## 5 Discussion of Problems  
1. Potential Limitations of Using LSTM for Analyzing Microseismic Data  
While LSTM models are powerful tools for analyzing time series data, several limitations may arise when dealing with microseismic data, particularly in the context of nonlinear relationships:  
Assumption of Sequential Data: LSTM models excel in capturing temporal dependencies, but they may struggle with nonlinear relationships that are not adequately represented in the training data. If the relationship between microseismic energy and rockfall risk is highly nonlinear, the model may fail to accurately predict outcomes. Data Quality and Quantity: LSTM requires large amounts of high-quality data to generalize effectively. In mining environments, obtaining sufficient labeled data can be challenging due to the rarity of certain events. Poor-quality or imbalanced data sets can lead to inaccurate predictions and a lack of robustness in the model. Complexity of Interpretability: The \"black box\" nature of LSTM makes it difficult to interpret how specific input features influence predictions. This lack of transparency can hinder the practical application of results in critical safety decisions, as stakeholders may be hesitant to trust the model's recommendations. Overfitting Risks: LSTMs can overfit to the training data, particularly if the model architecture is too complex for the available data. Overfitting compromises the model's ability to generalize to unseen data, leading to unreliable predictions in real-world scenarios. These limitations could affect the study's results by potentially leading to incorrect assessments of rock impact risks, which may undermine the overall safety of mining operations. 1. Integration into Existing Mine Safety Monitoring Systems  
Integrating the results of LSTM research into existing mine safety monitoring systems presents both opportunities and challenges:  
Data Integration: The successful implementation of LSTM models requires seamless integration with existing data sources, including microseismic monitoring systems, geologi  
Figure 11: Gengcun Coal Mine microseismic measuring point layout diagram. cal databases, and real-time sensor inputs. This may involve significant data preprocessing and synchronization efforts. Real-Time Processing: LSTM models can be computationally intensive, particularly when processing large data sets in real time. Technological challenges may include ensuring that the existing infrastructure can handle the computational load, which may necessitate upgrades or optimizations. User Training and Adaptation: Personnel may need training to understand and interpret the outputs from LSTM models effectively. This includes recognizing the limitations and appropriate contexts for applying model predictions. Continuous Monitoring and Model Updating: Mining environments are dynamic; therefore, the LSTM model should be regularly updated with new data to maintain its predictive accuracy. This requires a robust feedback loop between model outputs and ground truth observations. Risks of Over-Reliance on Machine Learning Algorithms  
An over-reliance on machine learning algorithms, such as BO-LSTM, can lead to several critical issues:  
Neglect of Traditional Methods: Traditional analysis methods, including empirical studies and domain expertise, provide essential insights that may be overlooked if machine learning is solely relied upon. These methods often incorporate years of expert knowledge, which can be crucial for understanding complex geological conditions. Potential for Misinterpretation: If decision-makers place undue trust in machine learning outputs without considering traditional methods, they may misinterpret the risks associated with rock impacts, leading to inadequate safety measures. To mitigate these risks, it is crucial to adopt a hybrid approach that combines machine learning with traditional analysis methods, ensuring a comprehensive understanding of rock impact risks.