微震信号FIR–小波联合滤波算法及应用

FIR-wavelet joint filtering algorithm for microseismic signals and its application

固定频率干扰和背景噪声是严重影响深部岩石工程岩爆灾害微震监测与预警的难题。为解决这一难题,充分利用经典FIR(finite impulse response)带阻滤波器擅于处理固定频率噪声信号的优势,对干扰进行滤除;针对小波阈值去噪方法存在的不足,提出无偏估计小波阈值去噪方法,解决了小波阈值去噪方法滤除噪声时阈值难以准确确定的难题;对FIR带阻滤波器及提出的无偏估计小波阈值去噪方法进行有机融合,提出微震信号FIR–小波联合滤波算法,并研发了FIR–小波联合滤波模块。利用加噪后的合成正弦信号和微震信号对提出的算法进行验证,结果表明不同信噪比条件下,提出的算法具有较好的滤波效果,且具有良好的鲁棒性。巴基斯坦NJ水电站引水隧洞TBM施工过程中,强环境噪声干扰条件下应用结果表明:提出的算法提高了P波拾取的精度和岩石破裂信号的定位精度,为岩爆的预警与防治提供有力支撑,具有重要的理论与实际意义。

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.