# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 3 Model Design  
With regard to the prediction and early warning of high-energy mine earthquake, it is difficult for the traditional experience-driven and mechanism-driven strategies to accurately answer the three key questions of when, where, and energy level [29]. This gives machine learning a chance. In fact, the data-driven method has a better solution to those questions. At present, most research follows a fixed model, that is, select a machine learning model and find the best results through parameter optimization. However, if the microseism data are directly input into the training model, the final prediction accuracy is often not high, because the microseisms are not isolated events, but have obvious spatial-temporal aggregation. Therefore, if the spatial-temporal clustering analysis of microseism events is carried out first, the closely related microseism events will be divided into clusters, so the correlation degree between microseism data will be improved. On this basis, the machine learning method is used to fit this rule and establish a prediction model, which can significantly improve the accuracy of mine earthquake prediction. Accordingly, we propose a time sequence prediction method for mine earthquake combined with clustering analysis. The specific process is shown in Figure 5, which mainly includes three parts. 1. The clustering of microseism events. Similar to earthquake, coal mine microseisms are in essence a kind of dynamic phenomenon caused by stress release. Generally, they do not appear as isolated events, but many microseism events with different energy levels will occur successively in a certain region and time period, to form swarm sequences. The microseism events included in a swarm sequence are caused by a stress release, so the occurrence time, location, and energy levels of these microseism events have a certain regularity. In the ST-DBSCAN algorithm, the density is obtained by calculating the number of adjacent points in the designated spatial-temporal neighborhood  
Figure 5: The workflow of mine earthquake prediction in time sequence. around the sample point. The points whose density are higher than the designated threshold will be constructed as clusters, which are very suitable for processing the spatial-temporal data recorded in the microseism monitoring system. Clustering analysis can identify the active areas of microseism events in the working face and divide the microseism events into closely related clusters. The time, location, and energy information of microseism events contained in each cluster can be regarded as time series data [30] and have a certain regularity. 2. The establishment of a prediction model. After the completion of cluster analysis, microseism events on the working face will be divided into clusters. Although the correlation degree between microseism events in the cluster increases, the number of microseism events in a cluster is not large. This is equivalent to dividing a large sample data set containing microseism events of the whole working face into many small sample data sets. Therefore, the SVR algorithm suitable for small samples is selected to predict the time series of microseism events within the cluster. Mine earthquake prediction includes two aspects: one is the location of high-energy events; the second is the daily frequency of high-energy events with different energy levels, that is, the information of microseism events that have occurred in a specific period of time is used to predict the possible occurrence location (X, Y and Z coordinates) and the daily frequency of subsequent high-energy events. 3. The comparative analysis of the model. In order to verify the effectiveness of the proposed method which uses SVR to predict mine earthquake on the basis of clustering analysis, the LSTM, a very classical method in time sequence prediction, is selected for comparison experiment. Similarly, the occurrence location (X, Y and Z coordinates) and the daily frequency of high-energy events are predicted.