Deep learning-driven simulation modelling for mine geological risk assessment integrating multi-source data

Mining geological risk assessment is crucial for ensuring production safety, yet traditional methods relying on single data sources and expert experience suffer from low accuracy and delayed early warning, often failing to capture dynamic risk characteristics.This paper proposes a simulation-driven intelligent assessment framework that integrates multi-source monitoring data -including geological, subsidence, hydrological, and microseismic information-with advanced deep learning techniques.The framework is designed to dynamically simulate risk evolution processes and construct an end-to-end predictive model.Validation on public datasets demonstrates an accuracy of 91.3%, significantly surpassing the 78.5% achieved by traditional methods (p < 0.01), with high stability and generalisation ability.This process-modelling paradigm effectively overcomes the bottlenecks of information incompleteness and response delay, providing reliable technical support for geological hazard prevention and a foundational tool for intelligent mine construction, thereby supporting dynamic safety management.