Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis

Abstract

Under the background of the intelligent construction of a coal mine, how to efficiently extract effective information from the massive monitoring data of mine earthquakes, and improve prediction accuracy, is a research hotspot in the field of coal mine safety production. In view of this problem, more and more machine learning methods are being applied to the prediction on mine earthquakes. Considering that clustering analysis can enhance the correlation between microseism data, we propose a method whose main idea is to cluster microseism data before establishing the prediction model, and then train the model, so as to improve prediction accuracy. Specifically, microseism events on a working face are divided into clusters in advance by the Spatial Temporal-DBSCAN(ST-DBSCAN) algorithm, then a prediction model is established with Support Vector Regression (SVR) to predict the occurrence location and daily frequency of high-energy mine earthquake events. A set of engineering experiments were conducted in H Coal Mine, and the results show that the spatial-temporal clustering analysis of microseism events can indeed improve the prediction accuracy of machine learning methods on mine earthquakes.