Feature Extraction and Classification Method of Mine Microseismic Signals Based on CEEMDAN_SE

Abstract

Since the mine microseismic and blasting signals are non-linear and non-stationary, the traditional linear analysis methods cannot effectively classify the microseismic and blasting signals. In this study, a method for feature extraction and classification of mine microseismic signals based on CEEMDAN_SE (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise_Sample Entropy) is proposed. Firstly, the microseismic of rock mass and blasting vibration signals are decomposed by CEEMDAN. Then five sensitive IMF components of IMF4~IMF8 (microseismic) and IMF1~IMF5 (blasting) are screened out by using correlation coefficient. Next, the sample entropy values of the sensitive IMF components are calculated. Finally, the sample entropy is used as the eigenvector and input into ELM (extreme learning machine) to classify and identify the microseismic and blasting signals. The results show that there is a significant difference between the sample entropy values of microseismic and blasting signals. Combining CEEMDAN_SE with ELM, the accuracy of classification, recognition of microseismic and blasting signals is 91.5%. Compared with EEMD_SE_ELM and EMD_SE_ELM, the classification, recognition accuracy of CEEMDAN_ SE_ELM is improved by 2.5% and 5%, respectively. Therefore, this method provides a new idea for the classification and recognition of mine microseismic signals.