• SeisT Architecture
• Introduction
• Usage
  • Data preparation
  • Training
  • Fine-tuning
  • Testing
• Citation
• Reporting Bugs
• Acknowledgement
• License

SeisT Architecture

Introduction

SeisT is a backbone for seismic signal processing, which can be used for multiple seismic monitoring tasks such as earthquake detection, seismic phase picking, first-motion polarity classification, magnitude estimation, back-azimuth estimation, and epicentral distance estimation.

This repository also provides some baseline models implemented by Pytorch under ./models, such as PhaseNet, EQTransformer, DitingMotion, MagNet, BAZ-Network, and distPT-Network.

NOTE: The model weights included in this repository serve as the basis for performance evaluation in the paper. They have been evaluated using identical training and testing data and a consistent training regimen, thereby affirming the architecture's validity. Nevertheless, if you intend to employ these models in practical engineering applications, it is crucial to retrain the SeisT models with larger datasets to align with the specific demands of engineering applications.

Usage

Data Preparation

• For training and evaluation

  Create a new file named yourdata.py in the directory dataset/ to read the metadata and seismograms of the dataset. And you need to use @register_dataset decorator to register your dataset.

  (Please refer to the code samples datasets/DiTing.py and datasets/PNW.py)

• For model deployment

  Follow the steps in demo_predict.py and overload the load_data function.

Training

• Model
  Before starting training, please make sure that your model code is in the directory models/ and register it using the @register_model decorator. You can inspect the models available in the project using the following method:

  >>> from models import get_model_list
  >>> get_model_list()
  ['seist_s_dpk', 'seist_m_dpk', 'seist_l_dpk', 'seist_s_pmp', 'seist_m_pmp', 'seist_l_pmp', 'seist_s_emg', 'seist_m_emg', 'seist_l_emg', 'seist_s_baz', 'seist_m_baz', 'seist_l_baz', 'seist_s_dis', 'seist_m_dis', 'seist_l_dis', 'eqtransformer', 'phasenet', 'magnet', 'baz_network', 'distpt_network', 'ditingmotion']

  The task names and their abbreviations in this project are shown in the table below:

  Task	Abbreviation
  Detection & Phase Picking	dpk
  First-Motion Polarity Classification	pmp
  Back-Azimuth Estimation	baz
  Magnitude Estimation	emg
  Epicentral Distance Estimation	dis

• Model Configuration
  The configuration of the loss function and model labels is in config.py, and a more detailed explanation is provided in this file.

• Start training
  If you are training with a CPU or a single GPU, please use the following command to start training:

  python main.py \
    --seed 0 \
    --mode "train_test" \
    --model-name "seist_m_dpk" \
    --log-base "./logs" \
    --device "cuda:0" \
    --data "/root/data/Datasets/Diting50hz" \
    --dataset-name "diting" \
    --data-split true \
    --train-size 0.8 \
    --val-size 0.1 \
    --shuffle true \
    --workers 8 \
    --in-samples 8192 \
    --augmentation true \
    --epochs 200 \
    --patience 30 \
    --batch-size 500

  If you are training with multiple GPUs, please use torchrun to start training:

  torchrun \
    --nnodes 1 \
    --nproc_per_node 2 \
    main.py \
      --seed 0 \
      --mode "train_test" \
      --model-name "seist_m_dpk" \
      --log-base "./logs" \
      --data "/root/data/Datasets/Diting50hz" \
      --dataset-name "diting" \
      --data-split true \
      --train-size 0.8 \
      --val-size 0.1 \
      --shuffle true \
      --workers 8 \
      --in-samples 8192 \
      --augmentation true \
      --epochs 200 \
      --patience 30 \
      --batch-size 500

  There are also many other custom arguments, see main.py for more details.

Fine-tuning

The following table provides the pre-trained checkpoints used in the paper:

Task	Train set	SeisT-S	SeisT-M	SeisT-L
Detection & Phase Picking	DiTing	download	download	download
First-Motion Polarity Classification	DiTing	download	download	download
Back-Azimuth Estimation	DiTing	download	download	download
Magnitude Estimation	DiTing	download	download	download
Magnitude Estimation	PNW	download	download	download
Epicentral Distance Estimation	DiTing	download	download	download

Use the "--checkpoint" argument to pass in the path of the pre-training weights.

Testing

If you are testing with a CPU or a single GPU, please use the following command to start testing:

python main.py \
  --seed 0 \
  --mode "test" \
  --model-name "seist_m_dpk" \
  --log-base "./logs" \
  --device "cuda:0" \
  --data "/root/data/Datasets/Diting50hz" \
  --dataset-name "diting" \
  --data-split true \
  --train-size 0.8 \
  --val-size 0.1 \
  --workers 8 \
  --in-samples 8192 \
  --batch-size 500

If you are testing with multiple GPUs, please use torchrun to start testing:

torchrun \
  --nnodes 1 \
  --nproc_per_node 2 \
  main.py \
    --seed 0 \
    --mode "test" \
    --model-name "seist_m_dpk" \
    --log-base "./logs" \
    --data "/root/data/Datasets/Diting50hz" \
    --dataset-name "diting" \
    --data-split true \
    --train-size 0.8 \
    --val-size 0.1 \
    --workers 8 \
    --in-samples 8192 \
    --batch-size 500

It should be noted that the train_size and val_size during testing must be consistent with that during training, and the seed must be consistent. Otherwise, the test results may be distorted.

Citation

Paper: https://doi.org/10.1109/TGRS.2024.3371503

If you find this repo useful in your research, please consider citing:

@ARTICLE{10453976,
  author={Li, Sen and Yang, Xu and Cao, Anye and Wang, Changbin and Liu, Yaoqi and Liu, Yapeng and Niu, Qiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={SeisT: A Foundational Deep-Learning Model for Earthquake Monitoring Tasks}, 
  year={2024},
  volume={62},
  pages={1-15},
  doi={10.1109/TGRS.2024.3371503}
}

The baseline models used in this paper:

• PhaseNet
  Zhu, W., & Beroza, G. C. (2019). PhaseNet: A deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261-273.

• EQTransformer
  Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11(1), 3952.

• DiTingMotion
  Zhao, M., Xiao, Z., Zhang, M., Yang, Y., Tang, L., & Chen, S. (2023). DiTingMotion: A deep-learning first-motion-polarity classifier and its application to focal mechanism inversion. Frontiers in Earth Science, 11, 1103914.

• MagNet
  Mousavi, S. M., & Beroza, G. C. (2020). A machine‐learning approach for earthquake magnitude estimation. Geophysical Research Letters, 47(1), e2019GL085976.

• BAZ-Network
  Mousavi, S. M., & Beroza, G. C. (2020). Bayesian-Deep-Learning Estimation of Earthquake Location From Single-Station Observations. IEEE Transactions on Geoscience and Remote Sensing, 58(11), 8211-8224.

Reporting Bugs

Report bugs at https://github.com/senli1073/SeisT/issues.

If you are reporting a bug, please include:

• Operating system version.
• Versions of Python and libraries such as Pytorch.
• Steps to reproduce the bug.

Acknowledgement

This project refers to some excellent open source projects: PhaseNet, EQTransformer, DiTing-FOCALFLOW, MagNet, Deep-Bays-Loc, PNW-ML and SeisBench.

License

Copyright S.Li et al. 2023. Licensed under an MIT license.