Machine Learning-Based 3D Modeling of Mineral Prospectivity Mapping in the Anqing Orefield, Eastern China

被引：0
|
作者：
Yaozu Qin
Liangming Liu
Weicheng Wu
机构：
[1] East China University of Technology,Key Laboratory of Digital Land and Resources, Faculty of Earth Sciences
[2] Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry,Computational Geosciences Research Centre, School of Geoscience and Info
[3] Central South University,Physics
来源：
Natural Resources Research | 2021年 / 30卷
关键词：
Geological predictive factor; Anqing orefield; Weight of evidence; Random forest; Mineral prospectivity mapping;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Actual geological data, accurate models and precise samples are critical for ore targeting.The RF-based prediction model is more applicable for mapping mineral prospectivity than other algorithms in this study.The determination of sample set is more important than algorithm if there is not enough field data.
引用
收藏
页码：3099 / 3120
页数：21
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