Automatic Recognition of Earthquake and Blasting Events Based on Deep Learning
Abstract Aiming at the classification of natural earthquake events and blasting events, we use 80 natural earthquake events and 20 blasting events in Gansu and its surrounding areas to establish datasets, and apply deep learning convolutional neural network(CNN) method to build two models with different structures for training, and use 500 waveforms of natural earthquakes events and blasting events out of the training sets as test datasets. The accuracy of training and testing is more than 90%. The results show that two training models designed in this paper have a certain generalization ability; especially the Inception V1 model has good effect in the classification and recognition of natural earthquake events and blasting events.
Key words :
convolutional neural network
deep learning
seismic phase
blasting
classification and recognition
Cite this article:
GAO Yongguo,YIN Xinxin,LI Shaohua. Automatic Recognition of Earthquake and Blasting Events Based on Deep Learning[J]. jgg, 2022, 42(4): 426-430.
GAO Yongguo,YIN Xinxin,LI Shaohua. Automatic Recognition of Earthquake and Blasting Events Based on Deep Learning[J]. jgg, 2022, 42(4): 426-430.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2022/V42/I4/426
[1]
ZHOU Yi,WANG Xiang,SUN Lina. Source Parameters and Seismogenic Structure Analysis of the July 12, 2020 Tangshan MS 5.1 Earthquake [J]. jgg, 2022, 42(2): 172-175.
[2]
ZHOU Yang. Seismic Data Prediction Based on Regression Model of Nuclear Mixed Effects [J]. jgg, 2021, 41(9): 967-972.
[3]
CHEN Huifang,LIN Binhua,ZHANG Yanming,CAI Xinghui,YU Weiheng. Precise Determination of the Focal Depth of Taiwan Strait M6.2 Earthquake by sPn Phase [J]. jgg, 2021, 41(8): 853-857.
[4]
ZHU Bingqing,WANG Jianguo,GUO Wei,ZHAO Liming,WANG Weitao. Data Fusion Analysis of Vertical Pendulum Broadband Tiltmeter and Broadband Seismometer in Tianjin [J]. jgg, 2021, 41(7): 759-764.
[5]
PENG Zhao,SHAO Yongqian,LI He,LIU Lintao. Research on Seismic Detection Based on Machine Learning with Small Sample [J]. jgg, 2021, 41(7): 765-770.
[6]
. [J]. jgg, 2020, 40(S2): 107-112.
[7]
. [J]. jgg, 2020, 40(S1): 57-63.
[8]
. [J]. jgg, 2020, 40(S1): 113-117.
[9]
CAI Xinghui, ZHANG Yanming, CHEN Huifang, WU Lihua. Automatic Identification of Earthquake and Explosion Based on Wavelet Transform and Neural Network [J]. jgg, 2020, 40(6): 634-639.
[10]
YIN Xinxin,LI Shaohua,CHEN Wenkai,CHEN Jifeng,WANG Xin. Research on Explosion Phase Recognition Based on Waveform Cross-Correlation [J]. jgg, 2020, 40(4): 362-365.
[11]
ZHU Bingqing, CAO Jingquan, DONG Yibing, TAN Yipei, ZHAO Liming, ZANG Chong, XU Henglei, NI Sidao. Determination of Focal Depth of Yongqing M4.3 Earthquake Using Near-Distance and Far-Distance Seismic Converted Wave
[J]. jgg, 2020, 40(3): 291-298.
[12]
ZHAO Rui,LI Junchao. Analysis of Seismic Phase Based on Theoretical Travel Time Table [J]. jgg, 2020, 40(12): 1237-1241.
[13]
FAN Xiaoyi,QU Junhao,LIU Fangbin,ZHOU Shaohui. Analysis of Influencing Factors in Use of Support Vector Machine Method to Identify Earthquake Types [J]. jgg, 2020, 40(10): 1034-1038.
[14]
FAN Xiaoyi,QU Junhao,QU Bao’an,LIU Fangbin,SHAN Changlun,ZHOU Shaohui. Support Vector Machine LIBSVM Method for Identifying Natural Earthquakes, Blasting and Collapse [J]. jgg, 2019, 39(9): 916-918.
[15]
ZHANG Yi,WANG Linfeng,FENG Qian. Stability and Reliability Analysis of Bedding Rock Slope under Blasting [J]. jgg, 2019, 39(3): 241-245.