Official reproducible material for Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network

Project structure

This repository is organized as follows:

• 📂 asset: folder containing logo;
• 📂 data: folder containing data;
• 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper;
• 📂 Matlab_CWT_Version: includes a more stable version for the 2D CWT using Matlab;
• 📂 outputs: includes the denoised data obtained by the proposed framework.

Getting started 👾 🤖

To ensure the reproducibility of the results, we suggest using the DASDL.yml file when creating an environment.

Simply run:

./install_env.sh

It will take some time, but if at the end you see the word Done! on your terminal you are ready to go.

Remember to always activate the environment by typing:

conda activate DASDL

Scripts (Fully based on Python)

Go to folder notebooks and

run the file named "DASDL_Main"

After running, go to folder outputs in the root_path, and you will find the denoised data obtained by the proposed framework.

Scripts (CWT based on Matlab)

This is an alternative way to run the code using a more stable 2D CWT version using Matlab. It provides less signal leakage compared to the 2D CWT version. Go to folder Matlab_CWT_Version and

1- run the file named "Prepare_CWT.m", it will obtain the Band-pass filter data and the CWT scale.

2- run the Python file named "DASDL_Main"

After running, go to folder outputs in the root_path, and you will find the denoised data obtained by the proposed framework.

Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) CPU @ 2.10GHz equipped with a single NVIDIA GEForce RTX 3090 GPU. Different environment configurations may be required for different combinations of workstation and GPU.

Cite us

@article{saad2024noise,
  title={Noise Attenuation in Distributed Acoustic Sensing Data Using a Guided Unsupervised Deep Learning Network},
  author={Saad, Omar M and Ravasi, Matteo and Alkhalifah, Tariq},
  journal={Geophysics},
  volume={89},
  number={6},
  pages={1--62},
  year={2024},
  publisher={Society of Exploration Geophysicists}
}