## 3 Results
### Dataset Creation and Enhancement
#### 3.2.1 The Guided Diffusion Model  
The guided diffusion model is primarily composed of a U-Net architecture, which includes an encoder, bottleneck, decoder, and output. The encoding component incorporates a feature extraction module based on convolutional operations with dimensions \(3\times 6\times 64\times 64\), residual modules with dimensions \(3\times 64\times 64\times 64\), \(3\times 128\times 32\times 32\), \(3\times 256\times 16\times 16\), and \(3\times 512\times 8\times 8\), down-sampling layers with dimensions \(3\times 64\times 64\times 64\), \(3\times 128\times 32\times 32\), and \(3\times 256\times 16\times 16\), and an attention module with dimension \(3\times 256\times 16\times 16\). The bottleneck includes residual modules with dimensions \(3\times 512\times 8\times 8\) and an attention module with dimension \(3\times 256\times 8\times 8\). The decoder comprises residual modules with dimensions \(3\times 1024\times 8\times 8\), \(3\times 384\times 16\times 16\), \(3\times 192\times 32\times 32\) and \(3\times 128\times 64\times 64\), as well as up-sampling layers with dimensions \(3\times 256\times 8\times 8\), \(3\times 128\times 16\times 16\), and \(3\times 64\times 32\times 32\). Finally, the output module is a convolutional layer with dimension \(3\times 64\times 64\times 64\). The prediction steps of the guided diffusion model in the training set are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Buertai Coal Mine Database** \\ \cline{2-3}
**Dataset** & **Rock Burst Precursor** \\ \hline Train & 2080 \\ \hline Test & 520 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of rock burst precursor training and validation sets in Buertai coal mine database.  
The guided diffusion model's parameter optimization was accomplished through the implementation of stochastic gradient descent (SGD) as the optimization algorithm. The training configuration employed a batch size of 8, while maintaining a dropout probability of 0.2 to mitigate the risk of overfitting. The learning rate was specifically tuned to \(5\times 10^{-5}\) for optimal convergence.  
Throughout the training process, testing was performed every 10 epochs. Due to disk storage limitations, training was halted after 4 days and 13 h, reaching epoch 2428. The LOSS curve is a crucial tool for understanding the model's training progress. Typically, during the early stages of training, the LOSS value is relatively high, as the model's parameters are far from optimal. As training progresses, the model begins to adjust its weights and biases, leading to a decrease in the LOSS value. This downward trend reflects the model's improvement in predicting the target outputs. Following a thorough evaluation of the loss values, the optimal model was saved at epoch 1850. The evaluation metrics, calculated through mean squared error (MSE) methodology for both training and validation datasets, demonstrated a consistent downward trajectory before reaching a plateau. This observed behavior suggests a successful convergence and the optimal adaptation of the Guided Diffusion architecture. The corresponding training loss progression is visually represented in Figure 9. All training procedures were executed on an RTX 5000 GPU equipped with 16 GB of VRAM (Nvidia, Santa Clara, CA, USA).  
Figure 8: Diffusion model prediction process during training.  
Figure 9: The loss curve for the training set.