Deep Convolutional Neural Network for Microseismic Signal Detection and Classification
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Pure and Applied Geophysics
Deep Convolutional Neural Network for Microseismic Signal Detection and Classification HANG ZHANG,1,2,3 CHUNCHI MA,1,2 VERONICA PAZZI,3 TIANBIN LI,1,2 and NICOLA CASAGLI3 Abstract—Reliable automatic microseismic waveform detection with high efficiency, precision, and adaptability is the basis of stability analysis of the surrounding rock mass. In this paper, a convolutional neural network (CNN)-based microseismic detection network (CNN-MDN) model was established and well trained to a high degree of accuracy using a dataset with 16,000 preprocessed waveforms. By comparison with other methods, 4000 waveforms were tested to evaluate the precision, recall, and F1-score. The results revealed that the CNN-MDN demonstrated the highest performance in microseismic detection. Moreover, the low sensitivity of the CNN-MDN to noise of different intensities was proved by testing on semi-synthetic data. The model also possesses good generalization ability and superior performance capability for microseismic detection under different geological structure backgrounds, and it can correctly detect the microseismic events with Mw C 0.5. These preliminary results show that the CNN-MDN can be directly applied to unprocessed microseismic data and has great potential in real-time microseismic monitoring applications. Keywords: Microseismic waveform, deep learning, CNN, detection and classification.
1. Introduction With the pressures of economic development and expanding social needs, the demand for various mineral resources, energy sources, and transportation infrastructure is increasing, which drives the development and utilization of subterranean spaces (deep land). As outlined in the Chinese national 13th Five-
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State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China. E-mail: [email protected]; [email protected]; [email protected]; [email protected] 2 College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, Sichuan, China. 3 Department of Earth Sciences, University of Florence, Florence, Italy. E-mail: [email protected]; [email protected]
Year Plan, strategic high-tech research and deployment are proposed in technologies for deep sea, deep land, deep space, deep blue (i.e., information technology), and other fields. With the emergence of large underground and tunneling projects, microseismic monitoring has rapidly developed as a new type of technology in disaster monitoring and early warning (Xu et al. 2011, 2018; Zhao et al. 2017; Ma et al. 2018a, b; Feng et al. 2014, 2019a, b, 2020). It can evaluate the damage and safety status of surrounding rock by monitoring the rupture or damage vibration inside the rock mass, and thus assess and predict the potentially dangerous areas, providing a basis for early warning and control of any potential disaster (Ma et al. 2016a, b, 2018a, b; Zhang et al. 2019). The lengthy construction period, complex construction condit
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