基于ST时频分析的地下厂房微震信号识别研究

doi:10.13722/j.cnki.jrme.2015.0702

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Abstract The classification of microseismic events is one of the key technologies in microseismic monitoring. The frequency characteristics of rock fracture，blasting vibration signal in underground powerhouse are studied through S transform(ST)，the energy distribution ratio in different frequency bands were used to quantify the frequency characteristics and a model for classification of microseismic events based on the BP neural network optimized by genetic algorithm(GA) was established. Based on the monitoring data in the left bank underground powerhouse of Baihetan hydropower station，the microseismic signals were decomposed in time-frequency domain to calculate the energy distribution ratio in different frequency bands using MATLAB software. Then the feature of microseismic signal can be established，combined with the GA-BP method，the classification model was built by training 400 sets of sample data. Finally，100 sets of test data were used to verify the accuracy of GA-BP network model. The results show that the rock fracture and blasting vibration events show difference in energy distribution， the GA-BP network model presented excellent performance for identifying the two types of signals，the correct rate of identification is 98%，the research method can provide reference for the establishment of events classification model in microseismic monitoring of similar projects.

Key words： underground engineering underground powerhouse microseismic monitoring waveform identification S transform(ST) frequency characteristics BP neural network genetic algorithm(GA)

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	Articles by authors
	JIANG Peng1
	DAI Feng1
	XU Nuwen1
	FAN Yilin2
	LI Biao1
	GUO Liang1
	XU Jian2

Cite this article:

JIANG Peng1,DAI Feng1,XU Nuwen1, et al. IDENTIFICATION OF MICROSEISMIC SIGNAL IN UNDERGROUND POWERHOUSE BASED ON ST TIME-FREQUENCY ANALYSIS[J]. , 2015, 34(S2): 4071-4079.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2015.0702 OR https://rockmech.whrsm.ac.cn/EN/Y2015/V34/IS2/4071

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