矿山微震波形特征自动模式识别算法研究

doi:10.13722/j.cnki.jrme.2021.0528

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Abstract The application of the micro-seismic monitoring technology in mines is increasing，but the automatic identification of effective signals and noise signals has not been realized，which seriously restricts its application and popularization. Six main pattern categories in the mine strong noise environment，including drilling，trackless equipment running，mine shaft dumping，electromagnetic interference，blasting and effective signal，are investigated，and the effective signal patterns are divided into two subsets of small-energy events and large-energy events. The generation mechanism of each pattern class is studied in detail. By collecting a large number of samples of each pattern class，the identifying characteristics extracted for the above six pattern classes are respectively the waveform interval time，the waveform duration，the combination of the total duration with the number of individual events，the waveform duration or the main frequency，the combination of the waveform duration and the main frequency，and the waveform duration and the exclusion method. The distribution probabilities of the recognition characteristic values are calculated and counted by using the pre-processing method. A decision function with excellent recognition performance is constructed，and an automatic pattern recognition algorithm of mine micro-seismic waveform characteristic is established. Based on the above algorithms，a software of automatic pattern recognition is developed. Through the field test in a typical mine，the recognition accuracy of the effective signal of the developed algorithm is 90.8%，showing a good field application.

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mining engineering micro-seismic waveform identifying characteristics decision function pattern recognition algorithmic software

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	Articles by authors
	HU Jingyun1
	ZHANG Ru2
	REN Li2
	PENG Fuhua1
	WU Fei3
	CAO Weiliang4

Cite this article:

HU Jingyun1,ZHANG Ru2,REN Li2, et al. Research on automatic pattern recognition algorithm of micro-seismic waveform characteristics in mines[J]. , 2022, 41(2): 346-361.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.0528 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/I2/346

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