A matching pursuit variational mode decomposition method for microseismic signal denoising

Abstract To enhance the safety of deep-buried underground tunnel construction, it is crucial to monitor microseismic signals generated by rock micro-fractures. However, these signals are often contaminated by significant noise interference, which significantly hinders accurate signal interpretation. To address this challenge, this paper proposes an improved denoising method, Matching Pursuit Variational Mode Decomposition (MP-VMD) is aimed at effectively suppressing noise in microseismic signals. This paper proposes a MP-VMD algorithm that effectively eliminates noise in microseismic signals. Firstly, the MP algorithm is employed to sparse decompose the micro-seismic signal containing noise. Secondly, the Variational Mode Decomposition (VMD) algorithm is utilized to adaptively decompose the preliminary de-noised signal to obtain a series of modal components. Finally, the MP-VMD algorithm is combined with the correlation coefficient method to select the effective modal components to reconstruct the final denoised microseismic signal. The MP-VMD algorithm is applied to the denoising in laboratory simulation and to the denoising micro seismic signals from an underground engineering tunnel, and compare with wavelet decomposition, VMD, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Local Mean Decomposition (LMD) algorithm. The experimental results show that the MP-VMD algorithm can effectively remove the noise from microseismic signals. MP-VMD achieves a 15.2% higher signal noise ratio compared to wavelet decomposition while exhibiting a 23.7% reduction in root mean square error relative to conventional VMD.