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Article

Application of a Maximum Entropy Model for Mineral Prospectivity Maps

by 1,2, 1,*, 1, 1 and 3
1
Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China
2
Student Affairs Department, China West Normal University, Nanchong 637002, China
3
Institute of Ecological Research, China West Normal University, Nanchong 637002, China
*
Author to whom correspondence should be addressed.
Minerals 2019, 9(9), 556; https://doi.org/10.3390/min9090556
Received: 24 July 2019 / Revised: 10 September 2019 / Accepted: 12 September 2019 / Published: 15 September 2019
(This article belongs to the Special Issue Geological Modelling, Volume II)
The effective integration of geochemical data with multisource geoscience data is a necessary condition for mapping mineral prospects. In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper deposits by integrating 43 ore-controlling factors from geological, geochemical and geophysical data. The MaxEnt model was used to screen the ore-controlling factors, and eight ore-controlling factors (i.e., stratigraphic combination entropy, structural iso-density, Cu, Hg, Li, La, U, Na2O) were selected to establish the MaxEnt model to determine the highest potential zone of copper deposits. The spatial correlation between each ore-controlling factor and the occurrence of a copper mine was studied using a response curve, and the relative importance of each ore-controlling factor was determined by jackknife analysis in the MaxEnt model. The results show that the occurrence of copper ore is positively correlated with the content of Cu, Hg, La, structural iso-density and stratigraphic combination entropy, and negatively correlated with the content of Na2O, Li and U. The model’s performance was evaluated by the area under the receiver operating characteristic curve (AUC), Cohen’s maximized Kappa and true skill statistic (TSS) (training AUC = 0.84, test AUC = 0.8, maximum Kappa = 0.5 and maximum TSS = 0.6). The results indicate that the model can effectively integrate multi-source geospatial data to map mineral prospectivity. View Full-Text
Keywords: maximum entropy model; mineral exploration; GIS; mineral perspective mapping; Mila mountain maximum entropy model; mineral exploration; GIS; mineral perspective mapping; Mila mountain
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MDPI and ACS Style

Li, B.; Liu, B.; Guo, K.; Li, C.; Wang, B. Application of a Maximum Entropy Model for Mineral Prospectivity Maps. Minerals 2019, 9, 556. https://doi.org/10.3390/min9090556

AMA Style

Li B, Liu B, Guo K, Li C, Wang B. Application of a Maximum Entropy Model for Mineral Prospectivity Maps. Minerals. 2019; 9(9):556. https://doi.org/10.3390/min9090556

Chicago/Turabian Style

Li, Binbin, Bingli Liu, Ke Guo, Cheng Li, and Bin Wang. 2019. "Application of a Maximum Entropy Model for Mineral Prospectivity Maps" Minerals 9, no. 9: 556. https://doi.org/10.3390/min9090556

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