Identification of Microseismic Signals Based on Multiscale Singular Spectrum Entropy

The accurate identification of effective microseismic events has great significance in the monitoring, early warning, and forecasting of rockburst hazards. However, the conventional identification methods have displayed difficulties in achieving satisfactory results. A microseismic signal identification method which combines variational mode decomposition (VMD) and multiscale singular spectrum entropy was proposed in this paper. The original signal was firstly broken down into a given number K variational mode components, which are ranked by frequency in descending order. Then, the characteristic pattern matrix was constructed according to the mode component signals, and the identification model of the microseismic signals based on the support vector machine was built by performing a multiscale singular spectrum entropy calculation of the collected vibration signals, constructing eigenvectors of signals. Finally, a comparative analysis of the microseismic events and blasting vibration signals in the experiment proved that the different characteristics of the two kinds of signals can be fully expressed by using multiscale singular spectrum entropy. Experimental results further confirmed the effective identification performance of this proposed method