This project requires the use of the conda package management tool for environment management. We recommend using Python 3.9.
Please follow the steps below to set up your development environment:
-
Create a New Conda Environment Use the following command to create a new Conda environment named
ts-eqt, specifying Python version 3.9:conda create -n ts-eqt python=3.9 -
Activate the Environment Activate the environment you just created:
conda activate ts-eqt -
Install Dependencies Install the required packages based on the
requirements.txtfile in the project:pip install -r requirements.txt -
Install the Local seisbench Package
pip install .
Contributions are welcome! If you find any issues or have suggestions for improvements, please submit an issue or pull request.
This project is licensed under the GNU General Public License v3.0.
Peng L, Li L, Mousavi S M, et al. TwoStream-EQT: A microseismic phase picking model combining time and frequency domain inputs. submitted manuscript, 2024.
Mousavi S M, Ellsworth W L, Zhu W, et al. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 2020, 11(1): 3952.
Zhu W, Beroza G C. PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 2019, 216(1): 261-273.
Zhu W, McBrearty I W, Mousavi S M, et al. Earthquake phase association using a Bayesian Gaussian mixture model. Journal of Geophysical Research: Solid Earth, 2022, 127(5): e2021JB023249.
Woollam J, Münchmeyer J, Tilmann F, et al. SeisBench—A toolbox for machine learning in seismology. Seismological Research Letters, 2022, 93(3): 1695-1709.
Zhao M, Xiao Z, Chen S, et al. DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology. Earthquake Science, 2022, 35: 1-11.
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