This paper addresses the application of integrated chargeability and resistivity method and grade data in modeling and evaluation of copper deposits. We argue that the relationship between IP, Rs and grade data may be used for modeling and reserve estimation and tested this argument for Sarbisheh copper deposit that is located in eastern Iran. Geology and mineralization situation of Sarbisheh deposit was reviewed. Then geophysical survey design was carried out based on the borehole exploration data and other parameters such as geological and topographical factors. Five profiles were designed and surveyed using dipole-dipole array. The obtained data was processed and 2D sections of IP and Rs were prepared for each profile by inverting the data using the Res2dinv software. Based on the geostatistical methods, a 3D block model for IP and Rs data was constructed using Datamine Studio software and this model was evaluated by some exploratory boreholes in the study area. The relationship between IP and Rs and copper grade has been calculated based on statistical and neural network methods. In the cases that borehole data was unavailable, Cu grade was estimated using regression and multivariate regression analysis. Moreover, Cu grade was predicted by neural network at unrecognized points. Then Cu grade was calculated for each block identified by IP 3D model. Finally, a 3D block model of this copper deposit was constructed. According to the drilling tests, there is a good correlation between 3D block model and real Cu grade modeling.
