Predicting Potential Mineralization Using Surface Geochemical Data and Multiple Linear Regression Model in the Kuh Panj Porphyry Cu Mineralization (Iran)

Abstract

Geochemical exploration is mainly utilized for prospecting of economically viable mineralization. Several methods have been introduced for identifying the potential mineralization based on surface geochemical data; however, based on these methods, the definite presence of mineralization in the region cannot be ascertained. Therefore, in this study, a combination of the core drilling analysis with the surface geochemical data was used in order to determine the actual position of the porphyry Cu mineralization. To achieve this objective, multivariate statistical method of multiple linear regression was applied. So, the regression equation is calculated based on the mean of Cu in sample of core drillings and elemental concentrations at surface geochemical rock samples. Predicting regression model has shown coefficient of determination (R 2 = 83 %). The model validity has also been checked through bootstrapping technique, which has demonstrated that the model is valid, with a 95 % confidence level as well. The result of multiple stepwise linear regression model showed that this method could draw the line of the best fit on the rock sample data in order to obtain the positions of mineralization underneath. The result obtained by this method was used to compare the distribution of Cu and Mo at surface samples with the boundary of economic zone in the core drillings, and the results are consistent and elongated northwest–southeast in diorite and quartz-diorite rock units.