Coal mine seismic disaster prediction method based on deep learning

Coal mine seismic disasters pose a significant threat to the safety and operational efficiency of coal mines. The early prediction of these seismic events is crucial for risk mitigation. This paper proposes a coal mine seismic disaster prediction method based on deep learning, which integrates multi-source data, including geological, historical, and operational information. We use a Convolutional Neural Network (CNN) model to capture spatial features and a Long Short-Term Memory (LSTM) network to model the temporal dependencies in seismic data. The model is trained using datasets from multiple coal mines, and its prediction accuracy is evaluated through standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the proposed method significantly outperforms traditional machine learning methods in terms of prediction accuracy and robustness, demonstrating its potential for implementing real-time prediction and early warning systems in coal mine operations. This method provides a promising solution for improving safety management and disaster preparedness in coal mines.