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

Abstract

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 50% 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 97.71%. Among these picked events, 210 498 events have an arrival time error below 0.2 s (96.86%) and 197968 waveforms with an arrival time error below 0.1 s (91.11%). The CapsNet precision, recall, and F1-score are 97.78%, 99.83%, and 98.79%, 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 98.61%. 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 85.65%. CapsNet detects many microearthquakes with a small magnitude, as low as -1.3 Ml, and detects earthquakes that have a low signal-to-noise ratio (SNR), e.g., as low as -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.