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Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

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Abstract

In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the “model” and “validation” base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.

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Authors and Affiliations

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

    Alok Porwal, E. J. M. Carranza & M. Hale

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

    Alok Porwal

  3. Delft University of Technology, Delft, The Netherlands

    M. Hale

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  1. Alok Porwal
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  2. E. J. M. Carranza
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  3. M. Hale
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Correspondence to Alok Porwal.

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Porwal, A., Carranza, E.J.M. & Hale, M. Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping. Natural Resources Research 12, 1–25 (2003). https://doi.org/10.1023/A:1022693220894

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