Presenting a Model for Distinguishing Between True and False Anomalies Using Data Classification

Data integration can be used to conduct exploratory studies on a regional scale simultaneously on all available data from the study area. The results obtained by considering all the data and the relationship between them are more accurate and reliable. In these cases, mineral potential modeling is utilized to determine promising areas. Although GIS-based mineral prospectivity mapping methods have been established, it is important to review which methods of geochemical data analysis result in anomaly maps that, in turn, lead to better models of mineral prospectivity. In this study, instead of using anomalies of pathfinder elements, using geochemical zonality anomalies as one of the several evidential maps resulted in the improved mapping of mineral prospectivity. In addition, whereas weights-of-evidence analysis was used in this study, other methods of data representation and integration for mineral prospectivity mapping can be used. In this study, a part of Arasbaran metallogenic zone was selected and one-element geochemical control maps, geochemical maps produced by zonality, structural, alteration and geological maps were weighted and produced using the position of known indices by the method of weights-of-evidence. In the next step, weighted layers were combined with logistic regression (LR) method to prepare mineral potential models.