Identification of Microseismic Signals Based on Multiscale Singular Spectrum Entropy

Xingli Zhang,Ruisheng Jia,Zhenhua Zhao,Lianyue Cao

doi:10.1155/2020/6717128

Xingli Zhang, Ruisheng Jia + Show 2 more

Open Access

https://doi.org/10.1155/2020/6717128

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Abstract

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 numberKvariational 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.

Highlights

In recent years, microseismic monitoring has become an advanced and effective means for monitoring rock and coal dynamical disasters
A LS-support-vector machine (SVM) classifier can accurately identify microseismic signals, so this paper introduces multiscale singular spectrum entropy to measure microseismic signal complexity, and the LS-SVM model was used to identify microseismic events
The identification model of the microseismic signal is established to accurately identify and record the microseismic signal under the influence of blasting vibration signals. e microseismic signal identification method is proposed with a combination of Multiscale singular spectrum entropy (MSSE) and SVM based on the LS-SVM theory, which can effectively distinguish the microseismic signal and blasting vibration signal and has high identification accuracy and stability

Summary

Introduction

Microseismic monitoring has become an advanced and effective means for monitoring rock and coal dynamical disasters. It can be carried out in real time for the continuous and online monitoring of the microseismic activities of rock fractures and thereby has become a useful source of microseismic monitoring data. Considering the fact that blasting operations often occur in coal mines, and the waveforms of rock fractures and blasting vibrations are very similar, the artificial identification method often makes processing errors, which is difficult to effectively identify. The identification methods used for the microseismic signal mainly include the first arrival times of the seismic phases, feature recognitions, and parameter discriminations. The automatic detection function of the method has been found to be unreliable, and the antinoise performances of the algorithm have been observed to be poor

Methods

Discussion

Conclusion