Five-category detection method for microseismic events based on residual network
PAN Yuxing1,2, TIAN Xiao1,2, GAN Zhaolong1, ZHANG Xiong1,3, ZHANG Wei2
1. Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. Guangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China; 3. Shanghai Sheshan National Geophysical Observatory, Shanghai 200062, China
Abstract：Conventional detection methods for microseismic events usually require manual selection of the threshold. They are inefficient when processing a large amount of continuously recorded data and fail to meet the needs of real-time monitoring. This study proposes a five-category detection method for microseismic events based on a residual network, which divides samples into five categories： noise, microseismic events, only P waves, only S waves, and multiple microseismic events. This method only needs to equally divide the continuously recorded waveform data and obtain a complete microseismic record by shifting time windows. Through a series of data augmentation methods, the model of a small set of actual data samples is trained, and the model accuracy is as high as 99%. This method and the binary classification method are used to detect microseismic monitoring data at the same time, and the detection effect is evaluated through P-wave and S-wave arrival time picking and source location. The research results show that the five-category detection method based on the residual network has greatly improved the detection quantity of microseismic events, and it has high computing efficiency, which can meet the needs of real-time monitoring.
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