Reproducible material for
Deep Learning-based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data - Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah
This repository is organized as follows:
- 📂 asset: folder containing logo;
- 📂 data: folder containing data for network training;
- 📂 MEDL: python libraries containing the main code and all packages;
- 📂 network: folder used for holding the trained networks;
- 📂 scripts: python scripts used to run experiments;
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 forge
Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) CPU @ 2.10GHz equipped with a single NVIDIA RTX A6000. Different environment configurations may be required for different combinations of workstation and GPU.
Download a synthetic dataset for training the network from here. Please kindly place them inside the data directory.
Go to the folder scripts
and run in the terminal:
bash run.sh
If you find our work useful, please cite:
@article{yang2024deep,
title={Deep Learning‐Based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data},
doi={10.1029/2024JH000365},
author={Yang, Yuanyuan and Saad, Omar M and Alkhalifah, Tariq},
journal={Journal of Geophysical Research: Machine Learning and Computation},
year={2024},
publisher={Wiley Online Library}
}