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

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/testing data and a consistent training strategy, thereby affirming the effectiveness of SeisT. 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.

• For training and evaluation

  Create a new file named yourdata.py in the directory dataset/ to read the metadata and seismograms of the dataset. Then the @register_dataset decorator needs to be used to register your dataset.

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

• For model deployment

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

• Model
  Before starting training, please make sure that your model file is in the directory models/ and is registered by using @register_model. The available models in the project can be inspected using the following method:

  >>> from models import get_model_list
  >>> get_model_list()
  ['eqtransformer', 'phasenet', 'magnet', 'baz_network', 'distpt_network', 'ditingmotion', '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']

  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 configurations of the loss functions, labels, and the corresponding models are in config.py which also provides a detailed explanation of all the fields.

• Start training
  To start 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

  To start 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 a variety of other custom arguments which are not mentioned above. Use the command python main.py --help to see more details.

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

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

To start training 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

To start training 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, val_size, and seed in the test phase must be consistent with that training phase. Otherwise, the test results may be distorted.

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.

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.

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

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