Research on an Intelligent Prospecting Workflow for Bauxite Deposits: Integrating AI Prediction with Surpac 3D Visualization

Abstract

The exploration for concealed bauxite deposits presents significant challenges for traditional methods. This paper proposes a novel intelligent prospecting workflow that seamlessly integrates artificial intelligence (AI) prediction with professional three-dimensional (3D) visualization and analysis in Surpac. The workflow begins with the systematic processing and standardization of multi-source geospatial data, including geological maps, geophysical data, and remote sensing imagery, into a unified gridded format. This data serves as input for machine learning models, such as Random Forest, which are trained on known mineral occurrences to generate a predictive mineral prospectivity map. The core innovation lies in the closed-loop workflow where the AI-generated prospectivity model, exported as an ASCII grid file, is directly imported into Surpac. Within Surpac's powerful 3D environment, geologists can dynamically visualize the probability model fused with other geological data, perform interactive 3D interpretation, and delineate exploration targets. This integration creates a synergistic cycle where quantitative AI-driven insights directly inform qualitative spatial reasoning and decision-making within a robust 3D platform, significantly enhancing the efficiency and effectiveness of bauxite exploration.