Microseismic signal denoising by combining variational mode decomposition with permutation entropy

Remarkable progress has been achieved on microseismic signal denoising in recent years, which is the basic component for rock-burst detection. However, its denoising effectiveness remains unsatisfactory. To extract the effective microseismic signal from polluted noisy signals, a novel microseismic signal denoising method that combines the variational mode decomposition (VMD) and permutation entropy (PE), which we denote as VMD—PE, is proposed in this paper. VMD is a recently introduced technique for adaptive signal decomposition, where K is an important decomposing parameter that determines the number of modes. VMD provides a predictable effect on the nature of detected modes. In this work, we present a method that addresses the problem of selecting an appropriate K value by constructing a simulation signal whose spectrum is similar to that of a mine microseismic signal and apply this value to the VMD—PE method. In addition, PE is developed to identify the relevant effective microseismic signal modes, which are reconstructed to realize signal filtering. The experimental results show that the VMD—PE method remarkably outperforms the empirical mode decomposition (EMD)—VMD filtering and detrended fluctuation analysis (DFA)—VMD denoising methods of the simulated and real microseismic signals. We expect that this novel method can inspire and help evaluate new ideas in this field.