Earthquake Detection and P-Wave Arrival Time Picking Using Capsule Neural Network

Earthquake detection is an essential step in observational earthquake seismology. We propose to utilize a capsule neural network (CapsNet) to automatically identify and detect earthquakes. CapsNet is the new generation of deep learning architecture. It has the capability of learning with a great generalization performance from a small dataset. We train the CapsNet using 50x0025; of the Southern California seismic data (2.25 million 4-s-three-component seismic windows) and use 222 395 waveforms from different seismic areas to evaluate the CpasNet performance, e.g., western United States, Europe, and Japan. As a result, the CapsNet misses 367 events and detects 217 305 events with an accuracy of 97.71x0025;. Among these picked events, 210 498 events have an arrival time error below 0.2 s (96.86x0025;) and 197968 waveforms with an arrival time error below 0.1 s (91.11x0025;). The CapsNet precision, recall, and F1-score are 97.78x0025;, 99.83x0025;, and 98.79x0025;, respectively. In addition, the CapsNet is tested using 100 000 60-s-three-component seismic noise waveforms. CapsNet shows a low false alarms rate of 1384, which gives the CapsNet an accuracy of 98.61x0025;. In addition, CapsNet is tested using continuous seismic data associated with the 24-hours microearthquakes swarm that occurred in the Arkansas area. Accordingly, the CapsNet detects 221 earthquakes and releases 37 false alarms with a detection accuracy of 85.65x0025;. CapsNet detects many microearthquakes with a small magnitude, as low as x2212;1.3 Ml, and detects earthquakes that have a low signal-to-noise ratio (SNR), e.g., as low as x2212;8.07 dB. The results of the CapsNet are compared to the benchmark methods, e.g., short-time average/long-time average (STA/LTA) and GPD methods. The CapsNet shows the highest picking accuracy and outperforms the benchmark methods.

Automated summary

The study on earthquake detection using Capsule Neural Network (CapsNet) has shown promising results, with high accuracy in Southern California seismic data and good performance in other seismic regions. CapsNet also demonstrates a low false alarm rate for seismic noise and high detection accuracy for microearthquakes, highlighting its potential for earthquake detection applications.