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Title: A MaxEnt Model for Mineral Prospectivity Mapping

Abstract

Mineral prospectivity mapping is an important preliminary step for mineral resource exploration. It has been widely applied to distinguish areas of high potential to host mineral deposits and to minimize the financial risks associated with decision making in mineral industry. In the present study, a maximum entropy (MaxEnt) model was applied to investigate its potential for mineral prospectivity analysis. A case study from the Nanling tungsten polymetallic metallogenic belt, South China, was used to evaluate its performance. In order to deal with model over-fitting, varying levels of β j-regularization were set to determine suitable β value based on response curves and receiver operating characteristic (ROC) curves, as well as via visual inspections of prospectivity maps. The area under the ROC curve (AUC = 0.863) suggests good performance of the MaxEnt model under the condition of balancing model complexity and generality. The relative importance of ore-controlling factors and their relationships with known deposits were examined by jackknife analysis and response curves. Prediction–area (P–A) curves were used to determine threshold values for demarcating high probability of tungsten polymetallic deposit occurrence within small exploration area. The final predictive map showed that high favorability zones occupy 14.5% of the study area and contain 85.5% of themore » known tungsten polymetallic deposits. Our study suggests that the MaxEnt model can be efficiently used to integrate multisource geo-spatial information for mineral prospectivity analysis.« less

Authors:
;  [1];  [2]
  1. Chinese Academy of Sciences, State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography (China)
  2. China University of Geoscience, Faculty of Earth Resources (China)
Publication Date:
OSTI Identifier:
22749939
Resource Type:
Journal Article
Journal Name:
Natural Resources Research
Additional Journal Information:
Journal Volume: 27; Journal Issue: 3; Other Information: Copyright (c) 2018 International Association for Mathematical Geosciences; http://www.springer-ny.com; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 1520-7439
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; DECISION MAKING; DEPOSITS; MAPPING; MINERAL INDUSTRY; ORES; TUNGSTEN

Citation Formats

Liu, Yue, Zhou, Kefa, and Xia, Qinglin. A MaxEnt Model for Mineral Prospectivity Mapping. United States: N. p., 2018. Web. doi:10.1007/S11053-017-9355-2.
Liu, Yue, Zhou, Kefa, & Xia, Qinglin. A MaxEnt Model for Mineral Prospectivity Mapping. United States. doi:10.1007/S11053-017-9355-2.
Liu, Yue, Zhou, Kefa, and Xia, Qinglin. Sun . "A MaxEnt Model for Mineral Prospectivity Mapping". United States. doi:10.1007/S11053-017-9355-2.
@article{osti_22749939,
title = {A MaxEnt Model for Mineral Prospectivity Mapping},
author = {Liu, Yue and Zhou, Kefa and Xia, Qinglin},
abstractNote = {Mineral prospectivity mapping is an important preliminary step for mineral resource exploration. It has been widely applied to distinguish areas of high potential to host mineral deposits and to minimize the financial risks associated with decision making in mineral industry. In the present study, a maximum entropy (MaxEnt) model was applied to investigate its potential for mineral prospectivity analysis. A case study from the Nanling tungsten polymetallic metallogenic belt, South China, was used to evaluate its performance. In order to deal with model over-fitting, varying levels of βj-regularization were set to determine suitable β value based on response curves and receiver operating characteristic (ROC) curves, as well as via visual inspections of prospectivity maps. The area under the ROC curve (AUC = 0.863) suggests good performance of the MaxEnt model under the condition of balancing model complexity and generality. The relative importance of ore-controlling factors and their relationships with known deposits were examined by jackknife analysis and response curves. Prediction–area (P–A) curves were used to determine threshold values for demarcating high probability of tungsten polymetallic deposit occurrence within small exploration area. The final predictive map showed that high favorability zones occupy 14.5% of the study area and contain 85.5% of the known tungsten polymetallic deposits. Our study suggests that the MaxEnt model can be efficiently used to integrate multisource geo-spatial information for mineral prospectivity analysis.},
doi = {10.1007/S11053-017-9355-2},
journal = {Natural Resources Research},
issn = {1520-7439},
number = 3,
volume = 27,
place = {United States},
year = {2018},
month = {7}
}