Feature extraction and classification method of mine microseismic signals based on LMD and pattern recognition

To solve the problem that it is hard to classify rock mass fracturing signal and blasting vibration signal auto- matically,a feature extraction and classification method of mine microseismic signals based on local mean decomposi- tion (LMD) and pattern recognition was proposed. Firstly,the LMD was used to decompose a microseismic signal a- daptively and obtain PF (product function) components,then correlation coefficients and variance contribution ratios were applied to select PF main components. Finally,the correlation coefficients and energy spectrum coefficients of the selected main components were set as the input of pattern recognition. The results show that the main components of LMD,empirical mode decomposition (EMD) and discrete wavelet transform (DWT) are PF1 to PF6 ,IMF1 to IMF6 and D2 to D7 respectively,where IMFi( i = 1,2,…,6) means the intrinsic mode function of EMD and Dj( j = 2,3,…,7) corresponds to the detail component of DWT. In addition,the classification result based on LMD is better than that based on EMD and DWT. Furthermore,the energy spectrum coefficients based method obtains a better result than that of correlation coefficients based method,and the artificial neural network (ANN) and support vector machine (SVM) based methods are much better than that of logistic regression (LR) and Bayes based methods. Also,the classification result based on energy spectrum coefficients of LMD and SVM obtains the best accurate classification rate of 93. 0% .