Research on mine microseismic signal recognition based on empirical wavelet transform

In view of the problem that it is difficult to identify microseismic signal and blasting vibration signal automatically, a method based on empirical wavelet transform (EWT) for mine microseismic signal identification is proposed. Firstly, using the simulated signal to compare EWT and empirical mode decomposition (EMD), the results showed that the EWT decomposition effect was better than the EMD, and the modal aliasing problem can be reduced; after that, the EWT decomposition of the measured 400 sets of blasting vibration and microseismic signals was carried out, and the intrinsic modal components of the compact supported spectrum were obtained, the seven principal components from f1 to f7 were obtained by mutual information filtering, and then the 7 components were respectively utilized to construct Hankel matrix, and the singular value mean, root mean square value, and standard deviation of each Hankel matrix were calculated and used as feature quantities; finally, microseismic and blasting vibration signals were classified by support vector machine (SVM). The results show that the singular value square root and standard deviation of the blasting vibration signal components from f1 to f7 are larger than the microseismic signal, and the singular value of the component from fl to f5 is larger than the microseismic signal; in terms of the recognition effect, EWT_Hankel_SVD feature extraction method is better than the widely used EWT_SVD, and the classification accuracy rate based on EWT_Hankel_SVD reaches 92.5%.