A sparse denoising method based on adaptive Laplace wavelet dictionary for mine microseismic signal
Aiming at the problem of mine microseismic signal noise reduction,a sparse noise reduction method based on an adaptive La-place wavelet dictionary was proposed.Beginning with an a priori knowledge analysis of the characteristic waveform of the mine micro-seismic signal,the Laplace wavelet parameters that best match the characteristic waveform of the microseismic signal were first selected by the correlation filtering method,and a Laplace wavelet parameter dictionary was constructed.The characteristic waveform of the mi-croseismic signal was then sparsely reconstructed by combining the orthogonal matching pursuit(OMP)algorithm.Considering that pa-rameter selection by the correlation filtering method,the Whale Optimization Algorithm(WO A)was used to achieve automatic and opti-mal parameter selection.The analysis results of the simulated signals and the actual measured signals show that the proposed method can effectively reconstruct the characteristic waveform and realize the noise reduction of the microseismic signal,and has a certain anti-inter-ference ability to the noise.Moreover,the proposed sparse denoising method based on adaptive Laplace wavelet dictionary has certain superiority over the commonly used ensemble empirical mode decomposition(EEMD)method.
noise reduction of microseismic signalLaplace wavelet dictionarysparse decompositionfeature reconstruction
万方数据
维普
