Reproducible material for

Joint Microseismic Event Detection and Location with a Detection Transformer - Yang Y., Birnie C., Alkhalifah T.

Click here to access the paper.

Project structure

This repository is organized as follows:

• 📂 asset: folder containing logo;
• 📂 data: folder containing data for network training and testing;
• 📂 MicroseismicDETR: python libraries containing the main code and all packages;
• 📂 network: folder used for holding the trained networks;
• 📂 notebooks: jupyter notebooks reproducing the experiments in the paper (see below for more details);
• 📂 scripts: python scripts used to run experiments;

Notebooks

The following notebooks are provided:

• 📙 test_network.ipynb: notebook performing predictions on one-, two-, three-event input data segments;

Getting started 👾 🤖

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

Simply run:

bash 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 MEDL

Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) CPU @ 2.10GHz equipped with a single NVIDIA A100 Tensor Core GPU. Different environment configurations may be required for different combinations of workstation and GPU.

Data preparation

Download synthetic datasets for training and testing the network from here. Please kindly place them inside the data directory.

Run experiments

Under the folder scripts, kindly run in the terminal:

bash train_network.sh

Cite us

@article{yang2023joint,
  title={Joint microseismic event detection and location with a detection transformer},
  author={Yang, Yuanyuan and Birnie, Claire and Alkhalifah, Tariq},
  journal={Geophysical Prospecting},
  pages={e70040},
  year={2023},
  publisher={Wiley Online Library}
}```