Construction of Multilayered Mineralization Prediction Data Based on Geological Big Data and Artificial Intelligence

With the advancement of big data and artificial intelligence technologies, mineralization prediction is undergoing a series of technological innovations, transitioning from data-sparse to data-intensive approaches. This shift is expected to become a new "technology engine" for breakthroughs in mineral exploration and discovery. Despite the substantial accumulation of heterogeneous multisource geological, geophysical, geochemical, and remote sensing data, as well as rich geological reports and literature, it remains a critical research challenge to effectively integrate and deeply mine these valuable data resources to further optimize mineralization prediction indicator systems and construct high-quality mineralization prediction datasets. To address the challenge, this paper proposes integrating multilayered and multidimensional mineralization prediction knowledge across the Earth system, metallogenic system, exploration system, and prediction-evaluation system through large models and knowledge graph technologies. A multilayered, multi-system-coupled mineralization prediction knowledge graph will be constructed, and intelligent construction of mineralization prediction indicator systems will be achieved through knowledge graph mining. Based on big data and artificial intelligence technologies, a multilayered method system for intelligent construction of mineralization prediction data will be developed. This system will focus on intelligent mining of geological exploration data, automated scientific data extraction and spatiotemporal analysis, and intelligent data inversion and simulation. Such advancements are expected to strengthen the deep coupling between prediction data and indicators, enhancing the accuracy and reliability of prediction results, and providing more robust technical support for mineral exploration and discovery breakthroughs.