Reproducible material for Diffusion model-based posterior sampling in full waveform inversion and Accelerating Stein variational gradient descent in full waveform inversion with diffusion models - Mohammad Taufik and Tariq Alkhalifah
This repository is organized as follows:
- 📂 asset: contains logo and Matplotlib plotting style file.
- 📂 data: contains synhtetic velocity models.
- 📂 results: contains the experiments.
- 📂 scripts: contains Python codes to run the jobs.
- 📂 notebooks: contains jupyter notebooks experiments.
- 📂 src: contains source code.
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.
Remember to always activate the environment by typing:
conda activate guidedfwi
Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) CPU @ 2.10GHz equipped with a single NVIDIA A100 GPU. Different environment configurations may be required for different combinations of workstation and GPU.