An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets

Weighting and synthesizing exploration evidence criteria for mineral prospectivity mapping (MPM) are affected by complexity and ambiguity of ore mineralization processes. In this regard, fuzziness could facilitate the modeling of such vague processes for MPM. Furthermore, imprecise selection of the exploration criteria to be used in MPM has negative influence on the efficiency of the generated prospectivity models. In this paper, of various exploration criteria, a coherent set of exploration features were recognized by using the distance distribution analysis. Then, the application of cosine amplitude-based similarity procedure was adapted as a data-driven fuzzy logic approach for predictive mapping of porphyry-Cu prospectivity in Arasbaran metallogenic zone, NW Iran. In addition, a conventional data-driven fuzzy prospectivity model was generated for comparison purpose. Comparison of the two models demonstrated the superiority of the cosine amplitude-based fuzzy procedure for MPM.