scholarly journals Data-driven based logistic function and prediction-area plot for mineral prospectivity mapping: a case study from the eastern margin of Qinling orogenic belt, central China

2021 ◽  
Author(s):  
Hongyang Bai ◽  
Yuan Cao ◽  
Heng Zhang ◽  
Wenfeng Wang ◽  
Chaojun Jiang ◽  
...  
Keyword(s):  
Logistic Function ◽  
Gold Deposits ◽  
Orogenic Gold ◽  
Central China ◽  
Data Driven ◽  
Orogenic Gold Deposits ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Potential Area ◽  
Prospectivity Mapping

Abstract he present work combines data-driven based logistic function with prediction-area plot for delineating target areas of orogenic gold deposits in eastern margin of Qinling metallogenic belt, central China. Firstly, the values of geological and geochemical information layer were transformed into a series of fuzzy numbers with a range of 0-1 through a data-driven based logistic function on the basis of mineralization theory of the orogenic gold deposits. Secondly, the prediction-area(P-A) plot was performed on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme for mineral prospectivity mapping(MPM) based on the known gold occurrences. What’s more, to further prove the advantages of this method, we also used a knowledge-driven approach for comparison purpose. Finally, with the concentration-area(C-A) fractal model, the fractal thresholds were determined and a mineral prospecting map was generated. The result, five of the six known gold deposits are located in high and moderate potential areas (accounts for 18.6 % of the study area), one in low potential area (accounts for 38.4 % of the study area) and none in weak potential area (accounts for 43 % of the study area), confirmed the joint application of data-driven based logistic function and prediction-area plot a simple, effective and low-cost method for mineral prospectivity mapping, which can be a guidance for further work in the research area.

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.

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