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Microseismic event detection based on multiscale detection convolutional neural network

YAN ZHANG1,2,3 ,  XIAO-QIU LIU1,2,3 ,  LI-WEI SONG1,2,3 ,  HONG-LI DONG4,5
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1 School of Physics and Electronic Engineering, Northeast Petroleum University, Da Qing, Heilongjiang 163318, P .R. China.,
5 Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Da Qing, Heilongjiang 163318, P .R. China.,
3 Key Laboratory of Networking and Intelligent Control of Heilongjiang Province, Da Qing, Heilongjiang 163000, P .R. China.,
4 School of Physics and Electronic Engineering, Northeast Petroleum University, Da Qing, Heilongjiang 163318; P .R. China.,
JSE 2023, 32(5), 455–481;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Zhang, Y ., Liu, X.Q., Song, L.W. and Dong, H.L., 2023. Microseismic event detection based on multiscale detection convolutional neural network. Journal of Seismic Exploration, 32: 455-477. The traditional microseismic event detection method is mainly based on the characteristic calculation of microseismic signals. Its accuracy is greatly affected by the empirical parameter setting of the algorithm, characteristics selection of signal, and signal-to-noise ratio of microseismic signals. Furthermore, it also takes a long computation time when dealing with massive microseismic data. Therefore, this paper presents a method of microseismic event detection based on the multiscale neural network. Firstly, according to the characteristics of microseismic signals, one- dimensional convolutional neural network is built to extract the fine-grained features of the shallow layers and the semantic features of the deep layers. Then, the credibility factor model is established for the detection results of the different scale feature expressions, and the final recognition results are obtained by uncertainty reasoning. Compared with wavelet analysis, BP neural network, and traditional convolution neural network, the experimental results show that the proposed model is superior to other methods, and has better anti-noise and generalization ability. In addition, this method also provides a new strategy for processing other monitoring signals with large interference.

Keywords
microseismic event detection
neural network
certainty factor
multiscale
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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