成都理工大学学报（自然科学版）2025，Vol.52Issue(5)：859-872,14.DOI:10.12474/cdlgzrkx.2025053103
基于空间随机森林的矿产资源定量预测——以河北大庙—红石砬钒钛磁铁矿带为例
Spatial random forest for mineral prospectivity mapping:a case study of the Damiao-Hongshila Fe-V-Ti belt,China
摘要
Abstract
Existing mineral prospectivity mapping(MPM)methods pay insufficient attention to spatial heterogeneity and spatial autocorrelation,which limits their effectiveness.To address this,we have developed a spatial random forest(SRF)method that integrates K-means clustering with traditional random forest(RF)for MPM in the Damiao-Hongshila Fe-V-Ti ore belt,Hebei Province,China.The SRF method first constructs spatially heterogeneous subsets via K-means clustering,establishing heterogeneous RF models as base learners.The predicted probabilities are subsequently aggregated using inverse distance weighting according to the distances between the target center locations and the corresponding cluster centroids.Our method outperforms traditional RF,with an improved area under the curve score of 6.83%,an improved accuracy of 8.62%,and an improved F1 score of 8.52%while maintaining high interpretability.Based on the SRF model and a concentration-area fractal analysis,five prospective targets were delineated for mineral exploration in the study area.In summary,SRF provides an effective framework for modeling both spatial heterogeneity and spatial autocorrelation in MPM.关键词
空间随机森林/贝叶斯优化/浓度-面积分形/大庙—红石砬钒钛磁铁矿带/矿产资源定量预测Key words
spatial random forest/Bayesian optimization/concentration-area fractal analysis/Damiao-Hongshila Fe-V-Ti ore belt/mineral prospectivity mapping分类
天文与地球科学引用本文复制引用
卢紫阳,陈文良,王功文,刘新星,李风,张帅,刘烊,东玉龙,张智强..基于空间随机森林的矿产资源定量预测——以河北大庙—红石砬钒钛磁铁矿带为例[J].成都理工大学学报（自然科学版）,2025,52(5):859-872,14.基金项目
河北省自然科学基金青年基金项目(D2023403051) （D2023403051）
2025年度河北省燕赵黄金台聚才计划骨干人才项目(留学回国平台)(B2025020) （留学回国平台）
河北省战略性关键矿产研究协同创新中心开放基金(HGUXT-2023-13). （HGUXT-2023-13）
