Combining Convolutional Neural Network with Physics- Based Features in Shallow and Intermediate-depth Earthquake Discrimination

Abstract

Abstract It is crucial for earthquake early warning (EEW) to distinguish earthquakes of various focal depths accurately and reliably. However, this task is a significant challenge due to the difficulty in interpreting the underlying physical mechanisms of earthquakes of different focal depths. In this study, we proposed an algorithm that combines a convolutional neural network with physics parameter-based features (CNN-PP) to discriminate between shallow and intermediate-depth earthquakes. A total of 3586 earthquakes in Japan recorded by the K-NET and KiK-net strong-motion seismograph networks from 2003 to 2020 were collected and processed as research data; 38081 Three-channel acceleration seismic records were obtained by station record interception, baseline correction and quality screening along with other pre-processing procedures. Among them, 26644 and 11437 records were used as the training and the test dataset, respectively. The test results show that the CNN-PP model outperforms the CNN model in discriminating shallow and intermediate-depth earthquakes. In addition, we test the CNN-PP model with the seismic events (M ≥ 3) that occurred in Japan in February 2022, and the results confirmed that this model has good performance in discriminating earthquakes of varying magnitudes. The CNN-PP model can effectively discriminate shallow and intermediate-depth earthquakes and has great application potential in EEW.