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

• Links
• Reference
• Installation
• Tutorials
• A Quick Example
• Test Set
• Contributing
• Reporting Bugs

• Documentation: https://rebrand.ly/EQT-documentations

• Paper:https://rdcu.be/b58li

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}
}

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:

conda create -n eqt python=3.7

conda activate eqt

conda install -c smousavi05 eqtransformer 

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

pip install EQTransformer

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

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

See either:

https://rebrand.ly/EQT-documentations

and/or

https://rebrand.ly/EQT-examples

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

    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)

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 Tabels 1, 2, and 3 in the paper. The remaining traces in the STEAD were used for the training (85 %) and validation (5 %) respectively.

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

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

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

If you are reporting a bug, please include:

• Your operating system name and version.
• Any details about your local setup that might be helpful in troubleshooting.
• Detailed steps to reproduce the bug.