VS30_PARAMS:
+            gid = spec.gid
+            slope_limits = spec.slope_limits
+            # Compute log10 of vs30 values at runtime
+            vs30_limits_log10 = np.log10(np.array(spec.vs30_values))
+
+            # Skip ID 4 if mod6 is active (handled separately later)
+            if gid == 4 and mod6:
+                continue
+
+            # Find mask: ID matches
+            mask = id_array == gid
+
+            if np.any(mask):
+                # Interpolate for all pixels in this group
+                interpolated_val = np.interp(
+                    safe_log_slope[mask], slope_limits, vs30_limits_log10
+                )
+                vs30_array[mask] = 10**interpolated_val
+
+    # 3. Distance-based modification for alluvium (GID 4) and floodplain (GID 10)
+    if mod6:
+        apply_coastal_distance_modification(
+            vs30_array,
+            id_array,
+            coast_dist_array,
+            gid=4,
+            dist_min=hybrid_mod6_dist_min,
+            dist_max=hybrid_mod6_dist_max,
+            vs30_min=hybrid_mod6_vs30_min,
+            vs30_max=hybrid_mod6_vs30_max,
+        )
+
+    if mod13:
+        apply_coastal_distance_modification(
+            vs30_array,
+            id_array,
+            coast_dist_array,
+            gid=10,
+            dist_min=hybrid_mod13_dist_min,
+            dist_max=hybrid_mod13_dist_max,
+            vs30_min=hybrid_mod13_vs30_min,
+            vs30_max=hybrid_mod13_vs30_max,
+        )
+
+    return vs30_array, stdv_array
+
+
+def apply_hybrid_modifications_at_points(
+    points: np.ndarray,
+    vs30: np.ndarray,
+    stdv: np.ndarray,
+    geology_ids: np.ndarray,
+    slope_raster_path: Path | None = None,
+    coast_distance_raster_path: Path | None = None,
+    mod6: bool = True,
+    mod13: bool = True,
+    hybrid: bool = True,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Apply hybrid geology modifications at specific points.
+
+    This is the point-based equivalent of apply_hybrid_geology_modifications().
+    It samples slope and coastal distance values at query points and applies
+    the same modification logic.
+
+    Parameters
+    ----------
+    points : np.ndarray
+        (N, 2) array of [easting, northing] coordinates in NZTM.
+    vs30 : np.ndarray
+        Initial Vs30 values at points (N,).
+    stdv : np.ndarray
+        Initial standard deviation values at points (N,).
+    geology_ids : np.ndarray
+        Geology category IDs at points (N,).
+    slope_raster_path : Path, optional
+        Path to slope raster. If None, uses source slope.tif from data directory.
+    coast_distance_raster_path : Path, optional
+        Path to coastal distance raster. If None, raises error (must be provided).
+    mod6 : bool, optional
+        Whether to apply modification for Group 6 (Alluvium). Default True.
+    mod13 : bool, optional
+        Whether to apply modification for Group 13 (Floodplain). Default True.
+    hybrid : bool, optional
+        Whether to apply general hybrid slope-based modifications. Default True.
+
+    Returns
+    -------
+    tuple[np.ndarray, np.ndarray]
+        Modified (vs30, stdv) arrays.
+
+    Raises
+    ------
+    FileNotFoundError
+        If required raster files are not found.
+    ValueError
+        If coast_distance_raster_path is None and mod6 or mod13 is True.
+    """
+    # Use source slope raster if not specified
+    if slope_raster_path is None:
+        slope_raster_path = constants.DATA_DIR / constants.SLOPE_SOURCE_RASTER_FILENAME
+
+    # Coast distance raster is required for mod6/mod13
+    if (mod6 or mod13) and coast_distance_raster_path is None:
+        raise ValueError(
+            "coast_distance_raster_path is required when mod6 or mod13 is True. "
+            "Run the grid pipeline first to create the coastal distance raster, "
+            "or provide a path to an existing one."
+        )
+
+    # Sample slope at points
+    if not slope_raster_path.exists():
+        raise FileNotFoundError(f"Slope raster not found: {slope_raster_path}")
+
+    with rasterio.open(slope_raster_path) as src:
+        slope_values = np.array(
+            [sample[0] for sample in src.sample(points)], dtype=np.float64
+        )
+
+    # Sample coastal distance at points (if needed)
+    if mod6 or mod13:
+        assert coast_distance_raster_path is not None  # Type guard: ValueError above ensures this
+        if not coast_distance_raster_path.exists():
+            raise FileNotFoundError(
+                f"Coastal distance raster not found: {coast_distance_raster_path}"
+            )
+
+        with rasterio.open(coast_distance_raster_path) as src:
+            coast_dist_values = np.array(
+                [sample[0] for sample in src.sample(points)], dtype=np.float64
+            )
+    else:
+        coast_dist_values = None
+
+    # Create copies to modify
+    modified_vs30 = vs30.copy()
+    modified_stdv = stdv.copy()
+
+    # 1. Update standard deviation for specific groups
+    if hybrid:
+        for gid, factor in constants.HYBRID_SIGMA_REDUCTION_FACTORS.items():
+            mask = geology_ids == gid
+            if np.any(mask):
+                modified_stdv[mask] *= factor
+
+    # 2. Hybrid slope-based VS30 calculation
+    if hybrid:
+        # Prevent log10(0) or log10(negative) by capping at constants.MIN_SLOPE_FOR_LOG
+        safe_slope = np.maximum(slope_values, constants.MIN_SLOPE_FOR_LOG)
+        # Handle nodata values
+        safe_slope[slope_values == constants.NODATA_VALUE] = constants.MIN_SLOPE_FOR_LOG
+        safe_log_slope = np.log10(safe_slope)
+
+        for spec in constants.HYBRID_VS30_PARAMS:
+            gid = spec.gid
+            slope_limits = spec.slope_limits
+            vs30_limits_log10 = np.log10(np.array(spec.vs30_values))
+
+            # Skip ID 4 if mod6 is active (handled separately)
+            if gid == 4 and mod6:
+                continue
+
+            mask = geology_ids == gid
+            if np.any(mask):
+                interpolated_val = np.interp(
+                    safe_log_slope[mask], slope_limits, vs30_limits_log10
+                )
+                modified_vs30[mask] = 10**interpolated_val
+
+    # 3. Distance-based modification for alluvium (GID 4) and floodplain (GID 10)
+    if mod6 and coast_dist_values is not None:
+        apply_coastal_distance_modification(
+            modified_vs30,
+            geology_ids,
+            coast_dist_values,
+            gid=4,
+            dist_min=constants.HYBRID_MOD6_DIST_MIN,
+            dist_max=constants.HYBRID_MOD6_DIST_MAX,
+            vs30_min=constants.HYBRID_MOD6_VS30_MIN,
+            vs30_max=constants.HYBRID_MOD6_VS30_MAX,
+        )
+
+    if mod13 and coast_dist_values is not None:
+        apply_coastal_distance_modification(
+            modified_vs30,
+            geology_ids,
+            coast_dist_values,
+            gid=10,
+            dist_min=constants.HYBRID_MOD13_DIST_MIN,
+            dist_max=constants.HYBRID_MOD13_DIST_MAX,
+            vs30_min=constants.HYBRID_MOD13_VS30_MIN,
+            vs30_max=constants.HYBRID_MOD13_VS30_MAX,
+        )
+
+    return modified_vs30, modified_stdv
diff --git a/vs30/resources/categorical_vs30_mean_and_stddev/geology/geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/vs30/resources/categorical_vs30_mean_and_stddev/geology/geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..3d1777c6
--- /dev/null
+++ b/vs30/resources/categorical_vs30_mean_and_stddev/geology/geology_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id, mean_vs30_km_per_s, standard_deviation_vs30_km_per_s, description, comment
+0, -32767, -32767, "water", "not used | id 00 in Ahdi et al. (2017ab) | -32767 used as a placeholder"
+1, 162.892120019443, 0.301033220758108, "peat", "id 01 in Ahdi et al. (2017ab)"
+2, 272.512711921894, 0.280305955290694, "fill", "id 04 in Ahdi et al. (2017ab)"
+3, 199.502400319993, 0.438753673163297, "fluvial estuarine", "id 05 in Ahdi et al. (2017ab)"
+4, 271.050623687222, 0.243486579272276, "alluvium", "id 06 in Ahdi et al. (2017ab)"
+5, 326, 0.5, "lacustrine", "id 08 in Ahdi et al. (2017ab)"
+6, 204.373998521848, 0.232196981798137, "beach bar dune", "id 09 in Ahdi et al. (2017ab)"
+7, 246.6138371423, 0.344601218802256, "fan", "id 10 in Ahdi et al. (2017ab)"
+8, 472.749388885179, 0.354562104171167, "loess", "id 11 in Ahdi et al. (2017ab)"
+9, 399, 0.5, "outwash", "id 12 in Ahdi et al. (2017ab)"
+10, 197.472849225266, 0.202629769603115, "flood plain", "id 13 in Ahdi et al. (2017ab)"
+11, 453, 0.512, "moraine till", "id 14 in Ahdi et al. (2017ab)"
+12, 455, 0.545, "undifferentiated sediment", "id 15 in Ahdi et al. (2017ab)"
+13, 335.279503497185, 0.602886888230288, "terrace", "id 16 in Ahdi et al. (2017ab)"
+14, 635, 0.995, "volcanic", "id 17 in Ahdi et al. (2017ab)"
+15, 690.974348966211, 0.446036993981441, "crystalline", "id 18 in Ahdi et al. (2017ab)"
diff --git a/vs30/resources/categorical_vs30_mean_and_stddev/geology/geology_model_prior_mean_and_standard_deviation.csv b/vs30/resources/categorical_vs30_mean_and_stddev/geology/geology_model_prior_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..01f24c4b
--- /dev/null
+++ b/vs30/resources/categorical_vs30_mean_and_stddev/geology/geology_model_prior_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id, mean_vs30_km_per_s, standard_deviation_vs30_km_per_s, description, comment
+0, -32767, -32767, "water", "not used | id 00 in Ahdi et al. (2017ab) | -32767 used as a placeholder"
+1, 161, 0.522, "peat", "id 01 in Ahdi et al. (2017ab)"
+2, 198, 0.314, "fill", "id 04 in Ahdi et al. (2017ab)"
+3, 239, 0.867, "fluvial estuarine", "id 05 in Ahdi et al. (2017ab)"
+4, 323, 0.365, "alluvium", "id 06 in Ahdi et al. (2017ab)"
+5, 326, 0.135, "lacustrine", "id 08 in Ahdi et al. (2017ab)"
+6, 339, 0.647, "beach bar dune", "id 09 in Ahdi et al. (2017ab)"
+7, 360, 0.338, "fan", "id 10 in Ahdi et al. (2017ab)"
+8, 376, 0.380, "loess", "id 11 in Ahdi et al. (2017ab)"
+9, 399, 0.305, "outwash", "id 12 in Ahdi et al. (2017ab)"
+10, 448, 0.432, "flood plain", "id 13 in Ahdi et al. (2017ab)"
+11, 453, 0.512, "moraine till", "id 14 in Ahdi et al. (2017ab)"
+12, 455, 0.545, "undifferentiated sediment", "id 15 in Ahdi et al. (2017ab)"
+13, 458, 0.761, "terrace", "id 16 in Ahdi et al. (2017ab)"
+14, 635, 0.995, "volcanic", "id 17 in Ahdi et al. (2017ab)"
+15, 750, 0.641, "crystalline", "id 18 in Ahdi et al. (2017ab)"
diff --git a/vs30/resources/categorical_vs30_mean_and_stddev/terrain/terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv b/vs30/resources/categorical_vs30_mean_and_stddev/terrain/terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..55585f5e
--- /dev/null
+++ b/vs30/resources/categorical_vs30_mean_and_stddev/terrain/terrain_model_posterior_from_foster_2019_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id, mean_vs30_km_per_s, standard_deviation_vs30_km_per_s, description, comment
+1, 519, 0.5, "Well dissected alpine summits, mountains, etc.",
+2, 393, 0.5, "Large volcano, high block plateaus, etc.",
+3, 547, 0.5, "Well dissected, low mountains, etc.",
+4, 459, 0.5, "Volcanic fan, foot slope of high block plateaus, etc.",
+5, 323.504047697085, 0.407430975749267, "Dissected plateaus, etc.",
+6, 300.639538792274, 0.306594194335118, "Basalt lava plain, glaciated plateau, etc.",
+7, 535.786417756242, 0.380788655293195, "Moderately eroded mountains, lava flow, etc.",
+8, 514.970575501527, 0.380788655293195, "Desert alluvial slope, volcanic fan, etc.",
+9, 284.470950299338, 0.360555127546399, "Well eroded plain of weak rocks, etc.",
+10, 317.368444092771, 0.33166247903554, "Valley, till plain, etc.",
+11, 266.870166096091, 0.4, "Eroded plain of weak rocks, etc.",
+12, 297, 0.5, "Desert plain, delta plain, etc.",
+13, 216.62368022053, 0.254950975679639, "Incised terrace, etc.", "Prior mean and standard deviation were estimated by Kevin Foster"
+14, 241.80869962047, 0.307482445914323, "Eroded alluvial fan, till plain, etc.",
+15, 198.64481157494, 0.205256370217328, "Dune, incised terrace, etc.",
+16, 202.125866002814, 0.206820130727133, "Fluvial plain, alluvial fan, low-lying flat plains, etc.",
diff --git a/vs30/resources/categorical_vs30_mean_and_stddev/terrain/terrain_model_prior_mean_and_standard_deviation.csv b/vs30/resources/categorical_vs30_mean_and_stddev/terrain/terrain_model_prior_mean_and_standard_deviation.csv
new file mode 100644
index 00000000..f8cad379
--- /dev/null
+++ b/vs30/resources/categorical_vs30_mean_and_stddev/terrain/terrain_model_prior_mean_and_standard_deviation.csv
@@ -0,0 +1,17 @@
+id, mean_vs30_km_per_s, standard_deviation_vs30_km_per_s, description, comment
+1, 519, 0.3521, "Well dissected alpine summits, mountains, etc.",
+2, 393, 0.4161, "Large volcano, high block plateaus, etc.",
+3, 547, 0.4695, "Well dissected, low mountains, etc.",
+4, 459, 0.3540, "Volcanic fan, foot slope of high block plateaus, etc.",
+5, 402, 0.3136, "Dissected plateaus, etc.",
+6, 345, 0.2800, "Basalt lava plain, glaciated plateau, etc.",
+7, 388, 0.4161, "Moderately eroded mountains, lava flow, etc.",
+8, 374, 0.3249, "Desert alluvial slope, volcanic fan, etc.",
+9, 497, 0.3516, "Well eroded plain of weak rocks, etc.",
+10, 349, 0.2800, "Valley, till plain, etc.",
+11, 328, 0.2736, "Eroded plain of weak rocks, etc.",
+12, 297, 0.2931, "Desert plain, delta plain, etc.",
+13, 500, 0.5, "Incised terrace, etc.", "Mean and standard deviation estimated were estimated by Kevin Foster"
+14, 209, 0.1749, "Eroded alluvial fan, till plain, etc.",
+15, 363, 0.2800, "Dune, incised terrace, etc.",
+16, 246, 0.2206, "Fluvial plain, alluvial fan, low-lying flat plains, etc.",
diff --git a/vs30/resources/observations/README.md b/vs30/resources/observations/README.md
new file mode 100644
index 00000000..a9bc1db6
--- /dev/null
+++ b/vs30/resources/observations/README.md
@@ -0,0 +1,88 @@
+# Measured Vs30 Observational Data
+
+This directory contains prepared observational datasets used for Vs30 mapping. These datasets are created by the script `vs30/temp/export_measured_vs30.py` which consolidates and prepares data from multiple sources.
+
+## Dataset: `measured_vs30_independent_observations.csv`
+
+This file contains **independent observations** that are processed without spatial clustering in the Vs30 mapping pipeline. Each observation is treated individually when updating the categorical models.
+
+### Data Sources
+
+The dataset combines measurements from three sources:
+
+#### 1. McGann et al. (2015)
+- **Source**: `sites_load.load_mcgann_vs()`
+- **Description**: Surface wave measurements from the Canterbury region
+- **Count**: ~400 sites
+- **Source label**: `mcgann`
+
+#### 2. Wotherspoon et al. (2015)
+- **Source**: `sites_load.load_wotherspoon_vs()`
+- **Description**: Additional surface wave measurements from Canterbury
+- **Count**: ~40 sites
+- **Source label**: `wotherspoon`
+
+#### 3. Kaiser et al. (2017) - Filtered
+- **Source**: `sites_load.load_kaiseretal_vs()`
+- **Description**: Comprehensive Vs30 dataset for New Zealand
+- **Filtering criteria**: Sites are excluded if they meet ALL of these conditions:
+  - `q == 3` (quality flag indicating questionable data)
+  - `len(station) != 3` (station code is not exactly 3 characters)
+- **Rationale**: The filter removes low-quality Kaiseretal sites while preserving stations with 3-letter codes that have quality flag 3
+- **Count**: ~400 sites (filtered from ~460 total)
+- **Source label**: `kaiseretal`
+
+### Data Structure
+
+All datasets are standardized to the following CSV format:
+
+```csv
+easting,northing,vs30,uncertainty,source,station,q
+1576480.1104056418,5207643.269430228,224.85387,0.20000000298023224,mcgann,,
+1577001.152476283,5207261.8981841365,205.06209,0.20000000298023224,mcgann,,
+1933261.1379653357,5620565.339633845,150.0,0.2,kaiseretal,014A,2.0
+```
+
+#### Column Descriptions
+
+- **`easting`**: Easting coordinate in NZTM2000 projection (meters)
+- **`northing`**: Northing coordinate in NZTM2000 projection (meters)
+- **`vs30`**: Measured Vs30 value (m/s)
+- **`uncertainty`**: Measurement uncertainty/standard deviation (m/s)
+- **`source`**: Data source identifier (`mcgann`, `wotherspoon`, or `kaiseretal`)
+- **`station`**: Station identifier (primarily from Kaiseretal data)
+- **`q`**: Quality flag (primarily from Kaiseretal data, 1-3 scale where 1 is best)
+
+### Processing in Vs30 Pipeline
+
+This dataset is configured in `config.yaml` as:
+```yaml
+independent_observations_file: observations/measured_vs30_independent_observations.csv
+```
+
+**Processing characteristics**:
+- **No spatial clustering**: Each measurement is treated independently
+- **Direct model updating**: Each observation updates the categorical model at its exact location
+- **Bayesian integration**: Uses `update_with_independent_data()` function in the category module
+- **Output layers**: Contributes to `constants.COL_POSTERIOR_MEAN_INDEPENDENT` and related uncertainty layers (`constants.COL_POSTERIOR_STDV_INDEPENDENT`)
+
+### Quality Considerations
+
+- **McGann/Wotherspoon**: High-quality surface wave measurements from specific studies
+- **Kaiseretal filtering**: Removes potentially unreliable measurements while preserving known good stations
+- **Uncertainty**: Most measurements have uncertainty ~0.2 m/s, though some Kaiseretal sites may have different values
+- **Spatial coverage**: Primarily focused on Canterbury region with some nationwide Kaiseretal coverage
+
+### Related Files
+
+- **`measured_vs30_independent_observations_unfiltered.csv`**: Same data sources but includes ALL Kaiseretal sites (not used in main pipeline)
+- **`measured_vs30_cpt.csv`**: CPT-derived measurements processed with spatial clustering
+
+### Generation
+
+This file is generated by running:
+```bash
+python vs30/temp/export_measured_vs30.py
+```
+
+The script loads data from the raw sources, applies filtering, adds source labels, and exports to CSV format.
\ No newline at end of file
diff --git a/vs30/resources/observations/measured_vs30_cpt.csv b/vs30/resources/observations/measured_vs30_cpt.csv
new file mode 100644
index 00000000..4bd0346b
--- /dev/null
+++ b/vs30/resources/observations/measured_vs30_cpt.csv
@@ -0,0 +1,35707 @@
+index,easting,northing,vs30,uncertainty,source
+0,1.5764672e+06,5.1812625e+06,184.62537,0.5,cpt
+1,1.5706816e+06,5.180753e+06,166.26828,0.5,cpt
+3,1.573105e+06,5.17927e+06,159.10588,0.5,cpt
+4,1.57315e+06,5.179295e+06,187.60077,0.5,cpt
+5,1.57316e+06,5.17941e+06,187.29077,0.5,cpt
+6,1.5741192e+06,5.177888e+06,186.3933,0.5,cpt
+7,1.5704692e+06,5.187186e+06,157.44801,0.5,cpt
+8,1.576583e+06,5.182912e+06,200.3916,0.5,cpt
+9,1.5660296e+06,5.1758925e+06,178.41258,0.5,cpt
+10,1.87406e+06,5.8171735e+06,129.23026,0.5,cpt
+12,1.7926892e+06,5.8265325e+06,165.53217,0.5,cpt
+13,1.571192e+06,5.186865e+06,177.85574,0.5,cpt
+14,1.570698e+06,5.186853e+06,210.96709,0.5,cpt
+16,1.7479919e+06,5.925623e+06,131.95085,0.5,cpt
+17,1.5740104e+06,5.1789855e+06,186.7924,0.5,cpt
+18,1.5697e+06,5.188217e+06,227.97533,0.5,cpt
+19,1.5737195e+06,5.1820915e+06,190.13925,0.5,cpt
+20,1.5726218e+06,5.1811995e+06,161.07153,0.5,cpt
+21,1.5760774e+06,5.1753925e+06,174.45561,0.5,cpt
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+23,1.8042569e+06,5.8103075e+06,139.20927,0.5,cpt
+24,1.8046992e+06,5.811231e+06,149.09535,0.5,cpt
+25,1.8044142e+06,5.8126595e+06,187.63644,0.5,cpt
+26,1.5711419e+06,5.18656e+06,168.85027,0.5,cpt
+27,1.5590702e+06,5.16704e+06,185.70038,0.5,cpt
+28,1.5768201e+06,5.183809e+06,206.72958,0.5,cpt
+29,1.88066e+06,5.826927e+06,165.50546,0.5,cpt
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+31,1.5770549e+06,5.183688e+06,191.95871,0.5,cpt
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diff --git a/vs30/resources/observations/measured_vs30_independent_observations.csv b/vs30/resources/observations/measured_vs30_independent_observations.csv
new file mode 100644
index 00000000..4331a732
--- /dev/null
+++ b/vs30/resources/observations/measured_vs30_independent_observations.csv
@@ -0,0 +1,460 @@
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diff --git a/vs30/resources/observations/measured_vs30_independent_observations_unfiltered.csv b/vs30/resources/observations/measured_vs30_independent_observations_unfiltered.csv
new file mode 100644
index 00000000..0431bcae
--- /dev/null
+++ b/vs30/resources/observations/measured_vs30_independent_observations_unfiltered.csv
@@ -0,0 +1,822 @@
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diff --git a/vs30/resources/observations/minimal_independent_observations.csv b/vs30/resources/observations/minimal_independent_observations.csv
new file mode 100644
index 00000000..8c161015
--- /dev/null
+++ b/vs30/resources/observations/minimal_independent_observations.csv
@@ -0,0 +1,41 @@
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diff --git a/vs30/resources/observations/test_cpt_observations.csv b/vs30/resources/observations/test_cpt_observations.csv
new file mode 100644
index 00000000..d49e8dbc
--- /dev/null
+++ b/vs30/resources/observations/test_cpt_observations.csv
@@ -0,0 +1,19237 @@
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diff --git a/vs30/resources/observations/test_independent_observations.csv b/vs30/resources/observations/test_independent_observations.csv
new file mode 100644
index 00000000..6420366b
--- /dev/null
+++ b/vs30/resources/observations/test_independent_observations.csv
@@ -0,0 +1,253 @@
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diff --git a/vs30/sites_cluster.py b/vs30/sites_cluster.py
deleted file mode 100644
index c01bb865..00000000
--- a/vs30/sites_cluster.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""
-Handles distance based clustering of measured site locations.
-Reduces influence of locations with more measured sites than those more sparse.
-"""
-
-import numpy as np
-from sklearn.cluster import DBSCAN
-
-from vs30.model import ID_NODATA
-
-
-def cluster(sites, letter, min_group=5, eps=15000, nproc=-1):
-    """
-    Sort sites into clusters spatially.
-    letter: which id? "t"(id) for terrain or "g"(id) for geology
-    min_group: the minimum group size
-    eps: (metres) how far points are to be considered a different cluster
-    nproc: -1 to detect available cores
-    """
-
-    features = np.column_stack((sites.easting.values, sites.northing.values))
-    # default not a member of any cluster (-1)
-    sites[f"{letter}cluster"] = -1
-    model_ids = sites[f"{letter}id"].values
-    ids = np.array(sorted(set(model_ids)))
-    ids = ids[ids != ID_NODATA].astype(np.int)
-
-    for i in ids:
-        subset = features[model_ids == i]
-        if subset.shape[0] < min_group:
-            # can't form any groups
-            continue
-
-        dbscan = DBSCAN(eps=eps, min_samples=min_group, n_jobs=nproc)
-        dbscan.fit(subset)
-
-        # save labels
-        sites.loc[model_ids == i, f"{letter}cluster"] = dbscan.labels_
-
-    return sites
diff --git a/vs30/sites_load.py b/vs30/sites_load.py
deleted file mode 100644
index affd14ba..00000000
--- a/vs30/sites_load.py
+++ /dev/null
@@ -1,190 +0,0 @@
-"""
-Loads measured sites.
-"""
-
-from math import sqrt
-import os
-
-import numpy as np
-import pandas as pd
-from pyproj import Transformer
-from scipy.spatial import distance_matrix
-
-data = os.path.join(os.path.dirname(__file__), "data")
-DATA_CPT = os.path.join(data, "cptvs30.ssv")
-DATA_KAISERETAL = os.path.join(data, "20170817_vs_allNZ_duplicatesCulled.ll")
-DATA_MCGANN = os.path.join(data, "McGann_cptVs30data.csv")
-DATA_WOTHERSPOON = os.path.join(data, "Characterised Vs30 Canterbury_June2017_KFed.csv")
-
-wgs2nztm = Transformer.from_crs(4326, 2193, always_xy=True)
-nzmg2nztm = Transformer.from_crs(27200, 2193, always_xy=True)
-
-
-def downsample_mcg(sites_df, res=1000):
-    """
-    Resample McGann points on 1km grid.
-    res: grid resolution (m)
-    """
-
-    max_dist = sqrt(res**2 * 2) / 2
-    x = sites_df["easting"].values
-    y = sites_df["northing"].values
-
-    # trying to copy R logic - extents
-    # works for this dataset
-    xmin = min(x)
-    nx = round((max(x) - xmin) / res)
-    if ((max(x) - xmin) / res) % 1 < 0.5:
-        # this is run
-        xmin += res / 2.0
-        xmax = (nx - 1) * res + xmin
-    else:
-        xmax = nx * res + xmin
-    ymax = max(y) - res
-    ny = round((ymax - min(y)) / res) + 1
-    if ((ymax - min(x)) / res) % 1 < 0.5:
-        ymax -= res / 2.0
-        ymin = ymax - (ny - 2) * res
-    else:
-        # this is run
-        ymax += res / 2.0
-        ymin = ymax - (ny - 1) * res
-
-    # coarse grid
-    grid_x = np.linspace(xmin, xmax, nx)
-    grid_y = np.linspace(ymin, ymax, ny)[::-1]
-    grid = np.dstack(np.meshgrid(grid_x, grid_y)).reshape(-1, 2)
-
-    # distances from coarse grid, nearest neighbor
-    dist = distance_matrix(grid, np.dstack((x, y))[0])
-    nn = np.argmin(dist, axis=1)
-    # cut out if no points within search area
-    nn = nn[dist[np.arange(nn.size), nn] <= max_dist]
-
-    # remove duplicate points from downsample algorithm
-    mcg = sites_df.iloc[nn]
-    return mcg[~mcg.duplicated()]
-
-
-def load_vs(source="original"):
-    """
-    Load measured sites. Either newer CPT based or paper version using 3 sources.
-    """
-
-    if source == "original":
-        # load each Vs data source
-        mcgann = load_mcgann_vs()
-        wotherspoon = load_wotherspoon_vs()
-        kaiseretal = load_kaiseretal_vs()
-
-        # remove Kaiser Q3 unless station name is 3 chars long (broadband seismometers)
-        kaiseretal = kaiseretal[
-            (kaiseretal.q != 3) | (kaiseretal.station.str.len() == 3)
-        ]
-
-        return pd.concat([mcgann, wotherspoon, kaiseretal], ignore_index=True)
-
-    if source == "cpt":
-        return load_cpt_vs()
-
-
-def load_cpt_vs():
-    """
-    Newer collection of Vs30 from CPT data.
-    """
-    cpt = pd.read_csv(
-        DATA_CPT,
-        sep=" ",
-        usecols=[0, 1, 2],
-        names=["easting", "northing", "vs30"],
-        skiprows=1,
-        engine="c",
-        dtype=np.float32,
-    )
-    # remove rows with no vs30 value
-    cpt = cpt[~np.isnan(cpt.vs30)].reset_index()
-
-    cpt["uncertainty"] = np.float32(0.5)
-
-    return cpt
-
-
-def load_mcgann_vs(downsample=True):
-    """
-    McGann Vs30 map
-    (McGann, submitted, 2016 SDEE
-    "Development of regional Vs30 Model and typical profiles... Christchurch CPT correlation")
-    """
-
-    # downsampling was originally done on the NZMG grid
-    mcgann = pd.read_csv(
-        DATA_MCGANN,
-        usecols=[3, 4, 7] if downsample else [5, 6, 7],
-        names=["easting", "northing", "vs30"],
-        skiprows=1,
-        engine="c",
-        dtype=np.float32,
-    )
-    if downsample:
-        mcgann = downsample_mcg(mcgann)
-        mcgann["easting"], mcgann["northing"] = nzmg2nztm.transform(
-            mcgann["easting"].values, mcgann["northing"].values
-        )
-
-    mcgann["uncertainty"] = np.float32(0.2)
-
-    return mcgann
-
-
-def load_wotherspoon_vs():
-    """
-    Internal communication with Wotherspoon: Characterised Vs30
-    """
-
-    wotherspoon = pd.read_csv(
-        DATA_WOTHERSPOON,
-        sep="\t",
-        usecols=[2, 3, 4],
-        names=["northing", "easting", "vs30"],
-        skiprows=1,
-        engine="c",
-        dtype=np.float32,
-    )
-    wotherspoon["easting"], wotherspoon["northing"] = wgs2nztm.transform(
-        wotherspoon["easting"].values, wotherspoon["northing"].values
-    )
-
-    wotherspoon["uncertainty"] = np.float32(0.2)
-
-    return wotherspoon
-
-
-def load_kaiseretal_vs():
-    """
-    Values from Kaiser et al. (2017)
-    """
-
-    # with removed duplicate points
-    kaiseretal = pd.read_csv(
-        DATA_KAISERETAL,
-        usecols=[0, 1, 2, 3, 4],
-        names=["station", "easting", "northing", "vs30", "q"],
-        skiprows=5,
-        engine="c",
-        dtype={"easting": np.float32, "northing": np.float32, "vs30": np.float32},
-        converters={"q": lambda text: int(text.strip()[2]), "station": str.strip},
-    )
-    kaiseretal["easting"], kaiseretal["northing"] = wgs2nztm.transform(
-        kaiseretal["easting"].values, kaiseretal["northing"].values
-    )
-
-    # [Q1, Q2 and Q3] correspond to approximate uncertainties
-    # of <10%, 10-20% and >20% respectively
-    # 10% : ln(1.1) ~= 0.1
-    # 20% : ln(1.2) ~= 0.2
-    # 50% : ln(1.5) ~= 0.5
-    kaiseretal["uncertainty"] = np.where(
-        kaiseretal["q"] == 3, 0.5, kaiseretal["q"] / 10
-    )
-
-    return kaiseretal
diff --git a/vs30/spatial.py b/vs30/spatial.py
new file mode 100644
index 00000000..8aa0e746
--- /dev/null
+++ b/vs30/spatial.py
@@ -0,0 +1,1447 @@
+"""Multivariate Normal (MVN) distribution-based spatial adjustment of Vs30 using nearby observations."""
+
+import logging
+import multiprocessing as mp
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import rasterio
+import scipy
+import sklearn.cluster
+from tqdm import tqdm
+
+from vs30 import category, constants, raster, utils
+
+# Use spawn context to avoid GDAL fork issues
+_spawn_context = mp.get_context("spawn")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ObservationData:
+    """
+    Bundled observation data for spatial processing.
+
+    Attributes
+    ----------
+    locations : ndarray
+        (n_obs, 2) array of [easting, northing] coordinates.
+    vs30 : ndarray
+        (n_obs,) measured Vs30 values.
+    model_vs30 : ndarray
+        (n_obs,) model Vs30 at observation locations.
+    model_stdv : ndarray
+        (n_obs,) model standard deviation at observation locations.
+    residuals : ndarray
+        (n_obs,) log residuals: log(vs30 / model_vs30).
+    omega : ndarray
+        (n_obs,) noise weights (if noisy=True).
+    uncertainty : ndarray
+        (n_obs,) observation uncertainties.
+    cluster_labels : ndarray or None
+        (n_obs,) cluster labels from DBSCAN, -1 = unclustered.
+    """
+
+    locations: np.ndarray
+    vs30: np.ndarray
+    model_vs30: np.ndarray
+    model_stdv: np.ndarray
+    residuals: np.ndarray
+    omega: np.ndarray
+    uncertainty: np.ndarray
+    cluster_labels: np.ndarray | None = None
+
+    @classmethod
+    def empty(cls) -> "ObservationData":
+        """
+        Create an empty ObservationData object with zero observations.
+
+        Returns
+        -------
+        ObservationData
+            An ObservationData instance with zero-length arrays.
+        """
+        return cls(
+            locations=np.empty((0, 2)),
+            vs30=np.empty(0),
+            model_vs30=np.empty(0),
+            model_stdv=np.empty(0),
+            residuals=np.empty(0),
+            omega=np.empty(0),
+            uncertainty=np.empty(0),
+            cluster_labels=None,
+        )
+
+
+@dataclass
+class PixelData:
+    """
+    Data for a single pixel.
+
+    Attributes
+    ----------
+    location : ndarray
+        [easting, northing] coordinates.
+    vs30 : float
+        Prior Vs30 value.
+    stdv : float
+        Prior standard deviation value.
+    index : int
+        Flat index in the raster.
+    """
+
+    location: np.ndarray
+    vs30: float
+    stdv: float
+    index: int
+
+
+@dataclass
+class SpatialAdjustmentResult:
+    """
+    Result of an MVN update for a single pixel.
+
+    Attributes
+    ----------
+    updated_vs30 : float
+        Updated Vs30 value after spatial adjustment.
+    updated_stdv : float
+        Updated standard deviation after spatial adjustment.
+    n_observations_used : int
+        Number of observations used in the MVN conditioning.
+    min_distance : float
+        Distance (meters) to the nearest observation used.
+    pixel_index : int
+        Flat index of this pixel in the raster.
+    """
+
+    updated_vs30: float
+    updated_stdv: float
+    n_observations_used: int
+    min_distance: float
+    pixel_index: int
+
+
+@dataclass
+class RasterData:
+    """
+    Raster data and metadata.
+
+    Attributes
+    ----------
+    vs30 : ndarray
+        Band 1: Vs30 mean values (2D array).
+    stdv : ndarray
+        Band 2: Vs30 standard deviation values (2D array).
+    transform : rasterio.transform.Affine
+        Affine transformation for coordinate conversion.
+    crs : rasterio.crs.CRS
+        Coordinate reference system.
+    nodata : float or None
+        No-data value used in the raster.
+    valid_mask : ndarray
+        Boolean mask of non-nodata pixels (2D array).
+    valid_flat_indices : ndarray
+        Flat indices of non-nodata pixels (1D array).
+    """
+
+    vs30: np.ndarray
+    stdv: np.ndarray
+    transform: rasterio.transform.Affine
+    crs: rasterio.crs.CRS
+    nodata: float | None
+    valid_mask: np.ndarray
+    valid_flat_indices: np.ndarray
+
+    @classmethod
+    def from_file(cls, path: Path) -> "RasterData":
+        """
+        Load 2-band VS30 raster.
+
+        Parameters
+        ----------
+        path : Path
+            Path to the 2-band raster file (band 1: mean Vs30, band 2: standard deviation of Vs30).
+
+        Returns
+        -------
+        RasterData
+            Loaded raster data with valid pixel mask.
+        """
+        with rasterio.open(path) as src:
+            vs30 = src.read(1)
+            stdv = src.read(2)
+            transform = src.transform
+            crs = src.crs
+            nodata = src.nodata
+
+            # Create mask of valid pixels (not nodata, not nan, and positive)
+            valid_mask = (
+                (vs30 != nodata)
+                & (~np.isnan(vs30))
+                & (~np.isnan(stdv))
+                & (vs30 > 0)
+                & (stdv > 0)
+            )
+            valid_flat_indices = np.where(valid_mask.flatten())[0]
+
+            return cls(
+                vs30=vs30,
+                stdv=stdv,
+                transform=transform,
+                crs=crs,
+                nodata=nodata,
+                valid_mask=valid_mask,
+                valid_flat_indices=valid_flat_indices,
+            )
+
+    def get_coordinates(self) -> np.ndarray:
+        """
+        Get coordinates for all valid pixels using GDAL affine transform.
+
+        Returns
+        -------
+        ndarray
+            (N_valid, 2) array of [easting, northing] coordinates.
+        """
+        valid_rows, valid_cols = np.where(self.valid_mask)
+
+        # Rasterio Affine: [a, b, c, d, e, f] = [x_scale, x_shear, x_origin, y_shear, y_scale, y_origin]
+        x_scale = self.transform[0]
+        x_origin = self.transform[2]
+        y_scale = self.transform[4]
+        y_origin = self.transform[5]
+
+        # Pixel centers: add offset to row/col indices (following legacy implementation)
+        cols_center = valid_cols.astype(float) + constants.PIXEL_CENTER_OFFSET
+        rows_center = valid_rows.astype(float) + constants.PIXEL_CENTER_OFFSET
+
+        xs = x_origin + cols_center * x_scale
+        ys = y_origin + rows_center * y_scale
+
+        return np.column_stack((xs, ys)).astype(np.float32)
+
+    def write_updated(
+        self, path: Path, updated_vs30: np.ndarray, updated_stdv: np.ndarray
+    ) -> None:
+        """
+        Write updated raster to file.
+
+        Parameters
+        ----------
+        path : Path
+            Output file path.
+        updated_vs30 : ndarray
+            Updated vs30 values (same shape as self.vs30).
+        updated_stdv : ndarray
+            Updated stdv values (same shape as self.stdv).
+        """
+        # Write using rasterio with compression
+        with rasterio.open(
+            path,
+            "w",
+            driver=constants.GEOTIFF_DRIVER,
+            height=self.vs30.shape[0],
+            width=self.vs30.shape[1],
+            count=2,
+            dtype=self.vs30.dtype,
+            crs=self.crs,
+            transform=self.transform,
+            nodata=self.nodata,
+            compress=constants.GEOTIFF_COMPRESSION,
+            tiled=constants.GEOTIFF_TILED,
+            bigtiff=constants.GEOTIFF_BIGTIFF,
+        ) as dst:
+            dst.write(updated_vs30, 1)
+            dst.write(updated_stdv, 2)
+
+
+@dataclass
+class BoundingBoxResult:
+    """
+    Result of bounding box search for affected pixels.
+
+    Attributes
+    ----------
+    mask : ndarray
+        Boolean mask of pixels in any observation's bounding box.
+    obs_to_grid_indices : list[ndarray]
+        For each observation, flat indices of pixels in its bounding box.
+    n_affected_pixels : int
+        Total number of pixels affected by at least one observation.
+    """
+
+    mask: np.ndarray
+    obs_to_grid_indices: list[np.ndarray]
+    n_affected_pixels: int
+
+
+def validate_raster_data(raster_data: RasterData) -> None:
+    """
+    Validate raster data before processing.
+
+    Parameters
+    ----------
+    raster_data : RasterData
+        Raster data object.
+
+    Raises
+    ------
+    ValueError
+        If raster data is invalid.
+    """
+    if raster_data.vs30.shape != raster_data.stdv.shape:
+        raise ValueError("Band shapes must match")
+    if not np.all(np.isfinite(raster_data.vs30[raster_data.valid_mask])):
+        raise ValueError("Valid pixels must be finite")
+    if not np.all(raster_data.vs30[raster_data.valid_mask] > 0):
+        raise ValueError("Vs30 must be positive")
+    if not np.all(raster_data.stdv[raster_data.valid_mask] > 0):
+        raise ValueError("Stdv must be positive")
+
+
+def validate_observations(observations: pd.DataFrame) -> None:
+    """
+    Validate observation data.
+
+    Parameters
+    ----------
+    observations : DataFrame
+        Observation data.
+
+    Raises
+    ------
+    ValueError
+        If observation data is invalid.
+    """
+    missing = [
+        col
+        for col in constants.REQUIRED_OBSERVATION_COLUMNS
+        if col not in observations.columns
+    ]
+    if missing:
+        raise ValueError(f"Missing required columns: {missing}")
+    if not np.all(observations[constants.COL_VS30] > 0):
+        raise ValueError("Vs30 must be positive")
+    if not np.all(observations[constants.COL_UNCERTAINTY] > 0):
+        raise ValueError("Uncertainty must be positive")
+
+
+def prepare_observation_data(
+    observations: pd.DataFrame,
+    raster_data: RasterData,
+    updated_model_table: np.ndarray,
+    model_type: constants.ModelType,
+    output_dir: Path,
+    noisy: bool,
+) -> ObservationData:
+    """
+    Prepare observation data for MVN processing.
+
+    For geology models, hybrid modifications (slope and coastal distance)
+    are applied to model values at observation locations before computing
+    residuals.
+
+    Parameters
+    ----------
+    observations : DataFrame
+        Observations with vs30, uncertainty, easting, northing columns.
+    raster_data : RasterData
+        Raster data object (used for transform/profile info).
+    updated_model_table : ndarray
+        Updated model table (n_categories, 2) array of [vs30, stdv].
+    model_type : constants.ModelType
+        Model type (ModelType.GEOLOGY or ModelType.TERRAIN).
+    output_dir : Path
+        Output directory for intermediate rasters (slope, coast distance).
+    noisy : bool
+        Whether to apply noise weighting.
+
+    Returns
+    -------
+    ObservationData
+        Prepared observation data object.
+    """
+    # Get observation locations
+    obs_locs = observations[[constants.COL_EASTING, constants.COL_NORTHING]].values
+
+    # Interpolate model values at observation locations
+    if model_type == constants.ModelType.GEOLOGY:
+        model_ids = category.assign_to_category_geology(obs_locs)
+    elif model_type == constants.ModelType.TERRAIN:
+        model_ids = category.assign_to_category_terrain(obs_locs)
+    else:
+        raise ValueError(f"Unknown model type: {model_type}")
+
+    # Get model vs30 and stdv from updated model table
+    # Model IDs are 1-indexed in the raster, but 0-indexed in the model table
+    # constants.RASTER_ID_NODATA_VALUE is 255, valid IDs are 1-15 for geology, 1-16 for terrain
+    valid_mask = (
+        (model_ids != constants.RASTER_ID_NODATA_VALUE)
+        & (model_ids > 0)
+        & (model_ids <= len(updated_model_table))
+    )
+    model_vs30 = np.full(len(observations), np.nan)
+    model_stdv = np.full(len(observations), np.nan)
+
+    # Convert 1-indexed model IDs to 0-indexed array indices
+    valid_model_ids = model_ids[valid_mask] - 1
+    model_vs30[valid_mask] = updated_model_table[valid_model_ids, 0]
+    model_stdv[valid_mask] = updated_model_table[valid_model_ids, 1]
+
+    # Filter out observations where model values are NaN/NoData
+    valid_obs_mask = ~np.isnan(model_vs30) & ~np.isnan(model_stdv)
+    obs_locs = obs_locs[valid_obs_mask]
+    vs30_obs = observations[constants.COL_VS30].values[valid_obs_mask]
+    model_vs30 = model_vs30[valid_obs_mask]
+    model_stdv = model_stdv[valid_obs_mask]
+    uncertainty = observations[constants.COL_UNCERTAINTY].values[valid_obs_mask]
+
+    # Calculate log residuals
+    # For geology, we must apply hybrid modifications to model values at observation points
+    if model_type == constants.ModelType.GEOLOGY:
+        # 1. Get slope and coast distance at points
+        # Use existing rasters in output_dir if possible, otherwise create temporary ones
+        slope_path = output_dir / constants.SLOPE_RASTER_FILENAME
+        coast_path = output_dir / constants.COAST_DISTANCE_RASTER_FILENAME
+
+        profile = {
+            "transform": raster_data.transform,
+            "width": raster_data.vs30.shape[1],
+            "height": raster_data.vs30.shape[0],
+            "crs": rasterio.crs.CRS.from_string(constants.NZTM_CRS),
+        }
+
+        if not slope_path.exists():
+            raster.create_slope_raster(slope_path, profile)
+        if not coast_path.exists():
+            raster.create_coast_distance_raster(coast_path, profile)
+
+        # Sample rasters at observation locations
+        with rasterio.open(slope_path) as src:
+            slope_obs = np.array([v[0] for v in src.sample(obs_locs)])
+        with rasterio.open(coast_path) as src:
+            coast_obs = np.array([v[0] for v in src.sample(obs_locs)])
+
+        # Apply modifications to model_vs30 and model_stdv at points
+        model_vs30, model_stdv = raster.apply_hybrid_geology_modifications(
+            model_vs30,
+            model_stdv,
+            model_ids[valid_obs_mask],
+            slope_obs,
+            coast_obs,
+        )
+
+    residuals = np.log(vs30_obs / model_vs30)
+
+    # Apply noise weighting (if noisy=True)
+    if noisy:
+        omega = np.sqrt(model_stdv**2 / (model_stdv**2 + uncertainty**2))
+        residuals *= omega
+    else:
+        omega = np.ones(len(residuals))  # Defaults to float64
+
+    return ObservationData(
+        locations=obs_locs,
+        vs30=vs30_obs,
+        model_vs30=model_vs30,
+        model_stdv=model_stdv,
+        residuals=residuals,
+        omega=omega,
+        uncertainty=uncertainty,
+    )
+
+
+def grid_points_in_bbox(
+    grid_locs: np.ndarray,
+    obs_eastings_min: np.ndarray,
+    obs_eastings_max: np.ndarray,
+    obs_northings_min: np.ndarray,
+    obs_northings_max: np.ndarray,
+    start_grid_idx: int = 0,
+) -> tuple[np.ndarray, list[np.ndarray]]:
+    """
+    Find grid points within bounding boxes of observations using fully vectorized NumPy.
+
+    Uses broadcasting to compute all observation-grid pairs simultaneously.
+    Returns collapsed mask and per-observation grid indices.
+
+    Parameters
+    ----------
+    grid_locs : array_like, shape (M, 2)
+        Grid point coordinates as (easting, northing) in NZTM.
+    obs_eastings_min : ndarray, shape (N, 1)
+        Precomputed obs_eastings - max_dist.
+    obs_eastings_max : ndarray, shape (N, 1)
+        Precomputed obs_eastings + max_dist.
+    obs_northings_min : ndarray, shape (N, 1)
+        Precomputed obs_northings - max_dist.
+    obs_northings_max : ndarray, shape (N, 1)
+        Precomputed obs_northings + max_dist.
+    start_grid_idx : int, optional
+        Starting index of grid_locs in the full grid (for offsetting indices).
+        Default is 0.
+
+    Returns
+    -------
+    chunk_mask : ndarray, shape (M,), dtype=bool
+        Boolean array indicating which grid points in this chunk are affected
+        by any observation (collapsed with np.any(axis=0)).
+    obs_to_grid_indices : list of ndarray
+        List of length n_obs. Each element is an array of grid point indices
+        (in the full grid) that are within that observation's bounding box.
+    """
+    # Extract coordinates
+    grid_eastings = grid_locs[:, 0]  # (n_grid,)
+    grid_northings = grid_locs[:, 1]  # (n_grid,)
+
+    # Vectorized bounding box check using broadcasting
+    # (n_obs, 1) operation against (n_grid,) -> (n_obs, n_grid)
+    # Checks: for each observation, which grid points are in its bounding box
+    in_bbox = (
+        (grid_eastings >= obs_eastings_min)
+        & (grid_eastings <= obs_eastings_max)
+        & (grid_northings >= obs_northings_min)
+        & (grid_northings <= obs_northings_max)
+    )
+
+    # Collapse to single mask: which grid points are affected by any observation
+    chunk_mask = np.any(in_bbox, axis=0)
+
+    # For each observation, get the grid point indices within its bounding box
+    # in_bbox shape: (n_obs, n_grid_chunk)
+    n_obs = in_bbox.shape[0]
+    obs_to_grid_indices = []
+
+    for obs_idx in range(n_obs):
+        # Get grid indices within this observation's bounding box
+        grid_indices_in_chunk = np.where(in_bbox[obs_idx])[0]
+        # Convert to full grid indices by adding start_grid_idx
+        full_grid_indices = grid_indices_in_chunk + start_grid_idx
+        obs_to_grid_indices.append(full_grid_indices)
+
+    return chunk_mask, obs_to_grid_indices
+
+
+def calculate_chunk_size(n_obs: int, max_spatial_boolean_array_memory_gb: float) -> int:
+    """
+    Calculate the max grid points per chunk given a memory budget for the mask of grid points inside observation bounding boxes.
+
+    Parameters
+    ----------
+    n_obs : int
+        Number of observations.
+    max_spatial_boolean_array_memory_gb : float
+        Maximum memory in GB allocated for spatial boolean arrays during chunked processing.
+
+    Returns
+    -------
+    int
+        Maximum number of grid points per chunk.
+    """
+    memory_per_chunk_bytes = max_spatial_boolean_array_memory_gb * 1024 * 1024 * 1024
+    chunk_size = int(memory_per_chunk_bytes / n_obs)
+    # Ensure at least 1 grid point per chunk.
+    return max(1, chunk_size)
+
+
+def process_bbox_chunk(args: tuple) -> tuple[int, np.ndarray, list[np.ndarray]]:
+    """
+    Worker function for parallel bounding box processing.
+
+    Processes a single chunk of grid points to find which are affected by observations.
+
+    Parameters
+    ----------
+    args : tuple
+        (chunk_idx, grid_locs_chunk, start_idx, obs_bounds)
+        where obs_bounds is (obs_eastings_min, obs_eastings_max,
+                            obs_northings_min, obs_northings_max)
+
+    Returns
+    -------
+    tuple
+        (chunk_idx, chunk_mask, obs_to_grid_indices)
+    """
+    chunk_idx, grid_locs_chunk, start_idx, obs_bounds = args
+    obs_eastings_min, obs_eastings_max, obs_northings_min, obs_northings_max = (
+        obs_bounds
+    )
+
+    chunk_mask, obs_to_grid_indices = grid_points_in_bbox(
+        grid_locs=grid_locs_chunk,
+        obs_eastings_min=obs_eastings_min,
+        obs_eastings_max=obs_eastings_max,
+        obs_northings_min=obs_northings_min,
+        obs_northings_max=obs_northings_max,
+        start_grid_idx=start_idx,
+    )
+
+    return chunk_idx, chunk_mask, obs_to_grid_indices
+
+
+def build_covariance_matrix(
+    pixel: PixelData,
+    selected_observations: ObservationData,
+    model_type: constants.ModelType,
+    noisy: bool = False,
+    cov_reduc: float = constants.COV_REDUC,
+) -> np.ndarray:
+    """
+    Build covariance matrix through clear pipeline of steps.
+
+    Parameters
+    ----------
+    pixel : PixelData
+        Pixel data for the pixel being updated.
+    selected_observations : ObservationData
+        Selected observations for this pixel.
+    model_type : constants.ModelType
+        Model type (ModelType.GEOLOGY or ModelType.TERRAIN).
+    noisy : bool, optional
+        Whether to apply noise weighting based on observation uncertainty.
+    cov_reduc : float, optional
+        Covariance reduction factor for dissimilar Vs30 values.
+
+    Returns
+    -------
+    ndarray
+        Covariance matrix (n_selected_obs + 1, n_selected_obs + 1).
+        First row/column is for the pixel, rest are for observations.
+    """
+
+    # Step 1: Compute Euclidean distance matrix
+    all_points = np.vstack([pixel.location, selected_observations.locations]).astype(
+        np.float64
+    )
+    distance_matrix = scipy.spatial.distance.cdist(
+        all_points, all_points, metric="euclidean"
+    )
+
+    # Step 2: Apply correlation function
+    corr = utils.correlation_function(distance_matrix, constants.PHI[model_type])
+
+    # Step 3: Scale by standard deviations
+    stdvs = np.insert(selected_observations.model_stdv, 0, pixel.stdv)
+    cov = corr * np.outer(stdvs, stdvs)
+
+    # Step 4: Apply noise weighting (if enabled)
+    if noisy:
+        omega = np.insert(selected_observations.omega, 0, 1.0)
+        omega_matrix = np.outer(omega, omega)
+        np.fill_diagonal(omega_matrix, 1.0)
+        cov *= omega_matrix
+
+    # Step 5: Apply covariance reduction (if enabled)
+    if cov_reduc > 0:
+        log_vs30s = np.insert(
+            np.log(selected_observations.model_vs30), 0, np.log(pixel.vs30)
+        )
+        log_dist_matrix = np.abs(log_vs30s[:, np.newaxis] - log_vs30s)
+        cov *= np.exp(-cov_reduc * log_dist_matrix)
+
+    return cov
+
+
+def select_observations_for_pixel(
+    pixel: PixelData,
+    obs_data: ObservationData,
+    max_dist_m: float = constants.MAX_DIST_M,
+    max_points: int = constants.MAX_POINTS,
+) -> ObservationData:
+    """
+    Select observations for a pixel using distance filtering.
+
+    Uses accurate distance-based selection to find the closest observations
+    within the maximum distance limit.
+
+    Parameters
+    ----------
+    pixel : PixelData
+        Pixel data.
+    obs_data : ObservationData
+        Full observation data.
+    max_dist_m : float
+        Maximum distance in meters to consider observations.
+    max_points : int
+        Maximum number of observations to select.
+
+    Returns
+    -------
+    ObservationData
+        Selected observations (subset of obs_data).
+    """
+    # Calculate distances from pixel to all observations
+    distances = np.sqrt(np.sum((obs_data.locations - pixel.location) ** 2, axis=1))
+
+    # Select observations using distance-based filtering
+    max_points_i = min(max_points, len(distances)) - 1
+    if max_points_i < 0:
+        return ObservationData.empty()
+
+    min_dist, cutoff_dist = np.partition(distances, [0, max_points_i])[
+        [0, max_points_i]
+    ]
+    if min_dist > max_dist_m:
+        # Not close enough to any observed locations
+        return ObservationData.empty()
+
+    # Include all observations within cutoff distance (may exceed max_points for accuracy)
+    loc_mask = distances <= min(max_dist_m, cutoff_dist)
+    filtered_indices = np.where(loc_mask)[0]
+
+    # Create subset of ObservationData
+    selected_obs = ObservationData(
+        locations=obs_data.locations[filtered_indices],
+        vs30=obs_data.vs30[filtered_indices],
+        model_vs30=obs_data.model_vs30[filtered_indices],
+        model_stdv=obs_data.model_stdv[filtered_indices],
+        residuals=obs_data.residuals[filtered_indices],
+        omega=obs_data.omega[filtered_indices],
+        uncertainty=obs_data.uncertainty[filtered_indices],
+    )
+
+    return selected_obs
+
+
+def compute_spatial_adjustment_for_pixel(
+    pixel: PixelData,
+    obs_data: ObservationData,
+    model_type: constants.ModelType,
+    max_dist_m: float = constants.MAX_DIST_M,
+    max_points: int = constants.MAX_POINTS,
+    noisy: bool = False,
+    cov_reduc: float = constants.COV_REDUC,
+) -> SpatialAdjustmentResult | None:
+    """
+    Compute MVN update for a single pixel.
+
+    Parameters
+    ----------
+    pixel : PixelData
+        Pixel data.
+    obs_data : ObservationData
+        Full observation data.
+    model_type : constants.ModelType
+        Model type (ModelType.GEOLOGY or ModelType.TERRAIN).
+    max_dist_m : float, optional
+        Maximum distance in meters to consider observations.
+    max_points : int, optional
+        Maximum number of observations to select per pixel.
+    noisy : bool, optional
+        Whether to apply noise weighting based on observation uncertainty.
+    cov_reduc : float, optional
+        Covariance reduction factor for dissimilar Vs30 values.
+
+    Returns
+    -------
+    SpatialAdjustmentResult or None
+        Update result, or None if pixel should be skipped.
+    """
+    # Handle NaN/NoData pixels
+    if np.isnan(pixel.vs30) or np.isnan(pixel.stdv):
+        return None
+
+    # Correlation at zero distance is slightly less than 1.0 due to the
+    # enforced minimum distance (nugget effect). This shrinks the prior
+    # variance to match the legacy implementation's behavior.
+    corr_zero = utils.correlation_function(np.array([0.0]), constants.PHI[model_type])[
+        0
+    ]
+    initial_var = (pixel.stdv**2) * corr_zero
+
+    # Select observations for this pixel
+    selected_obs = select_observations_for_pixel(
+        pixel,
+        obs_data,
+        max_dist_m=max_dist_m,
+        max_points=max_points,
+    )
+
+    if len(selected_obs.locations) == 0:
+        # No observations nearby, return unchanged values (but with shrunk stdv matching legacy)
+        return SpatialAdjustmentResult(
+            updated_vs30=pixel.vs30,
+            updated_stdv=np.sqrt(initial_var),
+            n_observations_used=0,
+            min_distance=np.inf,
+            pixel_index=pixel.index,
+        )
+
+    # Build covariance matrix
+    cov_matrix = build_covariance_matrix(
+        pixel,
+        selected_obs,
+        model_type,
+        noisy=noisy,
+        cov_reduc=cov_reduc,
+    )
+
+    # Invert covariance matrix (observations only)
+    inv_cov = np.linalg.inv(cov_matrix[1:, 1:])
+
+    # Calculate prediction update
+    pred_update = np.dot(
+        np.dot(cov_matrix[0, 1:], inv_cov),
+        selected_obs.residuals,
+    )
+
+    # Calculate variance
+    var = cov_matrix[0, 0] - np.dot(
+        np.dot(cov_matrix[0, 1:], inv_cov), cov_matrix[1:, 0]
+    )
+
+    # Update vs30 and stdv
+    new_vs30 = pixel.vs30 * np.exp(pred_update)
+    new_stdv = np.sqrt(var)
+
+    # Calculate minimum distance to nearest observation used
+    distances = scipy.spatial.distance.cdist(
+        pixel.location.reshape(1, -1),
+        selected_obs.locations,
+        metric="euclidean",
+    )[0]
+    min_distance = np.min(distances)
+
+    return SpatialAdjustmentResult(
+        updated_vs30=float(new_vs30),
+        updated_stdv=float(new_stdv),
+        n_observations_used=len(selected_obs.locations),
+        min_distance=float(min_distance),
+        pixel_index=pixel.index,
+    )
+
+
+def subsample_by_cluster(
+    cluster_labels: np.ndarray,
+    step: int = 100,
+) -> np.ndarray:
+    """
+    Subsample observation indices by taking every Nth observation within each cluster.
+
+    For clustered observations, nearby observations affect the same pixels.
+    This function ensures representative sampling from each cluster rather
+    than global subsampling which might miss small clusters entirely.
+
+    Parameters
+    ----------
+    cluster_labels : ndarray
+        Array of cluster labels for each observation. -1 indicates unclustered
+        observations (noise points from DBSCAN).
+    step : int, optional
+        Take every Nth observation within each cluster. Default is 100.
+
+    Returns
+    -------
+    ndarray
+        Indices of the subsampled observations.
+
+    Notes
+    -----
+    - Unclustered observations (label=-1) are all included since they are
+      spatially isolated and each may affect different pixels.
+    - Within each cluster, observations are subsampled by taking every Nth.
+    - At least one observation is always kept from each cluster.
+    """
+    unique_labels = np.unique(cluster_labels)
+
+    selected_indices = []
+
+    for label in unique_labels:
+        cluster_mask = cluster_labels == label
+        cluster_indices = np.where(cluster_mask)[0]
+
+        if label == -1:
+            # Unclustered observations - include all
+            selected_indices.extend(cluster_indices.tolist())
+        else:
+            # Clustered observations - take every Nth
+            subsampled = cluster_indices[::step]
+            selected_indices.extend(subsampled.tolist())
+
+    # Sort to maintain original order
+    selected_indices = np.array(sorted(selected_indices), dtype=np.int64)
+
+    return selected_indices
+
+
+def apply_clustered_subsampling(
+    obs_data: ObservationData,
+    is_clustered_obs: bool,
+    n_proc: int,
+    obs_subsample_step: int,
+) -> ObservationData:
+    """
+    Apply DBSCAN clustering and subsampling to observations for optimized pixel search.
+
+    For clustered observations (e.g., CPT data), this function identifies spatial
+    clusters and subsamples within each cluster to reduce the number of observations
+    used for the bounding box search, while maintaining spatial coverage.
+
+    Parameters
+    ----------
+    obs_data : ObservationData
+        Full observation data.
+    is_clustered_obs : bool
+        Whether the observations are from a clustered observations file.
+    n_proc : int
+        Number of parallel jobs for DBSCAN.
+    obs_subsample_step : int
+        Step size for subsampling within each cluster.
+
+    Returns
+    -------
+    ObservationData
+        Either the original obs_data (if not clustered) or a subsampled version
+        for use in the affected pixel search.
+    """
+    if not is_clustered_obs:
+        return obs_data
+
+    logger.info("Clustering observations for optimized affected pixel search...")
+
+    dbscan = sklearn.cluster.DBSCAN(
+        eps=constants.EPS, min_samples=constants.MIN_GROUP, n_jobs=n_proc
+    )
+    cluster_labels = dbscan.fit_predict(obs_data.locations)
+
+    n_clusters = len(set(cluster_labels)) - (1 if -1 in cluster_labels else 0)
+    n_noise = np.sum(cluster_labels == -1)
+    logger.info(
+        f"Found {n_clusters} clusters and {n_noise} noise points "
+        f"in {len(obs_data.locations)} observations"
+    )
+
+    subsample_indices = subsample_by_cluster(cluster_labels, step=obs_subsample_step)
+
+    obs_data_for_bbox = ObservationData(
+        locations=obs_data.locations[subsample_indices],
+        vs30=obs_data.vs30[subsample_indices],
+        model_vs30=obs_data.model_vs30[subsample_indices],
+        model_stdv=obs_data.model_stdv[subsample_indices],
+        residuals=obs_data.residuals[subsample_indices],
+        omega=obs_data.omega[subsample_indices],
+        uncertainty=obs_data.uncertainty[subsample_indices],
+    )
+
+    logger.info(
+        f"Subsampled to {len(subsample_indices)} observations for affected pixel search "
+        f"(step={obs_subsample_step})"
+    )
+
+    return obs_data_for_bbox
+
+
+def find_affected_pixels(
+    raster_data: RasterData,
+    obs_data: ObservationData,
+    max_spatial_boolean_array_memory_gb: float,
+    model_type: constants.ModelType,
+    max_dist_m: float = constants.MAX_DIST_M,
+    n_proc: int = 1,
+) -> BoundingBoxResult:
+    """
+    Find pixels affected by observations using bounding boxes.
+
+    Parameters
+    ----------
+    raster_data : RasterData
+        Raster data object.
+    obs_data : ObservationData
+        Observation data.
+    max_spatial_boolean_array_memory_gb : float
+        Memory limit (GB) for boolean arrays in spatial processing.
+    model_type : constants.ModelType
+        Model type (ModelType.GEOLOGY or ModelType.TERRAIN), used for
+        progress bar labelling.
+    max_dist_m : float, optional
+        Maximum distance for considering observations.
+    n_proc : int, optional
+        Number of parallel processes. 1 for sequential (default),
+        >1 for parallel processing.
+
+    Returns
+    -------
+    BoundingBoxResult
+        Result containing mask and observation-to-grid mappings.
+    """
+    # Get coordinates for valid pixels
+    grid_locs = raster_data.get_coordinates()
+
+    n_obs = len(obs_data.locations)
+
+    # Calculate chunk size based on observation count
+    chunk_size = calculate_chunk_size(n_obs, max_spatial_boolean_array_memory_gb)
+    n_chunks = int(np.ceil(len(grid_locs) / chunk_size))
+
+    # Precompute observation bounds
+    obs_eastings = obs_data.locations[:, 0:1]  # (n_obs, 1)
+    obs_northings = obs_data.locations[:, 1:2]  # (n_obs, 1)
+    obs_eastings_min = obs_eastings - max_dist_m
+    obs_eastings_max = obs_eastings + max_dist_m
+    obs_northings_min = obs_northings - max_dist_m
+    obs_northings_max = obs_northings + max_dist_m
+
+    # Bundle observation bounds for passing to workers
+    obs_bounds = (
+        obs_eastings_min,
+        obs_eastings_max,
+        obs_northings_min,
+        obs_northings_max,
+    )
+
+    # Initialize mask and obs_to_grid_indices
+    valid_points_in_bbox_mask = np.zeros(len(grid_locs), dtype=bool)
+    obs_to_grid_indices = [np.array([], dtype=np.int64) for _ in range(n_obs)]
+
+    logger.info(f"Processing {n_chunks} chunks of {chunk_size:,} pixels each")
+
+    label = str(model_type).capitalize()
+
+    # Prepare chunk arguments
+    chunk_args = []
+    for chunk_idx in range(n_chunks):
+        start_idx = chunk_idx * chunk_size
+        end_idx = min((chunk_idx + 1) * chunk_size, len(grid_locs))
+        grid_locs_chunk = grid_locs[start_idx:end_idx]
+        chunk_args.append((chunk_idx, grid_locs_chunk, start_idx, obs_bounds))
+
+    if n_proc > 1 and n_chunks > 1:
+        # Parallel processing
+        actual_n_proc = min(n_proc, n_chunks)
+        logger.info(f"Using {actual_n_proc} parallel workers")
+        with _spawn_context.Pool(processes=actual_n_proc) as pool:
+            results = list(
+                tqdm(
+                    pool.imap(process_bbox_chunk, chunk_args),
+                    total=n_chunks,
+                    desc=f"{label}: checking pixels for nearby observations ({n_chunks} chunks)",
+                    unit="chunk",
+                )
+            )
+
+    elif n_chunks > 1:
+        # Sequential processing with multiple chunks
+        results = []
+        for chunk_idx in tqdm(
+            range(n_chunks),
+            desc=f"{label}: checking pixels for nearby observations ({n_chunks} chunks)",
+            unit="chunk",
+        ):
+            results.append(process_bbox_chunk(chunk_args[chunk_idx]))
+
+    else:
+        # Single chunk — no progress bar needed
+        print(
+            f"{label}: checking {len(grid_locs):,} pixels for nearby observations... ",
+            end="",
+            flush=True,
+        )
+        results = [process_bbox_chunk(chunk_args[0])]
+        print("done")
+
+    # Merge results from either parallel or sequential processing
+    for chunk_idx, chunk_mask, chunk_obs_to_grid in results:
+        start_idx = chunk_idx * chunk_size
+        valid_points_in_bbox_mask[start_idx : start_idx + len(chunk_mask)] = chunk_mask
+        for obs_idx, grid_indices in enumerate(chunk_obs_to_grid):
+            if len(grid_indices) > 0:
+                full_raster_indices = raster_data.valid_flat_indices[grid_indices]
+                obs_to_grid_indices[obs_idx] = np.concatenate(
+                    [obs_to_grid_indices[obs_idx], full_raster_indices]
+                )
+
+    # Create full-size mask
+    grid_points_in_bbox_mask = np.zeros(raster_data.vs30.size, dtype=bool)
+    grid_points_in_bbox_mask[raster_data.valid_flat_indices] = valid_points_in_bbox_mask
+
+    n_affected = np.sum(valid_points_in_bbox_mask)
+    logger.info(
+        f"Bounding box search complete: {n_affected:,} pixels affected "
+        f"({n_affected / len(grid_locs) * 100:.1f}% of valid pixels)"
+    )
+
+    return BoundingBoxResult(
+        mask=grid_points_in_bbox_mask,
+        obs_to_grid_indices=obs_to_grid_indices,
+        n_affected_pixels=n_affected,
+    )
+
+
+def compute_spatial_adjustments(
+    raster_data: RasterData,
+    obs_data: ObservationData,
+    bbox_result: BoundingBoxResult,
+    model_type: constants.ModelType,
+    max_spatial_boolean_array_memory_gb: float,
+    max_dist_m: float = constants.MAX_DIST_M,
+    max_points: int = constants.MAX_POINTS,
+    noisy: bool = False,
+    cov_reduc: float = constants.COV_REDUC,
+) -> list[SpatialAdjustmentResult]:
+    """
+    Compute MVN updates for all affected pixels.
+
+    Parameters
+    ----------
+    raster_data : RasterData
+        Raster data object.
+    obs_data : ObservationData
+        Observation data.
+    bbox_result : BoundingBoxResult
+        Bounding box result.
+    model_type : constants.ModelType
+        Model type (ModelType.GEOLOGY or ModelType.TERRAIN).
+    max_spatial_boolean_array_memory_gb : float
+        Memory limit (GB) for boolean arrays in spatial processing.
+    max_dist_m : float, optional
+        Maximum distance in meters to consider observations.
+    max_points : int, optional
+        Maximum number of observations to select per pixel.
+    noisy : bool, optional
+        Whether to apply noise weighting based on observation uncertainty.
+    cov_reduc : float, optional
+        Covariance reduction factor for dissimilar Vs30 values.
+
+    Returns
+    -------
+    list
+        List of SpatialAdjustmentResult objects.
+    """
+    # Get affected pixel indices
+    affected_flat_indices = np.where(bbox_result.mask)[0]
+    affected_valid_indices = np.where(bbox_result.mask[raster_data.valid_flat_indices])[
+        0
+    ]
+
+    # Get coordinates for affected pixels
+    grid_locs = raster_data.get_coordinates()
+    affected_locs = grid_locs[affected_valid_indices]
+
+    # Get model values for affected pixels
+    affected_vs30 = raster_data.vs30.flat[affected_flat_indices]
+    affected_stdv = raster_data.stdv.flat[affected_flat_indices]
+
+    # Process in chunks for memory efficiency
+    chunk_size = calculate_chunk_size(
+        len(obs_data.locations), max_spatial_boolean_array_memory_gb
+    )
+    n_chunks = int(np.ceil(len(affected_flat_indices) / chunk_size))
+
+    all_updates = []
+
+    logger.info(
+        f"Processing {n_chunks} chunks of up to {chunk_size:,} pixels each "
+        f"({len(affected_flat_indices):,} total pixels to update)"
+    )
+
+    # Process all affected pixels with a single progress bar
+    label = str(model_type).capitalize()
+    with tqdm(
+        total=len(affected_flat_indices),
+        desc=f"{label}: spatial adjustment",
+        unit="pixel",
+    ) as pbar:
+        for chunk_idx in range(n_chunks):
+            start_idx = chunk_idx * chunk_size
+            end_idx = min((chunk_idx + 1) * chunk_size, len(affected_flat_indices))
+
+            chunk_flat_indices = affected_flat_indices[start_idx:end_idx]
+            chunk_affected_locs = affected_locs[start_idx:end_idx]
+            chunk_affected_vs30 = affected_vs30[start_idx:end_idx]
+            chunk_affected_stdv = affected_stdv[start_idx:end_idx]
+
+            for i, flat_idx in enumerate(chunk_flat_indices):
+                pixel = PixelData(
+                    location=chunk_affected_locs[i],
+                    vs30=float(chunk_affected_vs30[i]),
+                    stdv=float(chunk_affected_stdv[i]),
+                    index=flat_idx,
+                )
+
+                update_result = compute_spatial_adjustment_for_pixel(
+                    pixel,
+                    obs_data,
+                    model_type,
+                    max_dist_m=max_dist_m,
+                    max_points=max_points,
+                    noisy=noisy,
+                    cov_reduc=cov_reduc,
+                )
+
+                if update_result is not None:
+                    all_updates.append(update_result)
+
+                pbar.update(1)
+
+    logger.info(f"Completed processing all chunks: {len(all_updates):,} pixels updated")
+
+    return all_updates
+
+
+def apply_and_write_updates(
+    raster_data: RasterData,
+    updates: list[SpatialAdjustmentResult],
+    model_type: constants.ModelType,
+    output_dir: Path,
+) -> None:
+    """
+    Apply updates to raster and write output file.
+
+    Parameters
+    ----------
+    raster_data : RasterData
+        Raster data object.
+    updates : list
+        List of SpatialAdjustmentResult objects.
+    model_type : constants.ModelType
+        Model type (ModelType.GEOLOGY or ModelType.TERRAIN).
+    output_dir : Path
+        Directory where output raster will be saved.
+    """
+    # Initialize output arrays with original values
+    updated_vs30 = raster_data.vs30.copy()
+    updated_stdv = raster_data.stdv.copy()
+
+    # Apply updates
+    for update in updates:
+        updated_vs30.flat[update.pixel_index] = update.updated_vs30
+        updated_stdv.flat[update.pixel_index] = update.updated_stdv
+
+    # Write output using filename from constants
+    output_filename = constants.OUTPUT_FILENAMES[model_type]
+    output_path = output_dir / output_filename
+
+    raster_data.write_updated(output_path, updated_vs30, updated_stdv)
+
+    logger.info(
+        f"Wrote updated raster to {output_path} "
+        f"({len(updates):,} pixels updated out of {np.sum(raster_data.valid_mask):,} valid)"
+    )
+
+
+def compute_spatial_adjustment_at_points(
+    points: np.ndarray,
+    model_vs30: np.ndarray,
+    model_stdv: np.ndarray,
+    obs_locations: np.ndarray,
+    obs_vs30: np.ndarray,
+    obs_model_vs30: np.ndarray,
+    obs_model_stdv: np.ndarray,
+    obs_uncertainty: np.ndarray,
+    model_type: constants.ModelType,
+    max_dist_m: float = constants.MAX_DIST_M,
+    max_points: int = constants.MAX_POINTS,
+    noisy: bool = False,
+    cov_reduc: float = constants.COV_REDUC,
+    progress_bar: tqdm | None = None,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Compute MVN spatial adjustment at specific query points.
+
+    This is the point-based equivalent of compute_spatial_adjustments(). It applies
+    the same MVN conditioning algorithm but for arbitrary query points instead
+    of raster pixels.
+
+    Parameters
+    ----------
+    points : np.ndarray
+        (N, 2) array of [easting, northing] query locations in NZTM.
+    model_vs30 : np.ndarray
+        (N,) array of model Vs30 values at query points (before MVN adjustment).
+    model_stdv : np.ndarray
+        (N,) array of model standard deviation at query points.
+    obs_locations : np.ndarray
+        (M, 2) array of observation [easting, northing] locations.
+    obs_vs30 : np.ndarray
+        (M,) array of measured Vs30 values at observations.
+    obs_model_vs30 : np.ndarray
+        (M,) array of model Vs30 values at observation locations.
+    obs_model_stdv : np.ndarray
+        (M,) array of model standard deviation at observation locations.
+    obs_uncertainty : np.ndarray
+        (M,) array of observation uncertainties.
+    model_type : constants.ModelType
+        Either ModelType.GEOLOGY or ModelType.TERRAIN (determines phi correlation length).
+    max_dist_m : float, optional
+        Maximum distance (meters) to consider observations. Default from constants.
+    max_points : int, optional
+        Maximum number of observations per point. Default from constants.
+    noisy : bool
+        Whether to apply noise weighting.
+    cov_reduc : float
+        Covariance reduction factor. Default from constants.
+    progress_bar : tqdm, optional
+        External progress bar to update per point. If None, no progress is shown.
+
+    Returns
+    -------
+    mvn_vs30 : np.ndarray
+        (N,) array of spatially adjusted Vs30 values.
+    mvn_stdv : np.ndarray
+        (N,) array of spatially adjusted standard deviation values.
+
+    Notes
+    -----
+    The algorithm for each query point:
+    1. Find observations within max_dist_m (limited to max_points closest)
+    2. Compute log residuals: log(obs_vs30 / obs_model_vs30)
+    3. Build covariance matrix using exponential correlation function
+    4. Apply MVN conditioning to get posterior mean and variance
+    5. Convert back from log-space to linear Vs30
+    """
+    n_points = len(points)
+    n_obs = len(obs_locations)
+
+    # Initialize output arrays with prior values
+    mvn_vs30 = model_vs30.copy()
+    mvn_stdv = model_stdv.copy()
+
+    if n_obs == 0:
+        logger.warning("No observations provided for MVN adjustment")
+        return mvn_vs30, mvn_stdv
+
+    # Compute log residuals at observation locations
+    # Filter out invalid observations (NaN model values)
+    valid_obs_mask = (
+        ~np.isnan(obs_model_vs30)
+        & ~np.isnan(obs_model_stdv)
+        & (obs_model_vs30 > 0)
+        & (obs_model_stdv > 0)
+    )
+
+    if not np.any(valid_obs_mask):
+        logger.warning("No valid observations for MVN adjustment")
+        return mvn_vs30, mvn_stdv
+
+    # Filter to valid observations
+    obs_locs = obs_locations[valid_obs_mask]
+    obs_vs30_valid = obs_vs30[valid_obs_mask]
+    obs_model_vs30_valid = obs_model_vs30[valid_obs_mask]
+    obs_model_stdv_valid = obs_model_stdv[valid_obs_mask]
+    obs_uncertainty_valid = obs_uncertainty[valid_obs_mask]
+
+    # Compute residuals
+    obs_residuals = np.log(obs_vs30_valid / obs_model_vs30_valid)
+
+    # Apply noise weighting if enabled
+    if noisy:
+        omega_obs = np.sqrt(
+            obs_model_stdv_valid**2
+            / (obs_model_stdv_valid**2 + obs_uncertainty_valid**2)
+        )
+        obs_residuals = obs_residuals * omega_obs
+    else:
+        omega_obs = np.ones(len(obs_residuals))
+
+    # Compute correlation at distance 0 for default variance
+    corr_zero = utils.correlation_function(np.array([0.0]), constants.PHI[model_type])[
+        0
+    ]
+
+    # Process each query point
+    for i in range(n_points):
+        point = points[i]
+        prior_vs30 = model_vs30[i]
+        prior_stdv = model_stdv[i]
+
+        # Skip invalid points
+        if (
+            np.isnan(prior_vs30)
+            or np.isnan(prior_stdv)
+            or prior_vs30 <= 0
+            or prior_stdv <= 0
+        ):
+            if progress_bar is not None:
+                progress_bar.update(1)
+            continue
+
+        # Calculate distances from this point to all observations
+        distances = np.sqrt(np.sum((obs_locs - point) ** 2, axis=1))
+
+        # Find nearby observations
+        nearby_mask = distances <= max_dist_m
+
+        if not np.any(nearby_mask):
+            # No nearby observations - apply default variance shrinkage (matching legacy)
+            mvn_stdv[i] = np.sqrt(prior_stdv**2 * corr_zero)
+            continue
+
+        # Limit to max_points closest observations
+        nearby_indices = np.where(nearby_mask)[0]
+        if len(nearby_indices) > max_points:
+            nearby_distances = distances[nearby_indices]
+            closest_indices = np.argsort(nearby_distances)[:max_points]
+            nearby_indices = nearby_indices[closest_indices]
+
+        # Extract data for nearby observations
+        nearby_locs = obs_locs[nearby_indices]
+        nearby_residuals = obs_residuals[nearby_indices]
+        nearby_model_stdv = obs_model_stdv_valid[nearby_indices]
+        nearby_omega = omega_obs[nearby_indices]
+        nearby_model_vs30 = obs_model_vs30_valid[nearby_indices]
+
+        # Build covariance matrix
+        # First element is query point, rest are nearby observations
+        all_locs = np.vstack([point, nearby_locs])
+        dist_matrix = scipy.spatial.distance.cdist(
+            all_locs, all_locs, metric="euclidean"
+        )
+
+        # Apply correlation function
+        corr_matrix = utils.correlation_function(dist_matrix, constants.PHI[model_type])
+
+        # Scale by standard deviations to get covariance
+        stdv_vector = np.concatenate([[prior_stdv], nearby_model_stdv])
+        cov_matrix = corr_matrix * np.outer(stdv_vector, stdv_vector)
+
+        # Apply noise weighting if enabled
+        if noisy:
+            omega_vector = np.concatenate([[1.0], nearby_omega])
+            omega_matrix = np.outer(omega_vector, omega_vector)
+            np.fill_diagonal(omega_matrix, 1.0)
+            cov_matrix *= omega_matrix
+
+        # Apply covariance reduction for dissimilar Vs30 values
+        if cov_reduc > 0:
+            vs30_vector = np.concatenate([[prior_vs30], nearby_model_vs30])
+            log_vs30_dist = np.abs(
+                np.log(vs30_vector[:, np.newaxis]) - np.log(vs30_vector)
+            )
+            cov_matrix *= np.exp(-cov_reduc * log_vs30_dist)
+
+        # MVN conditioning
+        # Partition: C = [[C_pp, C_po], [C_op, C_oo]]
+        C_pp = cov_matrix[0, 0]  # point-point covariance
+        C_po = cov_matrix[0, 1:]  # point-observation covariance
+        C_oo = cov_matrix[1:, 1:]  # observation-observation covariance
+
+        try:
+            C_oo_inv = np.linalg.inv(C_oo)
+            C_op = cov_matrix[1:, 0]  # observation-to-point covariance
+
+            # Posterior mean adjustment: C_po @ inv(C_oo) @ residuals
+            pred_adjustment = C_po @ C_oo_inv @ nearby_residuals
+
+            # Posterior variance: C_pp - C_po @ inv(C_oo) @ C_op
+            var_reduction = C_po @ C_oo_inv @ C_op
+            posterior_var = C_pp - var_reduction
+
+            # Update vs30 in log-space, then convert back
+            log_vs30_posterior = np.log(prior_vs30) + pred_adjustment
+            mvn_vs30[i] = np.exp(log_vs30_posterior)
+            mvn_stdv[i] = np.sqrt(max(0, posterior_var))
+
+        except np.linalg.LinAlgError:
+            # Singular matrix - keep prior values with default variance shrinkage
+            mvn_stdv[i] = np.sqrt(prior_stdv**2 * corr_zero)
+            logger.debug(
+                f"Singular covariance matrix at point {i}, keeping prior values"
+            )
+
+        if progress_bar is not None:
+            progress_bar.update(1)
+
+    return mvn_vs30, mvn_stdv
diff --git a/vs30/utils.py b/vs30/utils.py
new file mode 100644
index 00000000..bcd32ba7
--- /dev/null
+++ b/vs30/utils.py
@@ -0,0 +1,181 @@
+"""Shared utility functions: correlation function and model combination."""
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from vs30 import constants
+
+
+def correlation_function(
+    distances: np.ndarray,
+    phi: float,
+    min_dist: float = constants.MIN_DIST_ENFORCED,
+) -> np.ndarray:
+    """
+    Calculate exponential correlation from distances.
+
+    Parameters
+    ----------
+    distances : ndarray
+        Array of distances in meters. Can be scalar, 1D, or 2D (distance matrix).
+    phi : float
+        Correlation length parameter in meters.
+    min_dist : float, optional
+        Minimum distance enforced to prevent division issues. Default is MIN_DIST_ENFORCED.
+
+    Returns
+    -------
+    ndarray
+        Correlation values between 0 and 1. Same shape as distances.
+
+    """
+    return np.exp(-np.maximum(min_dist, distances) / phi)
+
+
+def combine_vs30_models(
+    geol_vs30: np.ndarray,
+    geol_stdv: np.ndarray,
+    terr_vs30: np.ndarray,
+    terr_stdv: np.ndarray,
+    combination_method: str | float,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Combine geology and terrain Vs30 models using log-space weighted mixture.
+
+    This function implements the correct combination algorithm that matches
+    the raster-based `combine` CLI command. Models are combined in log-space
+    (geometric weighting) with standard deviation computed using the mixture
+    of log-normals formula.
+
+    Parameters
+    ----------
+    geol_vs30 : ndarray
+        Geology model Vs30 values.
+    geol_stdv : ndarray
+        Geology model standard deviation (in log-space).
+    terr_vs30 : ndarray
+        Terrain model Vs30 values.
+    terr_stdv : ndarray
+        Terrain model standard deviation (in log-space).
+    combination_method : str or float
+        Either a ratio (float) where ratio = geology_weight / terrain_weight,
+        so ratio=1.0 gives equal weighting, ratio=2.0 gives geology twice
+        the weight of terrain. Or "standard_deviation_weighting" for
+        variance-based weighting using K_VALUE exponent.
+
+    Returns
+    -------
+    combined_vs30 : ndarray
+        Combined Vs30 values (geometric weighted mean).
+    combined_stdv : ndarray
+        Combined standard deviation (mixture of log-normals formula).
+
+    Notes
+    -----
+    The combination is performed in log-space:
+    - Weights are computed based on the combination method
+    - Log-space combination: log_comb = w_g * log(geol) + w_t * log(terr)
+    - Combined Vs30 = exp(log_comb)
+    - Combined stdv uses mixture of log-normals: sqrt(w_g*(diff_g² + σ_g²) + w_t*(diff_t² + σ_t²))
+
+    For ratio=1.0 with inputs (200, 400), the result is ~283 (geometric mean),
+    not 300 (arithmetic mean).
+
+    Uses K_VALUE and WEIGHT_EPSILON_DIV_BY_ZERO constants from constants.py
+    for standard deviation weighting calculations.
+    """
+    # Determine weights based on combination method
+    if str(combination_method).strip() == constants.COMBINATION_METHOD_STDV_WEIGHTING:
+        # Variance-based weighting: lower stdv gets higher weight
+        m_g = (
+            geol_stdv**2 + constants.WEIGHT_EPSILON_DIV_BY_ZERO
+        ) ** -constants.K_VALUE
+        m_t = (
+            terr_stdv**2 + constants.WEIGHT_EPSILON_DIV_BY_ZERO
+        ) ** -constants.K_VALUE
+        total_m = m_g + m_t
+        w_g = m_g / total_m
+        w_t = m_t / total_m
+    else:
+        try:
+            # Ratio-based: ratio = geology_weight / terrain_weight
+            ratio = float(combination_method)
+        except (ValueError, TypeError):
+            raise ValueError(f"Unknown combination method: {combination_method}")
+        total_w = ratio + 1.0
+        w_g = ratio / total_w
+        w_t = 1.0 / total_w
+
+    # Combine in log-space (geometric weighting)
+    log_g = np.log(geol_vs30)
+    log_t = np.log(terr_vs30)
+
+    log_comb = log_g * w_g + log_t * w_t
+    combined_vs30 = np.exp(log_comb)
+
+    # Combined stdv using mixture of log-normals formula
+    # Each component contributes: weight * (squared_diff_from_mean + variance)
+    combined_stdv = np.sqrt(
+        w_g * ((log_g - log_comb) ** 2 + geol_stdv**2)
+        + w_t * ((log_t - log_comb) ** 2 + terr_stdv**2)
+    )
+
+    return combined_vs30, combined_stdv
+
+
+def resolve_observation_csv(
+    csv_path: Path | None,
+    config_filename: str | None,
+    res_dir: Path,
+) -> Path | None:
+    """
+    Resolve an observation CSV path from an explicit argument or config default.
+
+    Parameters
+    ----------
+    csv_path : Path or None
+        Explicitly provided path.
+    config_filename : str or None
+        Filename from config (e.g., cfg.clustered_observations_file).
+    res_dir : Path
+        Resources directory to look for config-specified files.
+
+    Returns
+    -------
+    Path or None
+        Resolved path, or None if no valid file found.
+    """
+    if csv_path is not None:
+        return csv_path
+    if config_filename is not None:
+        candidate = res_dir / config_filename
+        if candidate.exists():
+            return candidate
+    return None
+
+
+def validate_csv_columns(
+    df: pd.DataFrame, required_cols: list[str], label: str
+) -> None:
+    """
+    Raise ValueError if the DataFrame is missing any required columns.
+
+    Parameters
+    ----------
+    df : DataFrame
+        DataFrame to validate.
+    required_cols : list[str]
+        Column names that must be present.
+    label : str
+        Descriptive label used in the error message (e.g. "Clustered observations CSV").
+
+    Raises
+    ------
+    ValueError
+        If any required columns are missing.
+    """
+    missing = [col for col in required_cols if col not in df.columns]
+    if missing:
+        raise ValueError(f"{label} missing required columns: {missing}")
diff --git a/vs30/vs30calc.py b/vs30/vs30calc.py
deleted file mode 100644
index edb92487..00000000
--- a/vs30/vs30calc.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import time
-from pathlib import Path
-from functools import partial
-import logging
-import math
-from multiprocessing import Pool
-import os
-from shutil import copyfile, rmtree
-import sys
-
-import numpy as np
-import pandas as pd
-
-from pyproj import Transformer
-
-from vs30 import (
-    model,
-    model_geology,
-    model_terrain,
-    mvn,
-    params,
-    sites_cluster,
-    sites_load,
-)
-
-WGS2NZTM = Transformer.from_crs(4326, 2193, always_xy=True)
-
-
-def array_split(
-    array: np.ndarray, n_proc: int, chunk_size: int = 50
-) -> list[np.ndarray]:
-    """
-    Split dataframes and numpy arrays for multiprocessing.Pool.map
-    """
-    n_chunks = math.ceil(max(n_proc, len(array) / chunk_size))
-    return np.array_split(array, n_chunks)
-
-
-def run_vs30calc(
-    p_paths: params.PathsParams,
-    p_sites: params.SitesParams,
-    p_grid: params.GridParams | None,
-    p_ll: params.LLFileParams | None,
-    p_geol: params.GeologyParams,
-    p_terr: params.TerrainParams,
-    p_comb: params.CombinationParams,
-    n_procs: int,
-):
-    """
-    Run the Vs30 calculation for the specified parameters.
-    For details on the parameters, see params.py.
-
-    Parameters
-    ----------
-    p_paths : params.PathsParams
-        Paths parameters.
-    p_sites : params.SitesParams
-        Sites parameters.
-    p_grid : params.GridParams | None
-        Grid parameters.
-    p_ll : params.LLFileParams | None
-        Latitude/longitude file parameters.
-    p_geol : params.GeologyParams
-        Geology model parameters.
-    p_terr : params.TerrainParams
-        Terrain model parameters.
-    p_comb : params.CombinationParams
-        Combined model parameters.
-    n_procs : int
-        Number of processes to use.
-    """
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        level=logging.INFO,
-    )
-    logger = logging.getLogger(__name__)
-    logger.info(f"Running Vs30Calc with {n_procs} processes")
-
-    # working directory/output setup
-    if os.path.exists(p_paths.out):
-        if p_paths.overwrite:
-            rmtree(p_paths.out)
-        else:
-            sys.exit("output exists")
-    os.makedirs(p_paths.out)
-
-    # input locations
-    if p_ll is not None:
-        logger.info("loading locations...")
-        table = pd.read_csv(
-            p_ll.ll_path,
-            usecols=(p_ll.lon_col_ix, p_ll.lat_col_ix),
-            names=["longitude", "latitude"],
-            engine="c",
-            skiprows=p_ll.skip_rows,
-            dtype=np.float64,
-            sep=p_ll.col_sep,
-        )
-        table["easting"], table["northing"] = WGS2NZTM.transform(
-            table.longitude.values, table.latitude.values
-        )
-        table_points = table[["easting", "northing"]].values
-
-    # measured sites
-    logger.info("loading measured sites...")
-    measured_sites = sites_load.load_vs(source=p_sites.source)
-    measured_sites_points = np.column_stack(
-        (measured_sites.easting.values, measured_sites.northing.values)
-    )
-
-    MODEL_MAPPING = {"geology": model_geology, "terrain": model_terrain}
-
-    # model loop
-    tiffs = []
-    tiffs_mvn = []
-    for model_setup in [p_geol, p_terr]:
-        if model_setup.update == "off":
-            continue
-        logger.info(f"{model_setup.name} model...")
-        model_module = MODEL_MAPPING[model_setup.name]
-
-        logger.info("    model measured update...")
-        start_time = time.time()
-        measured_sites[f"{model_setup.letter}id"] = model_module.model_id(measured_sites_points)
-        if model_setup.update == "prior":
-            model_table = model_module.model_prior()
-        elif model_setup.update == "posterior_paper":
-            model_table = model_module.model_posterior_paper()
-        elif model_setup.update == "posterior":
-            model_table = model_module.model_prior()
-            if p_sites.source == "cpt":
-                measured_sites = sites_cluster.cluster(
-                    measured_sites, model_setup.letter, nproc=n_procs
-                )
-                model_table = model.cluster_update(
-                    model_table, measured_sites, model_setup.letter
-                )
-            else:
-                model_table = model.posterior(
-                    model_table, measured_sites, f"{model_setup.letter}id"
-                )
-        logger.info(f"    took: {time.time()-start_time:.2f}s")
-
-        logger.info("    model at measured sites...")
-        start_time = time.time()
-        (
-            measured_sites[f"{model_setup.name}_vs30"],
-            measured_sites[f"{model_setup.name}_stdv"],
-        ) = model_module.model_val(
-            measured_sites[f"{model_setup.letter}id"].values,
-            model_table,
-            model_setup,
-            paths=p_paths,
-            points=measured_sites_points,
-            grid=p_grid,
-        ).T
-        logger.info(f"    took: {time.time()-start_time:.2f}s")
-
-        if p_ll is not None:
-            logger.info("    model points...")
-            # if n_procs == 1:
-            start = time.time()
-            table[f"{model_setup.letter}id"] = model_module.model_id(
-                table_points
-            )
-            logger.info(f"    took: {time.time() - start} ")
-            (
-                table[f"{model_setup.name}_vs30"],
-                table[f"{model_setup.name}_stdv"],
-            ) = model_module.model_val(
-                table[f"{model_setup.letter}id"].values,
-                model_table,
-                model_setup,
-                paths=p_paths,
-                points=table_points,
-                grid=p_grid,
-            ).T
-
-            logger.info("    measured mvn...")
-            start_time = time.time()
-            if n_procs == 1:
-                (
-                    table[f"{model_setup.name}_mvn_vs30"],
-                    table[f"{model_setup.name}_mvn_stdv"],
-                ) = mvn.mvn_table(
-                    table,
-                    sites=measured_sites,
-                    model_name=model_setup.name,
-                ).T
-            else:
-                with Pool(n_procs) as pool:
-                    result = pool.map(
-                            partial(
-                                mvn.mvn_table,
-                                sites=measured_sites,
-                                model_name=model_setup.name,
-                            ),
-                            array_split(table, n_procs, 10_000),
-                        )
-                    (
-                        table[f"{model_setup.name}_mvn_vs30"],
-                        table[f"{model_setup.name}_mvn_stdv"],
-                    ) = np.concatenate(
-                        result,
-                    ).T
-            logger.info(f"    took: {time.time()-start_time:.2f}s")
-        else:
-            logger.info("    model map...")
-            tiffs.append(
-                model_module.model_val_map(p_paths, p_grid, model_table, model_setup)
-            )
-            logger.info("    measured mvn...")
-            tiffs_mvn.append(
-                mvn.mvn_tiff(p_paths.out, model_setup.name, measured_sites, n_procs)
-            )
-
-    if p_geol.update != "off" and p_terr.update != "off":
-        # combined model
-        logger.info("combining geology and terrain...")
-        start_time = time.time()
-        if p_ll is not None:
-            for prefix in ["", "mvn_"]:
-                table[f"{prefix}vs30"], table[f"{prefix}stdv"] = model.combine_models(
-                    p_comb,
-                    table[f"geology_{prefix}vs30"],
-                    table[f"geology_{prefix}stdv"],
-                    table[f"terrain_{prefix}vs30"],
-                    table[f"terrain_{prefix}stdv"],
-                )
-        else:
-            model.combine_tiff(p_paths.out, "combined.tif", p_grid, p_comb, *tiffs)
-            model.combine_tiff(
-                p_paths.out, "combined_mvn.tif", p_grid, p_comb, *tiffs_mvn
-            )
-        logger.info(f"{time.time()-start_time:.2f}s")
-
-    # save point based data and copy qgis project files
-    measured_sites.to_csv(
-        os.path.join(p_paths.out, "measured_sites.csv"), na_rep="NA", index=False
-    )
-    if p_ll is not None:
-        table.to_csv(
-            os.path.join(p_paths.out, "vs30points.csv"), na_rep="NA", index=False
-        )
-        copyfile(
-            os.path.join(sites_load.data, "qgis_points.qgz"),
-            os.path.join(p_paths.out, "qgis.qgz"),
-        )
-    else:
-        copyfile(
-            os.path.join(sites_load.data, "qgis_rasters.qgz"),
-            os.path.join(p_paths.out, "qgis.qgz"),
-        )
-
-    logger.info("complete.")