## 3 Results
### Dataset Creation and Enhancement
#### 3.2.3 Auxiliary Model for Recognizing Rock Burst Precursor Characteristics  
The input FEMR data for the auxiliary model has dimensions of \(1024\times 512\times 3(\mathrm{H}\times\mathrm{W}\times\mathrm{C})\). Initially, the data are processed using a \(4\times 4\) non-overlapping convolution with a stride of 4, followed by a normalization layer to generate \(\frac{H}{4}\times\frac{W}{4}\times\mathrm{C}\) patch embeddings. The architectural framework for building a multi-level feature hierarchy incorporates four distinct processing stages within its core structure, with each successive stage implementing incrementally larger stride values. Between consecutive stages, a non-overlapping \(2\times 2\) convolution with a stride of 2 is applied to down-sample the characteristics map, reducing its spatial size by half and doubling the characteristics dimensions. For the classification task, the characteristics maps from the final stage are normalized, and a linear classifier with pooled characteristics is used to predict the logits. In Stage 1, the number of blocks is 1, the channel dimension is 96, the region size in the local attention module is 7, and the number of heads in DMHA is 3. In Stage 2, the number of blocks is 1, the channel dimension is 192, the region size in the local attention module is 7, and the number of heads in DMHA is 6. In Stage 3, the number of blocks is 3, the channel dimension is 384, the region size in the local attention module is 7, the number of heads in DMHA is 12, and the offset groups in DMHA is 3. In Stage 4, the number of blocks is 1, the channel dimension is 768, the region size in the local attention module is 7, the number of heads in DMHA is 24, and the offset groups in DMHA is 6. An auxiliary model, which has a depth of 225 layers and approximately \(11\times 10^{6}\) parameters, was used. The model's parameter optimization was accomplished through the implementation of the stochastic gradient descent algorithm. The model was configured with a batch size of 3, a learning rate set at \(1\times 10^{-2}\), and a dropout rate of 0.9 to prevent overfitting. Throughout the training process, testing was conducted every epoch. Due to disk capacity constraints, the training was stopped after 55.2 min, at epoch 100. After the comprehensive verification of the loss value, the best training model was saved at epoch 57. The training metrics, calculated through binary cross-entropy (BCE) for both the training and validation datasets, displayed a consistent reduction before reaching equilibrium. This behavior confirms the successful convergence and optimal adaptation of the guided diffusion architecture. The corresponding training loss progression is illustrated in Figure 10. All training and testing processes were completed on an RTX 5000 GPU with 16 GB of VRAM. During the testing process, 510 out of 520 FEMR data samples were correctly recognized, resulting in an accuracy of 98.07%. Early Warning Results for Rock Burst Using Signal Super Prediction and Precursor Recognition Framework  
We selected 15 days of continuous FEMR data with rock burst risk, recorded from 1 April to 15 April 2023, from the No. 8 return wind roadway in the West No. 3 mining area of the Buertai coal mine, to test the framework. FEMR time-series data span from 00:00 on 1 April 2023, to 22:00 on 7 April 2023, during which the FEMR time-series data exhibit a periodic cyclic trend, indicating active mining operations at the working face. From 00:00 on 1 April 2023, to 15:51 on 9 April 2023, a slight cyclic growth trend in the FEMR time-series data can be observed. However, by 06:13 on 12 April 2023, the FEMR time-series data show a significant and pronounced cyclic growth trend, which can be identified as a distinctive signature preceding rock burst occurrences within the FEMR signal patterns. We input the known FEMR data from 00:00 to 12:00 on 1 April 2023, into the super prediction framework to predict future data.