Abstract
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.
Highlights
The origins of mineral prospectivity modeling (MPM) can be traced back to mathematical geology, which is an important field of mineral resource evaluation [1,2,3]
The knowledge-driven methods are based on expert knowledge and the experience of the spatial connection between mineral exploration criteria and the type of deposit sought and is often used in cases where there are insufficient known deposits in the study area, including fuzzy logic [4,5], boolean logic [3,4,5,6], evidential belief modeling [7,8,9], wildcat mapping [10,11], spatial factor analysis [12], etc
We considered the parameters that negatively correlated with the mineralization process and optimized the regularization multiplier, used geology, geochemistry and geophysical data to establish a maximum entropy (MaxEnt) model for mineral prospect analysis, and carried out quantitative prediction and evaluation of copper mineralization in the Mila Mountain integrated exploration area to delineate prospecting
Summary
The origins of mineral prospectivity modeling (MPM) can be traced back to mathematical geology, which is an important field of mineral resource evaluation [1,2,3]. With the rapid development of computer technology, many GIS-based mineral prospective analyses and prediction models have been developed. These models can be divided into knowledge-driven, data-driven and hybrid-driven methods. The knowledge-driven methods are based on expert knowledge and the experience of the spatial connection between mineral exploration criteria and the type of deposit sought and is often used in cases where there are insufficient known deposits in the study area, including fuzzy logic [4,5], boolean logic [3,4,5,6], evidential belief modeling [7,8,9], wildcat mapping [10,11], spatial factor analysis [12], etc. Data-driven methods are based on the spatial correlation between known mine sites and multiple exploration datasets and are typically
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