Reproducible material for Wavenumber-aware diffusion sampling to regularize multi-parameter elastic full waveform inversion

Project structure

This repository is organized as follows:

• 📂 asset: folder containing logo.
• 📂 data: a folder containing the subsampled velocity models used to train the diffusion model.
• 📂 saves: a folder containing the trained diffusion model's weight, diffusion.pt. It needs to be downloaded from the Restricted Area above.
• 📂 scripts: a set of Python scripts used to run diffusion training, diffusion with ILVR sampling, and EFWI.
• 📂 src: a folder containing routines for the ilvrefwi source file.

Getting started 👾 🤖

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

To install the environment, run the following command:

./install_env.sh

It will take some time, but if, in 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 ilvrefwi

Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) Silver 4316 CPU @ 2.30GHz equipped with a single NVIDIA A100 GPU. Different environment configurations may be required for different combinations of workstation and GPU.