1D Convolutional Seismic Event Classi?cation Method Based on Attention Mechanism and Light Inception Block
Yong-ming Huang*, Yi Xie, Fa-jun Miao, Yong-sheng Ma, Gao-chuan Liu, Guo-bao Zhang, Yun-tian Teng
1. School of Automation, Southeast University, Jiangsu 210096, P. R. China
2. Seismological Bureau of Jiangsu Province, Jiangsu 210096, P. R. China
3. Institute of Geophysics, China Earthquake Administration, Beijing 100081, P.R. China
Abstract：
Waveforms of artificially induced explosions and collapse events recorded by the seismic network share similarities with natural earthquakes. Failure to identify and screen them in a timely manner can introduce confusion into the earthquake catalog established using these recordings, thereby impacting future seismological research. Therefore, the identification and separation of natural earthquakes from continuous seismic signals contribute to the monitoring and early warning of destructive tectonic earthquakes. A 1D convolutional neural network (CNN) is proposed for seismic event classification using an efficient channel attention mechanism and an improved light inception block. A total of 9937 seismic sample records are obtained after waveform interception, filtering, and normalization. The proposed model can obtain better classification performance than other major existing methods, exhibiting 96.79% overall classification accuracy and 96.73%, 94.85%, and 96.35% classification accuracy for natural seismic events, collapse events, and blasting events, respectively. Meanwhile, the proposed model is lighter than the 2D convolutional and common inception networks. We also apply the proposed model to the seismic data recorded at the University of Utah seismograph stations and compare its performance with that of the CNN-waveform model.
作者简介: Huang Yong-Ming, Ph.D., Associate Professor, Assistant Dean of the School o f Auto mation and Head of the Department of Automation. He received his B.S. degree in Automation from Harbin Engineering University in 2005 and his M.S. and Ph.D. degrees from Southeast University in 2008 and 2012, respectively. He is currently working at Southeast University, where he is engaged in the research of seismic electromagnetic disturbance data acquisition and processing, earthquake prediction, earthquake early warning and other directions.
2025, Vol. 22 
