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