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

Key Points

• The central aim is to utilize deep learning to improve geological risk assessment in mining through data integration.
• Developed simulation models using deep learning techniques.
• Integrated multi-source geological data for assessment.
• Conducted risk evaluation based on simulated outcomes.
• Enhanced accuracy in risk predictions compared to traditional methods.
• Provided a comprehensive overview of geological risks using integrated data sources.
• Demonstrated potential benefits of deep learning in existing assessment processes.

Abstract