## 3 Experimental Results
### Acoustic Emission Evolution Characteristics  
The acoustic emission count is an important parameter in acoustic emission technology, referring to the total number of signal oscillations that exceed a predefined threshold level. This counts as an indicator of stress wave activity released during the propagation of microcracks, fracturing, or plastic deformation within a material. It is closely tied to the activity of internal cracks in rocks. As the stress on a rock increases, the AE count rises, indicating the development of more or larger cracks. Notably, as the rock approaches ultimate failure, the AE count often surges dramatically--a phenomenon commonly referred to as a \"precursory anomaly\" [11; 24]. The AE count, as an easily obtainable parameter, offers certain advantages in reflecting the frequency and cumulative changes of acoustic emission activity, which is particularly useful for providing quick early warnings during real-time monitoring. However, it is limited by its singular statistical scope, susceptibility to noise, and lack of deeper insights into fracture mechanisms or the distribution of fracture scales. By contrast, the acoustic emission \(b\)-value stands out for its ability to analyze the dynamics  
Figure 5: The experimental results and failure modes of strain-mode rockburst. (**a**) The granite specimen AG2. (**b**) The granite specimen AG3. of crack propagation, identifying precursors to failure and facilitating interdisciplinary applications. Therefore, combining AE count analysis with \(b\)-value evaluation in practical applications offers a more comprehensive understanding of the rock fracture process. In their research on global seismic activity, [PERSON] and [PERSON] introduced the renowned Gutenberg-Richter (G-R) relationship (see Equation (1)), which describes the correlation between earthquake magnitude and frequency. In this relationship, the \(b\)-value is a constant that represents the function of the relative distribution of magnitudes and also serves as an indicator of the scale of crack propagation [25]. \[\lg N=a-bM \tag{1}\]  
In the formula, \(N\) represents the frequency of earthquakes within the magnitude range \(\Delta M\), \(M\) denotes the earthquake magnitude, and \(a\) and \(b\) are constants. To uncover the precursory characteristics of rock instability and failure, acoustic emission events occurring during the rock's deformation and fracture process can be regarded as analogous to seismic activity. By analyzing the variations in the AE \(b\)-value to predict dynamic disasters in rock masses, researchers typically adapt the G-R relationship by replacing the earthquake magnitude \(M\) with the acoustic emission amplitude \(M_{L}\). This modified relationship can then be expressed as follows [26],  
\[\left\{ \begin{array}{l}M_{L}=m_{s}/20\\ \lg N=a-bM_{L}\end{array} \right. \tag{2}\]  
In the formula, \(m_{s}\) represents the amplitude of the acoustic emissions detected during the loading process, while \(M_{L}\) is analogous to the \"magnitude\" in the context. Various methods can be used to calculate the AE \(b\)-value in rocks, including the exponential least squares method, maximum entropy spectrum method, linear least squares method, and maximum likelihood estimation method [27, 28]. This study utilizes the linear least squares method to determine the \(b\)-value of acoustic emission signals, thereby facilitating the handling of large-scale data through a sliding window approach. Each window is composed of 500 AE events, with a step size of 100 events to maintain data continuity and statistical representativeness. Within each sliding window, AE events are initially organized by time, followed by the statistical analysis of their energy distribution and logarithmic transformation. The linear least squares method is subsequently employed for fitting, resulting in the \(b\)-value being identified as the slope of the energy distribution.