微震与爆破事件统计识别方法及工程应用

doi:10.13722/j.cnki.jrme.2015.1007

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Abstract A database of blasts and microseismic events was established with the manual recognition methods based on the microseismic monitoring at Kaiyang phosphate mine. The source parameters including the seismic moment，the seismic energy，the P and S wave energy ratios，the event occurrence time，the static stress drop，the sensor triggers and the corner frequency were analyzed statistically. The probability density distributions of the first peak arrival time，the first peak amplitude，the maximum peak arrival time and the maximum peak amplitude were compared and analyzed. The frequency distributions of two kinds of event signals were statistically analyzed with the FFT transform. The logarithm of seismic moment，the seismic energy，the event occurrence time，the first peak arrival time，the maximum peak amplitude，the numbers of triggered sensors and the dominant frequency were finally selected as the characteristic parameters based on the probability density distribution of each parameter，the performance of recognition and the difficulty of acquisition. A mathematical model of automatic recognition was established on the application of Fisher discriminant analysis. Results show that the accuracy of training samples is 100% and that the accuracy of testing samples is 94%. The model was applied to the second time blasting crushing of bulky rock of the stope and the recognition results were consistent with the actual ones.

Key words： mining engineering source parameters waveform characteristics frequency distribution microseismic events

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	Articles by authors
	DONG Longjun1
	2
	SUN Daoyuan1
	LI Xibing1
	MA Ju1
	CHEN Guanghui1
	ZHANG Chuxuan1

Cite this article:

DONG Longjun1,2,SUN Daoyuan1, et al. A statistical method to identify blasts and microseismic events and its engineering application[J]. , 2016, 35(7): 1423-1433.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2015.1007 OR https://rockmech.whrsm.ac.cn/EN/Y2016/V35/I7/1423

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