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چکیده
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Mississippi Valley-type (MVT) Pb-Zn deposits are a subtype of sedimentary-hosted mineralization. These deposits are hosted by carbonate sequences in passive-margin tectonic settings. This paper uses the Fry technique and distance distribution analysis to model the spatial distribution pattern of MVT Pb-Zn deposits in the west of Semnan province (Iran) and their association with some geological features, aiming at mapping mineral prospectivity in the area. The modeling results reveal that NE–SW trending faults and Permian-Cretaceous dolomites and limestone are, respectively, major structural and lithological controlling factors of mineralization that operate as conduits and physicochemical subsystems of ore formation. The integration of the corresponding evidence maps of the controlling factors with a model of the geochemical signature of MVT Pb-Zn deposits through a supervised random forest approach, a machine learning technique, gains an exceptional prospectivity map predicting 100% of the known MVT Pb-Zn deposits in only 15% of the study area, which is an achievement. The recognized targets can be planned for further exploration.
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