ArrayConvNet

Using convolutional neural networks (CNNs), we propose a new technique for automatic earthquake detection and 4D localization. This repository contains the code and trained models to write our paper. The full insights and report should be referenced at our paper submission.

Data

This study is based on the earthquake information and waveform data from the Hawaiian Volcano Observatory (HVO), run by the USGS. The USGS earthquake catalog is obtained from the USGS, last accessed March 23, 2020. The waveform data is available from the IRIS DMC.

Special thanks to researchers and staff at HVO for collecting the seismic data and providing the earthquake catalog used in this study and Quoqing Lin for providing the relocated earthquakes that are used to assess the effects of location accuracy in the training data set.

Preprocessing the data

To process the public raw trace data, we use two scripts:

1. generate_detect_data_3cRand.py
2. generate_event_array3c4d.py

The first script processes all detection data given earthquake and noise events. It creates the files for the training and test data for training the detection model.

Similarly, the second script processes all earthquake event data and their labels. It creates the files for the training and test data for training the localization model.

Training the models

Once all data is processed, we train and test the detection and localization model with the scripts detect_3c.py and predict_location3c4d.py, respectively. The output of these scripts are the trained models with their accuracies on test datasets.

Trained models

For ease of use, we've included the trained detection and localization models. The directory models contains:

• SeisConvNetDetect_sortedAbs50s.pth: trained model for detection
• SeisConvNetLoc_NotAbs2017Mcut50s.pth: trained model for 4d (latitude, longitude, depth, and time) localization

Together, they create ArrayConvNet.

Validation on continuous data

Earthquake catalogs usually represent only a subset of earthquakes that occurred, with detection and localization limited by signal-to-noise ratios in seismic records, number of detected stations, and other factors. Our training data from the USGS catalog for Hawaii is no exception. So while our ArrayConvNet performs well for the test data set, we evaluate its true efficacy on continuous data. In validate_consecution.py, we pass in continuous seismic readings and evaluate the results.