## 2 Data Establishment  
### Data Collection  
Due to the complicated mechanisms of rockburst, there are numerous influencing factors. It is generally deemed that the in situ stress, lithology, and energy storage properties of the surrounding rock are key aspects related to the occurrence of rockburst [46]. Drawing upon the influencing factors used in the existing research [47], six indicators were selected in this study: the maximum tangential stress (MTS), uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), elastic deformation energy index (\(W_{vt}\)), tangential stress index (MTS/UCS), and brittleness coefficient of strength (UCS/UTS).  
According to the selected indicators, a dataset of 266 rockburst samples with no duplicates or missing values was constructed by collecting relevant samples. In the dataset, sixty-five samples were from [PERSON] et al. [48], thirty were from [PERSON] et al. [49], twenty-nine were from [PERSON] [50], twenty-five were from [PERSON] et al. [51; 52], fifteen groups were derived from [PERSON] et al. [53], thirteen groups were derived from [PERSON] et al. [54], seven groups were derived from [PERSON] et al. [55], and others were from the Chinese literature [56; 57; 58; 59; 60]. All groups were obtained using a complete rockburst prediction index system. As for the classification standard, all rockburst samples used the same four-level standard except for [PERSON]'s data, which is classified with five levels [49]. By analyzing the classification levels, it is found that the combination of levels IV and V by [PERSON] aligns with level IV in the other literature. Therefore, to standardize the prediction results of rockburst hazards, the thirty rockburst samples from [PERSON] were redivided into four levels. Finally, the total 266 rockburst samples were divided using the four-level classification, namely none (I), light rockburst (II), moderate rockburst (III), and strong rockburst (IV). The distribution of the rockburst dataset is shown in Figure 1.  
Figure 1: The distribution of the original dataset.  
The distribution of rockburst levels in the original dataset is unbalanced. There are 35 samples in level I, 62 in level II, 122 in level III, and 47 in level IV. Since the majority of rockburst samples in the unbalanced database can dominate the prediction results, minority class samples may be misclassified as majority class samples by machine learning models.  
To better visualize the distribution patterns and ranges of features, the rockburst indicator values were analyzed by boxplot coupled with a normal curve, as shown in Figure 2. There is some key information in the figure. I, II, III, and IV represent the rockburst levels of none, light rockburst, moderate rockburst, and strong rockburst, respectively. Each bullet symbolizes a unique data point, with its precise location and color meticulously chosen based on the point's value and the corresponding rockburst label. Furthermore, the normal curve illustrates the probability density distribution of the data, where a more prominent peak signifies a higher concentration of data points.  
Figure 2: Boxplots of rockburst indicator values.  
In this figure, the long length of the boxes reveals a relatively high dispersion in the data. In addition, the box lengths and positions on the left and right sides of the box line plot are relatively balanced. Some outliers exceed the upper and lower limits, so the data may be somewhat skewed, which may have implications for statistical analysis, model selection, and interpretation of the results. However, considering that anomalous data can also exist in reality, the original dataset with outliers was used in this study to better reflect the field status.