Study on geological hazard prediction model and risk identification in deep mining

To enhance safety in deep mines, a risk identification and geological hazard prediction model based on multi-source information fusion was developed. By integrating monitoring data such as microseismic signals, in-situ stress measurements, and ground-penetrating radar reflection images, this study employs wavelet packet decomposition and deep learning methods to perform data preprocessing, feature extraction, and deep fusion. A multi-channel parallel network architecture was designed, incorporating attention mechanisms and cross-channel fusion to enhance the identification and prediction capabilities for geological hazards in mining areas. Results demonstrate high accuracy in mine risk classification and future hazard trend prediction, with particularly outstanding performance in identifying high-risk scenarios. Compared to traditional methods, the model exhibits significantly improved prediction precision and robustness, proving its effectiveness and application value in complex mining environments. This model provides scientific basis for mine disaster early warning and risk prevention, advancing the progress of intelligent mine management.