Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes

• 作者: DAI Shigui HU Jiupeng KUANG Wenhuan RUAN Xiang WANG Weitao YU Ziye
• 来源期刊: 中国地震研究（英文版）
• 年，卷（期）: 2020,（3）
• 所属期刊栏目: Special Section on AI Seismology
• 页码范围: 343-357
• 页数: 15页
• 语种: 英文
• DOI: 10.19743/j.cnki.0891-4176.202003007

摘要

Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network ( CNN) and recurrent neural network ( RNN) . The neural network training uses the waveforms of 60000 small earthquakes within a magnitude range of 0. 8-1. 2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90. 7％ in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92. 6％and an error rate lower than 2％. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.