WIN2PhaseNet

Related Publication

If you use this code or data, please cite the following paper:
Rintaroh Suzuki et al., The forearc seismic belt: A fluid pathway constraining down-dip megathrust earthquake rupture. Science 389, 190-194 (2025). https://doi.org/10.1126/science.adt6389

Summary

• Tool to make data for prediction and training of PhaseNet (Zhu and Beroza, 2019) from WIN/WIN32 (hereafter just 'WIN') format waveform file and pick list.
• High-speed processing is possible through the use of fwin module (Maeda, 2019) written in fortran and multi-thread processing.
• Easy to run on various OS by using docker.
• Provides the simplified operating procedure for PhaseNet and a docker environment to run PhaseNet.

Requirements

• OS
  Support Windows, macOS and Linux

• (Only required for Windows) Git Bash
  https://gitforwindows.org/
  For Windows, run "Git Bash" and use it to execute commands for following steps.

• docker
  

  • Installation
    For Windows and macOS, install "Docker Desktop" and run it to activate docker.
    https://docs.docker.com/get-docker/
    For Linux, install "Docker Engine".
    https://docs.docker.com/engine/install/
    

  • (Only required for Linux) Create the docker group and add your user
    https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user
    

  • Verify installation
    

    $ docker run hello-world
    ...
    Hello from Docker!
    This message shows that your installation appears to be working correctly.
    ...
    

Usage

• Installation

  $ git clone https://github.com/rintr-suzuki/WIN2PhaseNet.git
  $ cd WIN2PhaseNet
  

• Execution

  WIN2PhaseNet

  $ ./WIN2PhaseNet.bash -m cont --tbl2lst
  # See 'out' directory for the result.
  

  PhaseNet prediction

  $ ./PhaseNet.bash --model_dir=src/PhaseNet/model/190703-214543 --data_dir=out/npz --data_list=out/npz.csv --amplitude --plot_figure
  # See 'results' directory for the result.
  

• See following documents for the detailed information.
  For PhaseNet prediction, see here.
  For PhaseNet training, see here.
  

• See here for the tips of this tool.

Acknowledgements

A part of this program was created by Uchida, N and Matsuzawa, T.

References

• Maeda, T (2019), Development of a WIN/WIN32 format seismic waveform data reader. The 2019 SSJ Fall Meeting. (In Japanese)
• Saito, M (1978), An automatic design algorithm for band selective recursive digital filters, Geophysical exploration, 31, 240-263. (In Japanese)
• Takagi, R., Uchida, N., Nakayama, T., Azuma, R., Ishigami, A., Okada, T., Nakamura, T., & Shiomi, K. (2019), Estimation of the orientations of the S-net cabled ocean-bottom sensors. Seismological Research Letters, 90(6), 2175–2187. https://doi.org/10.1785/0220190093
• Zhu, W., & Beroza, G. C. (2019), PhaseNet: A deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423