## 3 Establishment of the Database
### Parameter Correlation Analysis  
In order to assess the correlation among the six characteristic parameters of rockbursts, a correlation analysis can be based on the Pearson correlation coefficient method [48] for correlation analysis of the above sample data of rockburst prediction; the formula is shown in Equation (25), and the results are shown in Figure 9. The magnitude of the absolute value of the correlation coefficient can be employed to demonstrate the strength of the correlation.  
Figure 8: Fiddle map of rockburst data distribution: (**a**) the maximum tangential stress of the surrounding rock; (**b**) elasticity and strain energy of the rock index; (**c**) rock britteness ratio of rock; (**d**) tensile strength of rock; (**e**) the maximum tangential stress; (**f**) the stress ratio of rock.  
The shape of the ellipse indicates the strength of the correlation; the more rounded the ellipse, the weaker the correlation, and the flatter the ellipse, the stronger the correlation.  
\[r=\frac{\sum\limits_{i=1}^{n}{\left({{x_{i}}-\overline{x}}\right)\left({{y_{i}}- \overline{y}}\right)}}{\sqrt{\sum\limits_{i=1}^{n}{\left({{x_{i}}-\overline{x }}\right)^{2}}}\sum\limits_{i=1}^{n}{\left({{y_{i}}-\overline{y}}\right)^{2}}} \tag{25}\]  
where: \(\overline{x}\) and \(\overline{y}\) are the mean values of \(n\) test values, respectively; \(r\in[-1,1]\).  
The diagonal position in Figure 9 is the histogram of the normal distribution of the original dataset; in the normal distribution, the mean is the centre point of the data, and the standard deviation determines the width of the curve [49] and can be a measure of the degree of dispersion between the data points and the mean. A wider histogram indicates that the standard deviation is larger, and otherwise, it is the opposite. As can be seen from the figure, most of the data are more concentrated, and the number of data along both sides of the mean gradually decreases, while the normal histogram of \(W_{et}\) is more abnormal, which indicates that there are certain outliers in the sample and also reflects the existence of a certain degree of uncertainty in \(W_{et}\).  
A correlation coefficient with an absolute value more than 0.5 implies a high degree of correlation between the two features [50; 51]. From Figure 9, we can see that the correlation between \(\sigma_{c}\) and \(\sigma_{t}\) reaches 0.6388, which indicates that the correlation between the two is strong, and it has been pointed out in the related literature [52] that there may be some kind of connection between the two. The correlation between \(\sigma_{c}\) and \(W_{et}\) is as high as 0.6124, which explains to a certain extent that when the rock is subjected to high stress, a large amount of elastic strain energy will be accumulated in its interior, and if the rock's compressive strength is lower and it cannot effectively resist the action of external stress,  
Figure 9: Correlation of rockburst samples.  
then the rock will be prone to occur. If the rock's compressive strength is low and can not effectively resist the role of external stress, then the rock is prone to damage [53], resulting in an abrupt release of elastic strain energy, which may cause rockburst. \(\sigma_{t}\) and \(\sigma_{c}/\sigma_{t}\) have a correlation of \(-0.5983\), with a strong negative correlation, which indicates that the brittle characteristics of the rock are closely related to rockbursts. The greater the brittleness of the rockbursts, the higher the tendency to be higher [54; 55]; in the presence of high stress, the rocks with lower tensile strength and higher brittleness are more likely to be damaged and prone to rockbursts. The correlation between the other features is weak, indicating a strong independence between the features. In summary, the consequences of the data correlation analysis are more consistent with the engineering reality, which further verifies the reliability and authenticity of the dataset.