# 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. mine earthquake; machine learning; time sequence; prediction model +
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relevant information of seismic waves, and determine the spatial-temporal location of the source, the strength and frequency of microseism activities, and other high-energy release activity information through calculation and inversion [6]. At present, microseism monitoring technology has made great progress in station network optimization, seismic wave pickup, inversion positioning, and waveform real-time monitoring, and has made a series of important research achievements [7]. The microseism monitoring system can identify the micro vibration wave and its dynamic change process in the whole coal mine, obtain the specific information of the time, space, and intensity of the microseism event, and directly reflect the real-time mechanical characteristics evolution of the coal mine. With the increasingly mature and extensive application of microseism monitoring technology, it has become an effective means for the early warning of coal mine disasters, such as rock burst. It works by using a large amount of microseism monitoring data to analyze the breaking and migration laws of coal seam mining surrounding rocks, and to summarize the spatial scale, combined structure, the mechanical parameters of key stratum, and the correlation between the breaking process and high-energy microseism events. [PERSON] et al. [8] used microseism monitoring technology to analyze the temporal and spatial evolution law of microseism events before and after the breaking of key stratum, laying a foundation for the prediction on rock burst using the combination of strata movement theory and microseism monitoring technology. [PERSON] et al. [9] analyzed the time sequence characteristics of microseism signals during the period of rock burst, and then obtained the spectrum characteristics and distribution change laws of microseism signals. [PERSON] et al. [10] analyzed the waveform signals of microseism events and obtained the power spectrum evolution characteristics before and after the occurrence of rock burst. [PERSON] et al. [11] revised the maximum effective amplitude based on the attenuation characteristics of microseism waves in deep buried tunnels, taking the relative effective amplitude and the maximum effective frequency as spectrum analysis parameters. [PERSON] et al. [12] analyzed the time-domain waveform and frequency spectrum characteristics of microseism signals when rock burst occurred in the coal mine by using the fast Fourier transform method. [PERSON] [13] classified the microseism signal categories of coal mine working faces and analyzed the distribution law of microseism events in time and space.