Abstract
In this paper, we describe fuzzy models for predictive porphyry Cu 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 piece-wise 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. The mineral favorability maps for porphyry Cu exploration were produced in a geographic information systems environment and took into account three sources of data and information: (1) satellite images; (2) a geochemical survey; (3) geo-structural mapping. These data and information were integrated through a conceptual model developed for porphyry Cu mines and occurrences in the studied region. Both favorability maps highlighted the known porphyry Cu occurrences and validated the approach, but the data-driven method shows better results than the knowledge-driven method.
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