Mineral Prospectivity Mapping based on Isolation Forest and Random Forest: Implication for the Existence of Spatial Signature of Mineralization in Outliers

2021 ◽  
Author(s):  
Shuai Zhang ◽  
Emmanuel John M. Carranza ◽  
Keyan Xiao ◽  
Hantao Wei ◽  
Fan Yang ◽  
...  
Keyword(s):  
Random Forest ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Isolation Forest ◽  
Prospectivity Mapping

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

scholarly journals Application of Knowledge-Driven Methods for Mineral Prospectivity Mapping of Polymetallic Sulfide Deposits in the Southwest Indian Ridge between 46° and 52°E

Minerals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 970
Author(s):  
Yao Ma ◽  
Jiangnan Zhao ◽  
Yu Sui ◽  
Shili Liao ◽  
Zongyao Zhang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Ahp ◽  
Hydrothermal Activity ◽  
Southwest Indian Ridge ◽  
Sulfide Deposit ◽  
Southwest Indian Ocean ◽  
Sulfide Deposits ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Prospectivity Mapping

As a product of hydrothermal activity, seafloor polymetallic sulfide deposit has become the focus of marine mineral exploration due to its great prospects for mineralization potential. The mineral prospectivity mapping is a multiple process that involves weighting and integrating evidential layers to further explore the potential target areas, which can be categorized into data-driven and knowledge-driven methods. This paper describes the application of fuzzy logic and fuzzy analytic hierarchy process (AHP) models to process the data of the Southwest Indian Ocean Mid-Ridge seafloor sulfide deposit and delineate prospect areas. Nine spatial evidential layers representing the controlling factors for the formation and occurrence of polymetallic sulfide deposit were extracted to establish a prospecting prediction model. Fuzzy logic and fuzzy AHP models combine expert experience and fuzzy sets to assign weights to each layer and integrate the evidence layers to generate prospectivity map. Based on prediction-area (P-A) model, the optimal gamma operator (γ) values were determined to be 0.95 and 0.90 for fuzzy logic and fuzzy AHP to synthesize the evidence layers. The concentration-area (C-A) fractal method was used to classify different levels of metallogenic probability by determining corresponding thresholds. Finally, Receiver Operating Characteristic (ROC) curves were applied to measure the performance of the two prospectivity models. The results show that the areas under the ROC curve of the fuzzy logic and the fuzzy AHP model are 0.813 and 0.887, respectively, indicating that prediction based on knowledge-driven methods can effectively predict the metallogenic favorable area in the study area, opening the door for future exploration of seafloor polymetallic sulfide deposits.

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