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A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping

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This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits. An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation and in integration of evidential features to map relative potential for mineral deposit occurrence.

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Authors and Affiliations

  1. International Institute for Geo-information Science and Earth Observation (ITC), Enschede, The Netherlands

    Alok Porwal, Emmanuel John M. Carranza & Martin Hale

  2. Department of Mines and Geology, Govt. of Rajasthan, Udaipur, India

    Alok Porwal

  3. Faculty of Geoscience, Utrecht University, Utrecht, The Netherlands

    Martin Hale

  4. ITC, Hengelosestraat 99, 7500 AA, Enschede, The Netherlands

    Emmanuel John M. Carranza

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  1. Alok Porwal
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  2. Emmanuel John M. Carranza
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  3. Martin Hale
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Correspondence to Emmanuel John M. Carranza.

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Porwal, A., Carranza, E.J.M. & Hale, M. A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping. Nat Resour Res 15, 1–14 (2006). https://doi.org/10.1007/s11053-006-9012-7

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  • DOI: https://doi.org/10.1007/s11053-006-9012-7

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