Reproducible material for

Deep Learning-based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data - Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah

Project structure

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;

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.

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.

Data preparation

Download a synthetic dataset for training the network from here. Please kindly place them inside the data directory.

Run experiments

Go to the folder scripts and run in the terminal:

bash run.sh

Cite us

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}
}