## 2 Model Principles and Methods  
### Establishment of Rock Burst Prediction Model  
The BO-LSTM model was selected for predicting rock burst risks due to its advantages in handling time series data:  
Characteristics of Time Series Data: The LSTM neural network excels at processing time series data and can effectively capture long-term dependencies within the sequence. Given that microseismic monitoring data are typical time series data, LSTM can identify the temporal dependencies, making it advantageous for predicting the next step in rock burst risk. Avoiding Gradient Vanishing: Traditional recurrent neural networks (RNNs) tend to suffer from the vanishing gradient problem when dealing with long-term dependencies in sequences. LSTM, with its unique gating mechanisms (forget gate, input gate, and output gate), effectively avoids this issue, making it more robust when handling complex time series data. Prediction Performance: LSTM has demonstrated superior predictive performance, with experimental results showing that it outperforms single models like GRUs and 1D-CNNs across multiple evaluation metrics. The optimized BO-LSTM model further enhances prediction accuracy, demonstrating the practical application value of this model. Below are the specific steps for constructing the dynamic ground pressure warning model proposed in this paper:  
1. Collect Microseismic Monitoring Information: Collect on-site microseismic monitoring information. Due to differences in data dimensions, direct importation into the model for training is not feasible and requires data preprocessing. 2. Establish an Expert Judgment System: In order to address the dearth of sample labels in the model, it is necessary to establish an expert judgment system. The use of expert judgments on microseismic monitoring data as data labels is proposed as a means of generating the data set. The system inputs microseismic data for a specific time period, processes the data, and assesses the dynamic pressure hazards for the following day. The combination of expert analysis and empirical experience yields judgment values, which are then output as sample labels. The judgment scores, which range from 0 to 100, correspond to four levels of dynamic ground pressure. The scores are as follows: 0-25 for no dynamic pressure hazard, 25-50 for a weak dynamic pressure hazard, 50-75 for a moderate dynamic pressure hazard, and 75-100 for a strong dynamic pressure hazard. 3. Model Construction: In the model construction process, the time series characteristics of microseismic energy changes are taken into account. Python 3.11 is employed to construct the LSTM network model, which comprises an input layer, a hidden layer, and an output layer. Additionally, a Bayesian optimization algorithm (BO) is utilized to optimize the parameters. The Python programming language was employed to implement the model on the Jupyter platform. The sample data are divided into two distinct sets: a training set and a verification set. The training set is employed to train the model, while the verification set is utilized to test the trained model and optimize the parameters through the Bayesian algorithm until the optimal neural network model is identified. 4. Obtain Predictions from the Optimal Model: The optimal model is evaluated based on the predicted value. The degree of fit between the predicted value and the actual value of the model is evaluated by comparing the curve fitting and statistical indicators, including the mean absolute error (MAE), mean absolute percentage error (MAPE), and the variance accounted for (VAF). This analysis is conducted using field data to verify the accuracy and practical application value of the BO-LSTM model. 5. Further Model Validation: In order to further verify the accuracy of the model prediction, based on the aforementioned methodology for establishing the model, the daily total energy value, daily maximum energy value, and daily frequency are predicted. The actual microseismic data are employed as the sample label for model training.