A novel data-knowledge dual-driven model coupling artificial intelligence with a mineral systems approach for mineral prospectivity mapping
Renguang Zuo, Fanfan Yang, Qiuming Cheng, Oliver P. Kreuzer
Abstract
Abstract Mineral prospectivity mapping (MPM) is recognized as an essential tool for targeting new mineral deposits. MPM typically comprises two end-member approaches: knowledge-driven and data-driven. Knowledge-driven MPM relies on expert knowledge, which is based on causal relationships but is not readily adaptable to dynamic changes. Data-driven MPM is capable of identifying underlying data patterns but involves poorly interpretable decision logic. Combining the advantages of knowledge-driven and data-driven paradigms is a research frontier in MPM. In this study, we designed a data-knowledge dual-driven model coupling artificial intelligence (AI) with a mineral systems approach to MPM. This model can utilize mineral systems as a guideline for data-driven AI to reasonably implement data selection, proxy extraction, and model operation for MPM. The newly developed data-knowledge dual-driven model achieved superior predictive performance and offered better interpretability compared to pure data-driven MPM.