Feature extraction and classification method of mine microseismic signals based on EWT_Hankel_SVD

To solve the difficult problem of automatic identification rock mass microseism and blasting vibration signals, a feature extraction and classification method based on empirical wavelet transform_Hankel matrix_singular value decomposition (EWT_Hnakel_SVD) is proposed. Firstly, EWT spectrum segmentation method is improved to adapt the transient and diversity of microseism signals. Its effectiveness is demonstrated by using simulation signals. Then, the improved EWT is used to decompose the microseismic and blasting vibration signals. Five principal components of f1~f5 are obtained by correlation analysis, which are utilized to formulate the Hankel matrix. The maximum singular value and singular entropy of each Hankel matrix are calculated. Finally, the genetic algorithm-optimized support vector machine (GA-SVM) is adopted to classify the microseism and blasting signals. Experimental results show that the singular entropy of the blasting vibration signal component fl~f4 is too much singular entropy of the rock mass microseismic signal component fl~f4, and the maximum singular value of the blasting vibration signal component fl~f5 is greater than that of the rock mass microseismic signal component fl~f5. The improved EWT recognition is better than traditional EWT and empirical mode decomposition. GA-SVM recognition effect is better than support vector machine, logistic regression and Bayes discriminant method. The method based on EWT_Hankel_SVD and GA-SVM classification can reach accuracy rate 94%. © 2019, Science Press. All right reserved.