PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method

1. Install miniconda and requirements

• Download PhaseNet repository

git clone https://github.com/wayneweiqiang/PhaseNet.git
cd PhaseNet

• Install to default environment

conda env update -f=env.yml -n base

• Install to "phasenet" virtual envirionment

conda env create -f env.yml
conda activate phasenet

2. Pre-trained model

Located in directory: model/190703-214543

3. Related papers

• Zhu, Weiqiang, and Gregory C. Beroza. "PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method." arXiv preprint arXiv:1803.03211 (2018).
• Liu, Min, et al. "Rapid characterization of the July 2019 Ridgecrest, California, earthquake sequence from raw seismic data using machine‐learning phase picker." Geophysical Research Letters 47.4 (2020): e2019GL086189.
• Park, Yongsoo, et al. "Machine‐learning‐based analysis of the Guy‐Greenbrier, Arkansas earthquakes: A tale of two sequences." Geophysical Research Letters 47.6 (2020): e2020GL087032.
• Chai, Chengping, et al. "Using a deep neural network and transfer learning to bridge scales for seismic phase picking." Geophysical Research Letters 47.16 (2020): e2020GL088651.
• Tan, Yen Joe, et al. "Machine‐Learning‐Based High‐Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence." The Seismic Record 1.1 (2021): 11-19.

4. Interactive example

See details in the notebook: example_interactive.ipynb

5. Batch prediction

See details in the notebook: example_batch_prediction.ipynb

6. QuakeFlow example

Earthquake detection workflows can be found in the QuakeFlow project.

7. Training

Please let us know of any bugs found in the code. Suggestions and collaborations are welcomed