The different methods for delineating favorable areas for mineral exploration utilize exploration criteria regarding targeted mineral deposits. The criteria are elicited according to conceptual model parameters of the targeted mineral deposits. The selection of indicator criteria, the evaluation of their comparative importance, and their integration are critical in mineral prospectivity modelling. In data-driven methods, indicator features are weighted using functions whereby the importance of certain indicator criteria may be ignored. In this paper, a data-driven method is described for recognizing and converting exploration criteria into quantitative coefficients representing favorability for the presence of the targeted mineral deposits. In this approach, all indicator features of the targeted mineral deposits are recognized and incorporated in the modelling procedure. The method is demonstrated for outlining favorable areas for a Mississippi valley-type fluorite deposit in an area, north of Iran. The method is developed by studying and modelling the geological characteristics of known mineral occurrences. The degree of prediction ability of each exploration criterion is quantified as a recognition coefficient, which can be used as a weight attributed to the criterion in mineral exploration targeting to outline favorable areas.
