## 2 Rockburst Monitoring Data Processing
### Rockburst Data Processing
#### 2.2.2 Rockburst Characteristic Variables Screening  
Rockburst is triggered by a combination of factors. The Pearson correlation coefficient Equation (2) calculates and determines the interrelationship between the characteristic variables (X1-X14) of rockburst after normalization. The characteristic variables are judged to be independent of each other only when correlation coefficients are zero.  
\[\rho(x_{1},x_{2})=\frac{\text{cov}(x_{1},x_{2})}{\sqrt{DX_{1}DX_{2}}} \tag{2}\]  
As shown in Figure 1, there are strong and weak influence relationships between characteristic variables, and the shades of color represent the strength of the interrelationship.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline  & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **X9** & **X10** & **X11** & **X12** & **X13** & **X14** & **Risk Level** \\ \hline
1 & 33 & 0 & 16,800 & 2990 & 1.039477928 & 1.531868332 & 7.6 & 6.7 & 0 & 68 & 138 & 0 & 2.4 & 10,967 & 0 \\
2 & 46 & 13 & 21,600 & 2990 & 1.039477928 & 1.207041073 & 8 & 10 & 4.3 & 68 & 138 & 0 & 3.2 & 17,895 & 0 \\
3 & 55 & 9 & 22,600 & 4360 & 1.003479728 & 1.239564608 & 8.2 & 10.4 & 0.5 & 68 & 138 & 0 & 4 & 16,997 & 0 \\  & & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & & \\
453 & 52 & \(-\) & 21,400 & 2890 & 1.477798495 & 2.127025147 & 7.6 & 7.7 & 0.5 & 80 & 103 & 0 & 2.4 & 10,061 & 1 \\
454 & 40 & \(-\) & 12 & 13,700 & 3010 & 0.8948153 & 7.43149913 & 7.6 & 7.7 & 0.1 & 80 & 103 & 0 & 0 & 1848 & 1 \\
455 & 29 & \(-\) & 11 & 47,900 & 829 & 0.31051595 & 6.490514905 & 7.4 & 7.5 & 0.1 & 80 & 103 & 0 & 0 & 738 & 1 \\
456 & 19 & \(-\) & 10 & 9900 & 2600 & 0.675306958 & 2.265446224 & 7.4 & 7.5 & 0.4 & 80 & 103 & 0 & 1.6 & 4370 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Rockburst monitoring raw data.  
similar to that of GBDT. That is, the negative gradient of the loss function is determined as the residual approximation of the current weak classifier decision tree to fit a new decision tree. In other words, a new loss function is added to the original iterative model to keep the predicted value close to the current actual value while ensuring that the state of the original model remains unchanged at each iteration, as shown in Equation (3).  
\[\hat{y}_{1}=\sum_{q=1}^{Q}f_{q}(x_{i}),f_{q}\in\chi \tag{3}\]  
\(\chi\) is the function space of the iterative tree, and \(f_{q}(x_{i})\) denotes the predicted value of the \(i\)th sample in the \(q\)th tree. For further feature selection, the LightGBM model is used to calculate the importance of each feature relative to the rockburst hazard level, and the heuristic information of the iterative tree is used as an important measure of the feature. The quality of the subset of candidate features was directly affected by the metric of the tree structure. Based on the total number of times each feature is segmented in the iterative tree (_T_Split_), the LightGBM model measures the feature importance by calculating the sum of the gains (_T_Gain_) that result from the features being used for segmentation in all decision trees, as shown in Equations (4) and (5).  
\[T\_Split=\sum_{t=1}^{k}Split_{t} \tag{4}\]  
\[T\_Gain=\sum_{t=1}^{k}Gain_{t} \tag{5}\]  
X1 to X14 are used as 14 characteristic variables, and the rockburst risk level is used as the label. The LightGBM model is established by dividing the dataset and is then trained. The importance of each feature for the rockburst risk level is calculated, as shown in Figure 2. Based on the actual situation of coal mine production, the less critical characteristic variables, such as diurnal frequency variation (X2), energy anomaly coefficient k (X5), AVG daily footage (X12), and aperiodic weighting (X14), are discarded to establish the new rockburst characteristic variables after screening.  
Figure 2: The importance of each characteristic variable of rockburst.