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smousavi05 / EQTransformer

EQTransformer, a python package for earthquake signal detection and phase picking using AI.
https://rebrand.ly/EQT-documentations
MIT License
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attention-mechanism deep-learning detection earthquakes global lstm-neural-networks multi-task-learning neural-network phase-picking stead transformer

readme

event

An AI-Based Earthquake Signal Detector and Phase Picker

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Description

EQTransformer is an AI-based earthquake signal detector and phase (P&S) picker based on a deep neural network with an attention mechanism. It has a hierarchical architecture specifically designed for earthquake signals. EQTransformer has been trained on global seismic data and can perform detection and arrival time picking simultaneously and efficiently. In addition to the prediction probabilities, it can also provide estimated model uncertainties.

The EQTransformer python 3 package includes modules for downloading continuous seismic data, preprocessing, performing earthquake signal detection, and phase (P & S) picking using pre-trained models, building and testing new models, and performing a simple phase association.

Developer: S. Mostafa Mousavi (smousavi05@gmail.com)

Links

Installation

EQTransformer supports a variety of platforms, including macOS, Windows, and Linux operating systems. Note that you will need to have Python 3.x (3.6 or 3.7) installed. The EQTransformer Python package can be installed using the following options:

Via Anaconda (recommended):

conda create -n eqt python=3.7

conda activate eqt

conda install -c smousavi05 eqtransformer
Note: sometimes you need to keep repeating executing the last line multiple time to succeed.

Via PyPI:

If you already have Obspy installed on your machine, you can get EQTransformer through PyPI:

pip install EQTransformer

From source:

The sources for EQTransformer can be downloaded from the GitHub repo.

Note: the GitHub version has been modified for Tensorflow 2.5.0

You can either clone the public repository:

git clone git://github.com/smousavi05/EQTransformer

or (if you are working on Colab)

pip install git+https://github.com/smousavi05/EQTransformer

Once you have a copy of the source, you can cd to EQTransformer directory and install it with:

python setup.py install

If you have installed EQTransformer Python package before and want to upgrade to the latest version, you can use the following command:

pip install EQTransformer -U
To install EqT on M1 laptop with python>=3.9 from the source (GitHub) code by changing tensorflow to tensorflow-maco in the setup.py and follow these steps:
  conda create -n eqt python=3.10
  conda activate eqt
  conda install -c apple tensorflow-deps
  conda install obspy jupyter pandas
  pip install tensorflow-macos
  python3 setup.py install

Tutorials

See either:

https://rebrand.ly/EQT-documentations

and/or

https://rebrand.ly/EQT-examples

Important Notes:

There is not much difference between the two models (i.e. original and conservative models) regarding the network architecture (only 1 or 2 layers). The main difference comes from the training procedure and the hyperparameters used for data augmentation during the training.

Original model (the one in the paper) has been optimized to minimize the false negative rate (maximize the number of detected events). This is a good choice when high false positive rates is not an issue in your application. For instance, in event location workflow most of the false positives will automatically be removed during the association, location, and - especially - relocation procedure. You can use the original model with higher threshold values (~ 0.3 for P and S and 0.5 for detection).

Conservative Model, on the other hand, has been optimized to minimize the false positive rate (maximum number of valid detections). This model is useful when your application can not tolerate a high false positive rate. For example, in travel time tomography or using the detected events by EqT as the templates for template matching. You should use this model with a much lower threshold levels (0.03 for P and S).

Suggestions for Optimal Catalog Building: 1) use the original model for detection and picking and have a cross-correlation-based event relocation at the end to clean up false positives. 2) use detected events by the conservative model as template events and then perform a complementary template-matching to detect the rest of events. See this paper (https://academic.oup.com/gji/advance-article-abstract/doi/10.1093/gji/ggac487/6881721) as an example.

If you feel some larger events are missed while smaller ones are detected: Use larger overlapping value (e.g. 0.9) for moving windows.

Note: to run the notebook examples, you may need to reinstall the jupyter on the same environment that EQTransformer has been installed.

Related Software Packages:

QuakePhase (https://github.com/speedshi/quakephase) the quakephase can be used to largely enhance the results of pre-trained EQT model for picking phases on traces recorded in epicentral distances > 100 km, different types of instruments, larger events, etc.

Blocky Earthquake Transformer (https://github.com/maihao14/BlocklyEQTransformer) is a user-interface no-code-based platform that makes it easy to fine-tune EqT for specific data/regions. It also provides a user-friendly interface to create your own EqT model and train it without dealing with coding or knowing in-depth ML.

Siamese Earthquake Transformer (https://github.com/MrXiaoXiao/SiameseEarthquakeTransformer) performs a secondary template-matching-type post-processing step (by using the learned features in EqT layers as the templates and measure the similarities using a Siamese neural network instead of cross-correlation) to reduce the false-negative rate of the EqT retrieving previously missed phase picks in low SNR seismograms.

EdgePhase (https://github.com/lovelytt0/EdgePhase) Graph-based EQTransformer for picking phases using multi-station data.

OBSTransformer (https://github.com/alirezaniki/OBSTransformer)) is a transfer-learned seismic phase picker for Ocean Bottom Seismometer (OBS) data adopted from the EqTransformer model. OBSTransformer has been trained on an auto-labeled tectonically inclusive OBS dataset comprising ~36k earthquake and 25k noise samples.

QuakeLabeler (https://maihao14.github.io/QuakeLabeler/) is a software package that can be used to create labeled training dataset for EQTransformer (i.e. STEAD format).

SeisBench (https://github.com/seisbench/seisbench/) is an open source benchmarking package with PyTorch implementation of EgT that makes it easy to either apply pre-trained EqT model, retrain it, or compare it with other models.

MALMI (https://github.com/speedshi/MALMI/) is an earthquake monitoring pipline, i.e. picking and event location determination, that uses EqT for event detection and phase picking.

easyQuake (https://github.com/jakewalter/easyQuake) is an earthquake monitoring pipline, i.e. detection, picking, association, location, and magnitude determination, that includes EqT and other DL-pickers for event detection and phase picking.

A Quick Example


    from EQTransformer.core.mseed_predictor import mseed_predictor

    mseed_predictor(input_dir='downloads_mseeds',   
                    input_model='ModelsAndSampleData/EqT_model.h5',
                    stations_json='station_list.json',
                    output_dir='detection_results',
                    detection_threshold=0.2,                
                    P_threshold=0.1,
                    S_threshold=0.1, 
                    number_of_plots=10,
                    plot_mode='time_frequency',
                    batch_size=500,
                    overlap=0.3)

Reference

Mousavi, S.M., Ellsworth, W.L., Zhu, W., Chuang, L, Y., and Beroza, G, C. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat Commun 11, 3952 (2020). https://doi.org/10.1038/s41467-020-17591-w

BibTeX:

@article{mousavi2020earthquake,
    title={Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking},
    author={Mousavi, S Mostafa and Ellsworth, William L and Zhu, Weiqiang and Chuang, Lindsay Y and Beroza, Gregory C},
    journal={Nature Communications},
    volume={11},
    number={1},
    pages={1--12},
    year={2020},
    publisher={Nature Publishing Group}
}

License

The EQTransformer package is distributed under the MIT license, a permissive open-source (free software) license.

If you think that EqT does not detect all of the events in your experiment or produces too many false positives, please let us know. We are always interested to learn more about out of distribution cases to be able to improve our models.

Test Set

test.npy fine in the ModelsAndSampleData folder contains the trace names for the test set used in the paper. Based on these trace names you can retrieve our test data along with their labels from STEAD. Applying your model to these test traces you can directly compare the performance of your model to those in Tables 1, 2, and 3 in the paper. The remaining traces in the STEAD were used for the training (85 %) and validation (5 %) respectively.

Contributing

If you would like to contribute to the project as a developer, follow these instructions to get started:

  1. Fork the EQTransformer project (https://github.com/smousavi05/EQTransformer)
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create a new Pull Request

Reporting Bugs

Please see https://github.com/smousavi05/EQTransformer/issues?q=is%3Aissue+is%3Aclosed for a list of issues/bugs that have been already reported/fixed before filing a new bug report.

Report bugs at https://github.com/smousavi05/EQTransformer/issues.

If you are reporting a bug, please include:

Some of the studies in which EqT has been used: