## 3 Establishment of the Database  
### Data Sources  
The study extensively collected data from domestic and international rockburst-related engineering cases and used them to establish a rockburst database (Supplementary Materials). This database consists of 287 groups of data, including 8 groups of data from [PERSON] et al. [24], 15 groups of data from [PERSON] et al. [25], 7 groups of data from [PERSON] et al. [42], 12 groups of data from [PERSON] et al. [23], 108 groups of data from [PERSON] et al. [43] and 137 groups of data from [PERSON] et al. [4], which are shown in Table 1.  
In general, the selection of predictive indicators should meet the requirements of easy access, strong representativeness and strong characteristics, so this study selected the maximum tangential stress of the surrounding rock \(\sigma_{\theta}\) (MPa), uniaxial compressive strength of rock \(\sigma_{c}\) (MPa), tensile strength of rock \(\sigma_{t}\) (MPa), the stress ratio of rock \(\sigma_{\theta}/\sigma_{c}\), rock brittleness ratio of rock \(\sigma_{c}/\sigma_{t}\) and elasticity and strain energy of the rock index \(W_{et}\), six quantitative indexes as a characteristic parameter for analysing the situation of rockbursts. From the viewpoint of the geological structure of rockbursts, the maximum tangential stress \(\sigma_{\theta}\) (MPa) of the enclosing rock body is usually one of the main driving factors for the occurrence of rockbursts. In contrast, although the main maximum (minimum) stress,  
Figure 6: Structure of CNN-BiGRU-Attention model. equivalent stress and bias stress can also reflect the stress state, the maximum tangential stress of the surrounding rock is more intuitive and direct in characterising the local stress concentration and damage risk of the surrounding rock body. Many existing studies and engineering practices have shown that the maximum tangential stress \(\sigma_{\theta}\) (MPa) plays an important role in rockburst prediction and stability analysis of the surrounding rock [3]. Also, rock uniaxial compressive strength \(\sigma_{c}\) (MPa) and rock tensile strength \(\sigma_{t}\) (MPa) can better reflect the mechanical properties of the rock, in the actual situation of rockbursts have been documented and can be used as a prediction and evaluation of rockburst indicators; from the energy point of view, the rockburst is the energy of elastic stress to a certain degree of energy accumulation after the rapid release of the results. Therefore, the rock elastic strain energy index \(W_{et}\) was selected as a rockburst prediction index. In addition, the stress ratio \(\sigma_{\theta}/\sigma_{c}\) and rock brittleness ratio \(\sigma_{c}/\sigma_{t}\) are also proposed by many researchers to predict the selection of rockburst strength indicators [44; 45; 46]. In this paper, the rockburst rating criteria selected are a posteriori derived from the rockburst intensity level proposed by [PERSON] et al. [20] (Table 2), which categorises the intensity level into four distinct categories: None, Light, Moderate and Strong. Table 3 demonstrates the calculation of the statistical analysis metrics for each indicator (i.e., standard deviation, kurtosis, maximum, mean, median and range). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Number of Cases**} & \multicolumn{5}{c}{**Rockburst Label**} \\ \cline{3-6}  & & **None** & **Light** & **Moderate** & **Strong** \\ \hline [PERSON] et al. (2022) & 8 & 0 & 3 & 3 & 2 \\ [PERSON] et al. (2020) & 15 & 3 & 5 & 4 & 3 \\ [PERSON] et al. (2019) & 7 & 0 & 1 & 5 & 1 \\ [PERSON] et al. (2019) & 12 & 0 & 1 & 11 & 0 \\ [PERSON] et al. (2024) & 108 & 19 & 32 & 43 & 14 \\ [PERSON] et al. (2019) & 137 & 25 & 36 & 55 & 21 \\ Total & 287 & 47 & 78 & 121 & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of rockburst labelling in the database [42; 43; 23; 24; 42; 43].