# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 5 Conclusions  
It is easy for mine earthquake activity to cause coal mine dynamic disasters, resulting in serious consequences. The microseism monitoring system can monitor the microseism events in real-time and record the time, space, and intensity information when the mine earthquake occurs. In this paper, the microseism data of 103# working face of the H Coal Mine were selected. Firstly, the ST-DBSCAN algorithm was used to find out the microseism clusters, and then SVR was used to predict the occurrence location and daily frequency of high-energy events on this basis. MSE and R\({}^{2}\) were selected as the assessment indexes of the model prediction effect. The results (Tables 5 and 6) showed that among the four different kernel functions, the SVR prediction model with RBF kernel function has the best performance. Finally, as a comparison experiment, the LSTM model was used to predict high-energy mine earthquakes in the same period using the 103# working face microseism data without clustering process. Through comparative analysis, its performance (Table 7) was inferior to the SVR prediction model with RBF kernel function. This engineering experiment showed that the spatial-temporal clustering analysis of microseism events in advance can improve the prediction accuracy of the machine learning method on high-energy mine earthquakes.  
However, results of out-of-sample prediction experiment (Table 8) showed that the trained SVR model does not perform well on the new cluster. The reason could be that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Assessment** \\ **Indexes** \\ \end{tabular} } & \multicolumn{2}{c}{**X Coordinate**} & \multicolumn{2}{c}{**Daily Frequency of \(10^{4}\) J Level Events**} \\ \cline{2-5}  & **LSTM** & **SVR\_RBF** & **LSTM** & **SVR\_RBF** \\ \hline MSE & 0.0314 & 0.0076 & 0.5387 & 0.2251 \\ R\({}^{2}\) & 0.4602 & 0.8636 & 0.8098 & 0.8342 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of model performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Assessment** \\ **Indexes** \\ \end{tabular} } & \multicolumn{2}{c}{**X Coordinate**} & \multicolumn{2}{c}{**Daily Frequency of \(10^{4}\) J Level Events**} \\ \cline{2-5}  & **LSTM** & **SVR\_RBF** & **LSTM** & **SVR\_RBF** \\ \hline MSE & 0.0298 & 0.0471 & 0.6127 & 1.2062 \\ R\({}^{2}\) & 0.4954 & 0.4383 & 0.7351 & 0.5281 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model performance for out-of-sample prediction.  
time series laws of data in different clusters are not exactly the same, due to the influence of the changing geological conditions and other factors. In future work, we will conduct research on the model's generalization ability on different microseism events clusters and mines.  
Conceptualization, X.L. and P.Z.; methodology, P.Z. and J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (Grant no.2020 YFB1314200) and China Postdoctoral Science Foundation (Grant no.2018M642367).  
Not applicable.  
The authors would like to thank the anonymous referees for their valuable comments and suggestions.  
The authors declare no conflict of interest.