Document Type : Case Study
Authors
1 Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
2 Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
3 Department of Mineral Resources, Geological Survey of Sweden, Uppsala, Sweden
Abstract
In this work, the concentration gradient (CG) analysis of local-scale exploration for Porphyry-Cu deposits is applied in two zones using the G(Vz) index (CG(Zn*Pb)/CG(Cu*Mo)). The first zone is covered by a 1:2000 map of the Sungun and Astamal areas in NW Iran and the second one in the Inza area in British Columbia, Canada. The rock samples are taken from Sungun and Astamal and the soil samples are taken from Inza. The Inza samples are analyzed for Cu, Pb, Zn, and Mo elements by the atomic absorption method, while the rock samples of Astamal and Sungun are analyzed for Cu, Pb, Zn, Mo, Ag, As, and Sb elements. The indices of gradient geochemical zonality (G(Vz)) of multi-elements around the mineral deposits and their spatial associations with particular geological, geochemical, and structural factors are the critical aspects that must be considered in mineral exploration. The values for the G(Vz) indices allow a distinction between the
sub-ore and supra-ore anomalies, which are associated with Zone Dispersed Mineralization (ZDM) and Blind Mineralization (BM), respectively. For a comparative identification of BM and ZDM, a supra-ore (Pb*Zn) anomaly, a sub-ore (Cu*Mo) anomaly, and Vz maps are used in place of the mining geochemistry representing the supra-ore gradient anomaly, sub-ore gradient anomaly and G(Vz) map. The G(Vz) model outperforms the Vz model. The introduced technique allows for a computational distinction between the BM and ZDM ore mineralizations without exploration drilling. Prior to writing this paper, the blind porphyry-Cu mineralization was intersected at depth through borehole exploration in a highly prospective zone delineated by the G(Vz) model. The results obtained confirm the usefulness of the G(Vz) modeling for local-scale targeting of blind mineral deposits.
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