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A MaxEnt Model for Mineral Prospectivity Map**

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Abstract

Mineral prospectivity map** 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 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.

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Acknowledgments

We would like to thank Editor-in-Chief Dr. John Carranza and two anonymous reviewers who provided valuable comments that have helped us to greatly improve the quality of the paper. This work was jointly funded by the Natural Science Foundation of China (Nos. 41672328, U1503291) and the CAS “Light of West China” program (2015-XBQN-B-23).

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Authors and Affiliations

  1. State Key Laboratory of Desert and Oasis Ecology, **njiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, **njiang, China

    Yue Liu & Kefa Zhou

  2. **njiang Research Centre for Mineral Resources, **njiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, **njiang, China

    Yue Liu & Kefa Zhou

  3. **njiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi, 830011, **njiang, China

    Yue Liu & Kefa Zhou

  4. Department of Earth and Space Science and Engineering, York University, Toronto, M3J 1P3, Canada

    Yue Liu

  5. Faculty of Earth Resources, China University of Geoscience, Wuhan, 430074, China

    Qinglin **a

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  1. Yue Liu
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  3. Qinglin **a
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Correspondence to Yue Liu.

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Liu, Y., Zhou, K. & **. Nat Resour Res 27, 299–313 (2018). https://doi.org/10.1007/s11053-017-9355-2

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