A Multi-Model Ensemble Approach for Gold Mineral Prospectivity Mapping: A Case Study on the Beishan Region, Western China

被引:11
|
作者
Wang, Kaijian [1 ]
Zheng, Xinqi [1 ,2 ]
Wang, Gongwen [3 ]
Liu, Dongya [1 ]
Cui, Ning [4 ]
机构
[1] China Univ Geosci Beijing, Sch Informat & Engn, Beijing 100083, Peoples R China
[2] MNR China, Technol Innovat Ctr Terr Spatial Big Data, Beijing 100083, Peoples R China
[3] China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China
[4] Dev & Res Ctr China Geol Survey, Beijing 100037, Peoples R China
基金
中国国家自然科学基金;
关键词
stacking ensemble learning method; random forest; support vector machine; maximum entropy model; mineral prospectivity mapping; Beishan region; China; RANDOM FOREST; LOGISTIC-REGRESSION; PREDICTIVE MODELS; NEURAL-NETWORKS; MACHINE; VALIDATION; CLASSIFICATION; DISTRICT; CLASSIFIERS; INFORMATION;
D O I
10.3390/min10121126
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Mineral prospectivity mapping (MPM) needs robust predictive techniques so that the target zones of mineral deposits can be accurately delineated at a specific location. Although an individual machine learning algorithm has been successfully applied, it remains a challenge because of the complicated non-linear relations between prospecting factors and deposits. Ensemble learning methods were efficiently applied for their excellent generalization, but their potential has not been fully explored in MPM. In this study, three well-known machine learning models, namely random forest (RF), support vector machine (SVM), and the maximum entropy model (MaxEnt), were fused into ensembles (i.e., RF-SVM, RF-MaxEnt, SVM-MaxEnt, RF-SVM-MaxEnt) to produce a final prediction. The paper aims to investigate the potential application of stacking ensemble learning methods (SELM) for MPM. In this study, 69 hydrothermal gold deposits were split into two parts: 70% for the training model and 30% for testing the model. Then, 11 mineral prospecting factors were selected as a spatial dataset constructed for MPM. Finally, the models' performance was assessed using the receiver operating characteristic (ROC) curves and five statistical metrics. Compared with other single methods, the SELM framework showed an improved predictive performance in the model evaluation. Therefore, this finding suggests that the SELM framework is promising and should be selected as an alternative technique for MPM.
引用
收藏
页码:1 / 19
页数:19
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