Reproducible material for Seismic deblending with a hard data constraint - Ravasi M. submitted to IMAGE 2024.

Project structure

This repository is organized as follows:

• 📂 pocsdeblend: python library containing routines to perform deblending with various proximal solvers;
• 📂 data: folder where input data must be placed;
• 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper (see below for more details);

NOTE: due to their large size, the datasets used in this repository cannot be shared directly in this repository. Refer to the README file in the data folder for more details.

Notebooks

The following notebooks are provided:

• 📂 seam1:

  • 📙 Deblending_Seam1OBC.ipynb: notebook performing deblending with different solvers and frequency-independent thresholding;
  • 📙 Deblending_Seam1OBC_freqthresh.ipynb: notebook performing deblending with different solvers and frequency-dependent thresholding;
  • 📙 Deblending_Seam1OBC_freqthresh_gpu.ipynb: gpu implementation of Deblending_Seam1OBC_freqthresh;
  • 📙 Deblending_Seam1OBC_visualization.ipynb: notebook used to create the final figures in the paper.

• 📂 ove3dnew:

  • 📙 3DBlending_FKIHT.ipynb: notebook performing deblending with IHT solver and frequency-independent thresholding;
  • 📙 3DBlending_FKHQS.ipynb: notebook performing deblending with HQS solver and frequency-independent thresholding;
  • 📙 3DBlending_FKADMM.ipynb: notebook performing deblending with ADMM solver and frequency-independent thresholding;
  • 📙 3DBlending_Visualization.ipynb: notebook used to create the final figures in the paper;
  • 📙 3DBlending_FKIHT_freqthresh.ipynb: notebook performing deblending with IHT solver and frequency-dependent thresholding;
  • 📙 3DBlending_FKHQS_freqthresh.ipynb: notebook performing deblending with HQS solver and frequency-dependent thresholding;
  • 📙 3DBlending_FKADMM_freqthresh.ipynb: notebook performing deblending with ADMM solver and frequency-dependent thresholding;
  • 📙 3DBlending_Visualization_freqthresh.ipynb: notebook used to create the figures from frequency-dependent thresholding experiments.

Getting started 👾 🤖

To ensure reproducibility of the results, we suggest using the environment.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. After that you can simply install your package:

pip install .

or in developer mode:

pip install -e .

Remember to always activate the environment by typing:

conda activate pocsdeblend

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.