结合变分模态分解与小波阈值的微震去噪方法OA

Microseismic monitoring technology is of application significance in fields such as unconventional oil and gas reservoir development and mine disaster monitoring.However,its signals are susceptible to noise inter-ference,which results in a low signal-to-noise ratio(SNR),thus severely compromising the accuracy of subse-quent seismic source localization and mechanism inversion.Traditional denoising methods such as the complete ensemble empirical mode decomposition(CEEMD)and wavelet modulus maxima(WMM)have limitations in processing non-stationary microseismic signals.To this end,this paper proposes a microseismic denoising method named SSA-VMD-CC-WT,which combines variational mode decomposition(VMD)optimized by the sparrow search algorithm(SSA)with the adaptive wavelet thresholding(WT).Firstly,SSA is employed to opti-mize key parameters of the VMD algorithm.Secondly,effective modal components are selected by utilizing the cross-correlation coefficient(CC)to suppress noise.Finally,adaptive WT is applied to perform secondary de-noising on the effective components,reducing signal distortion.Simulation tests demonstrate that in strong noise conditions,the SSA-VMD-CC-WT method can separate noise from effective signals more accurately than the CEEMD and WMM methods.The processing of actual microseismic data reveals that the proposed method sig-nificantly suppresses both low-frequency and high-frequency noise while maintaining the fidelity of weak seis-mic sources,thereby improving data interpretability and SNR.Meanwhile,compared with the traditional genetic algorithm(GA),SSA demonstrates higher optimization efficiency.