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

2021 ◽  
Author(s):  
Yaozu Qin ◽  
Liangming Liu ◽  
Weicheng Wu
Keyword(s):  
Machine Learning ◽  
3D Modeling ◽  
Eastern China ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Prospectivity Mapping

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

Minerals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1126
Author(s):  
Kaijian Wang ◽  
Xinqi Zheng ◽  
Gongwen Wang ◽  
Dongya Liu ◽  
Ning Cui
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Gold Deposits ◽  
Western China ◽  
Support Vector ◽  
Learning Methods ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Prospectivity Mapping

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.

scholarly journals A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping

2020 ◽  
Vol 11 (6) ◽  
pp. 2297-2308 ◽  
Author(s):  
Ziye Wang ◽  
Zhen Yin ◽  
Jef Caers ◽  
Renguang Zuo
Keyword(s):  
Monte Carlo ◽  
Risk Return ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Prospectivity Mapping
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