机器学习与成矿预测:以闽西南铁多金属矿预测为例

作为近年来爆炸式发展的方法模型,机器学习为地质找矿提供了新的思维和研究方法.本文探讨矿产预测研究的理论方法体系,总结机器学习在矿产预测领域的特征信息提取和信息综合集成两个方面的应用现状,并讨论机器学习在矿产资源定量预测领域面临的训练样本稀少且不均衡、模型训练中缺乏不确定性评估、缺少反哺研究、方法选择等困难和挑战.进一步...

Machine learning for mineral prospectivity: A case study of iron-polymetallic mineral prospectivity in southwestern Fujian

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.