微震信号FIR–小波联合滤波算法及应用

doi:10.13722/j.cnki.jrme.2019.1130

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Abstract The specific frequency interference and background noise are the problems that seriously affect the microseismic monitoring and early warning of rockburst disasters in deep rock engineering. In order to solve these problems，firstly，the advantages of the classical FIR bandstop filter which is good at processing the fixed-frequency noise signal are made use of to denoise the interference. Then，in view of the problem of wavelet threshold denoising method，an unbiased estimation wavelet denoising method is proposed，which solves the problem that the threshold is difficult to be accurately determined when using the wavelet threshold denoising method to filter noise. Next，through organically combining the FIR bandstop filter and unbiased estimate wavelet threshold denoising methods，a FIR-wavelet joint filtering algorithm for microseismic signals is put forward and the FIR-wavelet joint filtering module is developed. The proposed algorithm is verified by the synthesized sinusoidal signal and microseismic signal. The results show that the proposed algorithm has better filtering effect and good robustness under different SNR conditions. The application to the TBM construction of the diversion tunnel of NJ hydropower station in Pakistan under strong environmental noise interference shows that the proposed algorithm improves the accuracy of P-wave picking and the location accuracy of rock fracture signals，provides powerful support for the early warning and prevention of rockburst，and hence has important theoretical and practical significance.

Key words： rock mechanics microseismic signal filtering FIR filter wavelet threshold denoising threshold estimation unbiased estimate

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	Articles by authors
	ZHU Xinhao1
	2
	CHEN Bingrui1
	2
	LI Tao1
	2
	WEI Fanbo1
	2
	WANG Xu1
	2

Cite this article:

ZHU Xinhao1,2,CHEN Bingrui1, et al. FIR-wavelet joint filtering algorithm for microseismic signals and its application#br#[J]. , 2020, 39(9): 1872-1882.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.1130 OR https://rockmech.whrsm.ac.cn/EN/Y2020/V39/I9/1872

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