Research on the classification of complex noise-mixed microseismic events based on machine vision

Abstract

Event classification is important for accurately monitoring and warning against rockburst hazards using microseismic technology. Here, we propose an automatic classification method for microseismic events based on machine vision. The method uses Histogram of Oriented Gradient (HOG) integrated with Support Vector Machine (SVM) as the core model (HOG-SVM, HSVM) to classify microseismic events. First, the method uses as input spectrograms generated from microseismic event signals recorded in the field. Next, the HOG method is used to accurately extract the spectral feature information of the useful signals of microseismic events under the interference of noisy signal. Finally, the extracted feature data is used to train SVM, after the training is completed, the SVM is used to classify the microseismic events. The performance of the method for categorizing microseismic events was tested using multiple independent test sets built from data monitored in the field of a mine in Shandong Province. The results show that the method can effectively extract the spectral feature information of useful signals of microseismic events contaminated with noise, with good classification accuracy and robustness to noise. It classifies microseismic events with high accuracy and efficiency compared to well-performing classification methods based on seismic source parameters and typical depth models. The method can provide technical support for the effective classification of microseismic events in complex construction sites, especially in noisy deep underground construction environments.