Reproducible material for Self-supervised multi-stage deep learning network for seismic data denoising -Omar M. Saad, Matteo Ravasi, and Tariq Alkhalifah

Project structure

This repository is organized as follows:

• 📂 asset: folder containing logo;
• 📂 data: folder containing data (or instructions on how to retrieve the data

Notebooks

The following notebooks are provided:

• 📙 MSMHA_Marmousi2.ipynb: notebook performing the denoising;
• 📙 Utils.ipynb: notebook including models, patching, and unpatching scripts;

Getting started 👾 🤖

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

Simply run:

./install_env.sh

It will take some time, 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 MSMHA

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{saad2025self,
  title={Self-supervised multi-stage deep learning network for seismic data denoising},
  author={Saad, Omar M and Ravasi, Matteo and Alkhalifah, Tariq},
  journal={Artificial Intelligence in Geosciences},
  pages={100123},
  year={2025},
  publisher={Elsevier}
}