DA(S)pectralDenoiser: A Spectral Subtraction-based Denoiser for Distributed Acoustic Sensing (DAS) data.

This repository implements a spectral subtraction-based denoising algorithm for DAS data, applied to the Utah FORGE Enhanced Geothermal System (EGS) dataset.

A detailed description of the method can be found in the paper:

G. Pascucci, S. Gaviano, A. Pozzoli, F. Grigoli (2025). Signal Enhancement of Distributed Acoustic Sensing data using a Spectral Subtraction-based Approach. Seismological Research Letters (SRL). doi:10.1785/0220250105

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Setup / Installation

1. Clone the repository

git clone https://github.com/giuliopas/DASpectralDenoiser.git
cd DASpectralDenoiser

2. Create and activate a Python environment

Option A: Using conda (recommended)

conda create -n das_env
conda activate das_env

Option B: using venv

python -m venv das_env
# macOS/Linux
source das_env/bin/activate
# Windows (PowerShell)
.\das_env\Scripts\Activate.ps1

3. Install required packages

conda install pip

pip install --upgrade pip #optional step
pip install -r requirements.txt

Usage:

The file Example_Denoising_FORGE.ipynb contains a tutorial on how to run the code on a test DAS dataset from Utah FORGE (April 2022 stimulation).

Additional Notes:

The example DAS data (.tdms format) used in Example_Denoising_FORGE.ipynb can be obtained in two ways:

1. Download manually from Zenodo: https://doi.org/10.5281/zenodo.17554490
2. Download directly from the notebook using Python:

   import urllib.request
   fname = 'FORGE_DFIT_UTC_20220421_144609.398.tdms'
   url = "https://zenodo.org/records/17554490/files/FORGE_DFIT_UTC_20220421_144609.398.tdms"
   urllib.request.urlretrieve(url, fname)