Abstract:

As a rapidly evolving technology in recent years, machine learning (ML) provides a novel approach for mineral prospecting (MP). In this paper, we discuss the progress on the methodology and theory of machine learning and summarize the applications of ML in MP in the areas of pattern recognition/information mining and information integration. We also point out the difficulties and challenges of ML in MP, such as data imbalance, lack of training data, lack of uncertainty evaluation in model selection, feedback feeding, and method selection. Here, we use mineral prospecting of the Makeng-type iron deposit in southwestern Fujian, China as an example to illustrate the process of using the ML method in MP. A complete prediction procedure should include (1) establishing a metallogenic model and identifying ore controlling factors by studying metallogenic systems; (2) building an exploration model and obtaining relevant data by researching exploration systems;(3) establishing a prediction model and extracting predictive factors by researching prediction evaluation systems;(4) obtaining metallogenic probability through information integration of predictive factors using ML models;(5) evaluating uncertainties of prediction performances and results; and (6) delineating prospecting/target areas and estimating resource reserves. Lastly, a future research roadmap for developing big-data based quantitative mineral prospecting theory and methods is proposed, guided by the geological big data and Earth system theory in following the research route of earth system-metallogenic system-exploration system-prediction evaluation system.

Key words: mineral prospectivity, machine learning, Makeng-type iron deposit