Mineral prospectivity mapping with weights of evidence and fuzzy logic methods

Knowledge-and data-driven approaches are two major methods used to integrate various evidential maps for mineral prospectivity mapping (MPM). Geological maps, geochemical samples and data from known gold deposits were collected in the western Junggar area, Xinjiang Province. The geological and a spatial database for geological and mineral occurrences were constructed for the studied region. A weights-of-evidence model and a fuzzy logic model were employed for MPM, and the results were compared. Results indicate that favorable sedimentary rocks, fault density, fault distance and concentration of Au were the primary factors affecting Au mineralization. Arsenic (AS), Stibium (Sb), fault direction, quartz veins and intrusive rocks were secondary factors affecting Au mineralization. Conditional independence exerted a major influence on the weights-of-evidence model. However, posterior probability would be very high if the conditional independence was disregarded, which impaired results. Combining the quantification results provided by weights-of-evidence and the fuzzy membership values determined by expert knowledge, the mineral prospectivity mapping according to the fuzzy logic method was proved to be valid. For the study area, which had a large number of deposits, data-driven approaches for MPM are generally considered to be appropriate. However, if sufficient data are not collected, the knowledge-driven approaches, for example, the fuzzy logic method used in the present study, usually achieves a better result.