JRMGE / Vol 16 / Issue 2
Predicting microseismic, acoustic emission and electromagnetic radiation data using neural networks
Yangyang Di, Enyuan Wang, Zhonghui Li, Xiaofei Liu, Tao Huang, Jiajie Yao
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a School of Materials Engineering, Changshu Institute of Technology, Suzhou, 215506, China
b Key Laboratory of Gas and Fire Control for Coal Mines (China University of Mining and Technology), Ministry of Education, Xuzhou, 221116, China
c School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, China
d State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, 221116, China
2024, 16(2): 616-629. doi:10.1016/j.jrmge.2023.05.012
Received: 2023-01-10 / Revised: 2023-05-16 / Accepted: 2023-05-17 / Available online: 2023-07-24
2024, 16(2): 616-629.
doi:10.1016/j.jrmge.2023.05.012
Received: 2023-01-10
Revised: 2023-05-16
Accepted: 2023-05-17
Available online: 2023-07-24
Microseism, acoustic emission and electromagnetic radiation (M-A-E) data are usually used for predicting rockburst hazards. However, it is a great challenge to realize the prediction of M-A-E data. In this study, with the aid of a deep learning algorithm, a new method for the prediction of M-A-E data is proposed. In this method, an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data, and then the M-A-E data can be predicted. The predicted results are highly correlated with the real data collected in the field. Through field verification, the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.
Keywords: Microseism, Acoustic emission, Electromagnetic radiation, Neural networks, Deep learning, Rockburst
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Author(s) Information
Enyuan Wang
Enyuan Wang is currently the dean of the School of Safety Engineering at China University of Mining and Technology, and deputy director of the State Key Laboratory of Coal Resources and Safe Mining. He is the member of the Rockburst Special Committee of China Coal Industry Technical Committee, and the member of the Coal Mine Dynamic Disaster Prevention Committee of the China Coal Society. He has presided over the completion of seven national-level projects. His main research interests cover prevention of coal or rock dynamic disaster (coal and gas outburst, rockburst, etc.), safety monitoring and early-warning.