CSESnet: A deep learning P-wave detection model based on UNet plus plus designed for China Seismic Experimental Site

Accurate detection of P-wave arrivals has important applications in real-time seismic data processing, such as earthquake monitoring and earthquake early warning. The Sichuan and Yunnan regions, where the China Seismic Experimental Site (CSES) is located, has frequent strong earthquakes and large amount small earthquakes, resulting in serious earthquake disasters. In this paper, we modify the UNet++ network structure and use 490,000 event waveform data and 78,000 noisy data from the CSES as the data set, and analyze the effects of the training set quality, labeled data and loss function on the model performance to obtain a new P-wave detection model-CSESnet. The recall, precision and F1 score of this model are 94.6%, 85.4% and 89.7%, respectively. The tests in Beijing Capital Circle (BCC) indicates the performance of the CSESnet decrease little and has good generalization. The test in Luxian M6.0 earthquake shows that CSESnet can also predict the P-wave arrival times of large earthquakes and process strong motion data very well. CSESnet provides a new detection model to improve the earthquake detection capability in CSES.