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 ([email protected])

• Links
• Installation
• Tutorials
• Related Software Packages
• A Quick Example
• Reference
• Test Set
• Contributing
• Reporting Bugs
• Studies That Used EqT

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

• Paper: https://rdcu.be/b58li

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

  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

See either:

https://rebrand.ly/EQT-documentations

and/or

https://rebrand.ly/EQT-examples

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.

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.

    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)

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}
}

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

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.

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

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.

• di Uccio, F. S., Scala, A., Festa, G., Picozzi, M., & Beroza, G. C. (2022). Comparing and integrating artificial intelligence and similarity search detection techniques: application to seismic sequences in Southern Italy. Authorea Preprints.

• Gong, J., & Fan, W. (2022). Seismicity, fault architecture, and slip mode of the westernmost Gofar transform fault. Journal of Geophysical Research: Solid Earth, 127(11), e2022JB024918.

• Shiddiqi, H. A., Ottemöller, L., Rondenay, S., Custódio, S., Halpaap, F., & Gahalaut, V. K. (2023). Comparison of Earthquake Clusters in a Stable Continental Region: A Case Study from Nordland, Northern Norway. Seismological Research Letters.

• Chin, S. J., Sutherland, R., Savage, M. K., Townend, J., Collot, J., Pelletier, B., ... & Illsley‐Kemp, F. (2022). Earthquakes and Seismic Hazard in Southern New Caledonia, Southwest Pacific. Journal of Geophysical Research: Solid Earth, e2022JB024207.

• Miller, M. S., Pickle, R., Murdie, R., Yuan, H., Allen, T. I., Gessner, K., ... & Whitney, J. (2023). Southwest Australia Seismic Network (SWAN): Recording Earthquakes in Australia’s Most Active Seismic Zone. Seismological Research Letters.

• Zhang, Z., Deng, Y., Qiu, H., Peng, Z., & Liu‐Zeng, J. (2022). High‐Resolution Imaging of Fault Zone Structure Along the Creeping Section of the Haiyuan Fault, NE Tibet, From Data Recorded by Dense Seismic Arrays. Journal of Geophysical Research: Solid Earth, 127(9), e2022JB024468.

• Bannister, S., Bertrand, E. A., Heimann, S., Bourguignon, S., Asher, C., Shanks, J., & Harvison, A. (2022). Imaging sub-caldera structure with local seismicity, Okataina Volcanic Centre, Taupo Volcanic Zone, using double-difference seismic tomography. Journal of Volcanology and Geothermal Research, 431, 107653.

• Michailos, K., Carpenter, N. S., & Hetényi, G. (2021). Spatio-temporal evolution of intermediate-depth seismicity beneath the Himalayas: Implications for metamorphism and tectonics. Frontiers in Earth Science, 859.

• Gong, J., Fan, W., & Parnell‐Turner, R. (2022). Microseismicity Indicates Atypical Small‐Scale Plate Rotation at the Quebrada Transform Fault System, East Pacific Rise. Geophysical Research Letters, 49(3), e2021GL097000.

• Jiang, C., Zhang, P., White, M. C., Pickle, R., & Miller, M. S. (2022). A Detailed Earthquake Catalog for Banda Arc–Australian Plate Collision Zone Using Machine‐Learning Phase Picker and an Automated Workflow. The Seismic Record, 2(1), 1-10.

• Chuang, L. Y., Peng, Z., Lei, X., Wang, B., Liu, J., Zhai, Q., & Tu, H. (2023). Foreshocks of the 2010 Mw 6.7 Yushu, China Earthquake Occurred Near an Extensional Step‐Over. Journal of Geophysical Research: Solid Earth, 128(1), e2022JB025176.

• Kemna, K. B., Roth, M. P., Wache, R. M., Harrington, R. M., & Liu, Y. Small magnitude events highlight the correlation between hydraulic fracturing injection parameters, geological factors, and earthquake occurrence. Geophysical Research Letters, e2022GL099995.

• Cianetti, S., Bruni, R., Gaviano, S., Keir, D., Piccinini, D., Saccorotti, G., & Giunchi, C. (2021). Comparison of deep learning techniques for the investigation of a seismic sequence: An application to the 2019, Mw 4.5 Mugello (Italy) earthquake. Journal of Geophysical Research: Solid Earth, 126(12), e2021JB023405.

• van der Laat, L., Baldares, R. J., Chaves, E. J., & Meneses, E. (2021, November). OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoring in Costa Rica. In 2021 IEEE 3rd International Conference on BioInspired Processing (BIP) (pp. 1-6). IEEE.

• Caudron, C., Aoki, Y., Lecocq, T., De Plaen, R., Soubestre, J., Mordret, A., ... & Terakawa, T. (2022). Hidden pressurized fluids prior to the 2014 phreatic eruption at Mt Ontake. Nature communications, 13(1), 1-9.

• WEN, X., SHEN, X., & ZHOU, Q. (2022). Study on the characters of the aftershocks of Beiliu 5.2 earthquake using machine learning method and dense nodal seismic array. Chinese Journal of Geophysics, 65(9), 3297-3308.

• Sheng, Y., Pepin, K. S., & Ellsworth, W. L. (2022). On the Depth of Earthquakes in the Delaware Basin: A Case Study along the Reeves–Pecos County Line. The Seismic Record, 2(1), 29-37.

• Walter, J. I., Ogwari, P., Thiel, A., Ferrer, F., & Woelfel, I. (2021). easyQuake: Putting machine learning to work for your regional seismic network or local earthquake study. Seismological Research Letters, 92(1), 555-563.

• Shi, P., Grigoli, F., Lanza, F., Beroza, G. C., Scarabello, L., & Wiemer, S. (2022). MALMI: An Automated Earthquake Detection and Location Workflow Based on Machine Learning and Waveform Migration. Seismological Research Letters.