No abstract

No abstract

No abstract

No abstract

Stream sediment geochemical data are usually subjected to methods of multivariate analysis (e.g. factor analysis) in order to extract an anomalous geochemical signature (factor) of the mineral deposit-type sought. A map of anomalous geochemical signature can be used as evidence, in combination with other layers of evidence, for mineral prospectivity mapping (MPM). Because factor analysis may yield more than one factor in a stream sediment dataset, it raises the challenge of how to recognize the factor that best indicates presence of the mineral deposit-type sought. In addition, MPM is faced with the challenge of how to assign weights to classes in a geochemical evidence map. Accordingly, a new approach is discussed in this paper for the extraction of significant anomalous geochemical signature of the mineral deposit-type sought and for assigning weights to anomaly classes in a geochemical evidence map. In this approach, we used a staged factor analysis and then applied a logistic function to transform factor scores representing an anomalous geochemical signature in order to derive a map of geochemical mineralisation prospectivity indices (GMPI) as a spatial evidence layer for MPM based on the theory of fuzzy sets and fuzzy logic. The GMPI is a fuzzy weight in the [0,1] range. We demonstrate the application of the GMPI for mapping prospectivity for Mississippi valley-type fluorite deposits in the Mazandaran province, north of Iran, which is a greenfield area.