An AHP–TOPSIS predictive model for district-scale mapping of porphyry Cu–Au potential: a case study from Salafchegan Area (Central Iran)

The Salafchegan area in central Iran is a greenfield region of high porphyry Cu–Au potential, for which a sound prospectivity model is required to guide mineral exploration. Satellite imagery, geological geochemical, geophysical, and mineral occurrence datasets of the area were used to run an innovative integration model for porphyry Cu–Au exploration. Five favorable multi-class evidence maps, representing diagnostic porphyry Cu–Au recognition criteria (intermediate igneous intrusive and sub-volcanic host rocks, structural controls, hydrothermal alterations, stream sediment Cu anomalies, magnetic signatures), were combined using analytic hierarchy process and technique for order preference by similarity to ideal solution to calculate a final map of porphyry Cu–Au potential in the Salafchegan area

Multivariate regression analysis of lithogeochemical data to model subsurface mineralization: a case study from the Sari Gunay epithermal gold deposit, NW Iran

In this contribution, multivariate regression was applied to surface channel rock and borehole geochemical data from the world-class Sari Gunay epithermal gold deposit, in northwest Iran, to model subsurface mineralization for further drilling. Multiple, factorial, polynomial and response surface regression models were applied to the geochemical data sets from a training mineralized area to evaluate the accuracy of these models using separate geochemical data from a test area. Geochemical data of 31 elements in surface channel rock samples were used as independent variables, and three parameters namely average grade, sum and productivity in individual 25 m by 25 m grid cells, obtained by kriging of borehole data, were used as dependent variables. All the multivariate regression models revealed high determination coefficients for three parameters, among which the response surface regression model yielded the highest values. The response surface regression yielded the best result, followed by the multiple regression, in modeling the geochemical data from the test area. Therefore, the result of the response surface regression was used to model subsurface gold mineralization at the Sari Gunay gold deposit in order to design additional drillings