# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 4 Engineering Experiments
### Prediction of Mine Earthquake
#### 4.3.1 Prediction for Location of High-Energy Events  
It is of great significance to predict the location of high-energy mine earthquake for the prevention and control of underground dynamic disasters in coal mines. The specific method is extracting the high-energy events with release energy greater than \(10^{4}\) J from the cluster, using the related information of the previous 10 mine earthquakes to predict the location of the next event. The precursory pattern sequence features of the model include the X, Y, Z coordinate values and the distance between two events immediately adjacent to each other of the previous 10 events. The corresponding label is the location of the next event, that is, the X, Y, Z coordinate values. As shown in Figure 12 below, \(P=(p_{1},p_{2},p_{3},\ldots,p_{n})\) is the time series set of microseism location information, where \(p_{i}\) is the location information of one event. When the sampling step is 10 and the moving step is 1, the precursory pattern sequence set \(S=(s_{1},s_{2},s_{3},\ldots,s_{u})\) can be obtained, where \(s_{i}\) is a precursory pattern sequence, which contains the location information of the previous ten microseism events. The label set corresponding to \(S\) is \(L=(l_{1},l_{2},l_{3},\ldots,l_{u})\),where \(l_{i}\) is the label corresponding to \(s_{i}\) and contains the location coordinates of the next microseism event.  
The statistical result was \(n=97\). After \(P\) was processed as shown in the Figure 12, \(u=87\) was obtained, that is, the prediction model would have 87 samples and corresponding labels. The samples were divided into training set and test set according to the ratio of 7.3. The training set was used for the simulation training of the model, and the test set was used to test the effect of the training. Four kernel functions of SVR, Linear, Sigmoid, Polynomial and RBF, were selected for the experiment, and the grid search method was used to optimize the hyperparameters of model. Overfitting can be suppressed by two parameters. One is the penalty factor C, the higher its value is, the more intolerant the model is to errors and easy to overfit. The other is the parameter gamma in the kernel function. With the increase of gamma, the prediction effect of the training set becomes better, and the prediction effect of the test set becomes worse. At the same time, the complexity of the model increases, and the generalization ability becomes worse, which leads  
Figure 12: The workflow of microseism location data processing.  
to the overfitting. The overfitting issue is controlled by adjusting the above two parameters combined with the final prediction effect of the model.  
The predicted values outputted from model and the true values were compared together to get Figure 13 below. In view of the equal position of X, Y and Z coordinates in location prediction, this case only presents the prediction of X coordinates due to space limitation. The MSE and R\({}^{2}\) of the four prediction models with different kernel functions in the figure were calculated respectively, and the statistical results are shown in Table 5.  
Combined with Figure 13 and Table 5, it can be seen that the SVR model with RBF kernel function has the best effect on the location prediction of high-energy mine earthquakes.