Data-driven logistic function for weighting of geophysical evidence layers in mineral prospectivity mapping

Logistic function has been extremely executed to transform values of evidence layers into the range [0,1] to produce evidence maps assimilated or to predict mineralization zones as fuzzy prospectivity models. Many re-searchers have recently presented fuzzy layers transformed with continuous weights in range from [0, 1]. But thay did not try to consider efficiency or inefficiency of achieved results by overcoming exploratory bias. However, majority of integration methods comprising an inherent bias due to employing expert's judgments or applying trial-and-error procedure for determining some parameters of their functions. This research demon-strated, the application of data-driven fuzzy logic to assign continuous weights to geophysical evidence maps which can be integrated to generate prospectivity map with no biasness (with a 91% prediction ability). Results of the fuzzy transformation as continuous weights and discrete weights were compared and validated using prediction-area plot and locations of some mineralization clues.