Microseismic event detection based on multiscale detection convolutional neural network

JSE

Journal of Seismic Exploration

0963-0651

AccScience Publishing

Article

Microseismic event detection based on multiscale detection convolutional neural networkhttps://geophysical-press.com/journal/JSE/articles/22ZHANGYAN,LIUXIAO-QIU,SONGLI-WEI,DONGHONG-LI

2023

325

2023-10-01

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.microseismic event detection, neural network, certainty factor, multiscale

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