Seismic event P phase picking project

Main framework: Obspy, Seisan, Tensorflow with Keras

Using U-net to generate pick probability

This version is unstable. Do not use it now.

The code is still in the development state, API will change frequently.

Please star us for upcoming updates!

Prerequisite:

• S-File catalog from SEISAN

• SeisComP Data Structure (SDS) database. The directory and file layout of SDS is defined as:

  SDSROOT/YEAR/NET/STA/CHAN.TYPE/NET.STA.LOC.CHAN.TYPE.YEAR.DAY
  

Installation:

• Follow the instructions in the Docker folder to create a Docker container.

• SSH into the Docker container you create.

  ssh username@localhost -p49154
  

• Copy /SeisNN/jupyter.sh to your workspace and execute to start jupyter lab server

  cp /SeisNN/jupyter.sh ~/.
  chmod 777 jupyter.sh
  ./jupyter.sh
  

• Copy /SeisNN/notebook to your workspace

  cp -r /SeisNN/notebook ~/.
  

• Paste the URL with generate token into your local browser

  http://127.0.0.1:8888/?token=36b31a373a9d18cc9b30a50883ad5a3638b19bed47be8074
  

• Go through notebook/00_initialize.ipynb to generate config.yaml

In the notebook folder:

00_initialize

01_load_database

Reference:

PhaseNet | Github

Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211.

U-net

Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

U-net ++ | Github

Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3-11). Springer, Cham.

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