Probabilistic modeling of mineralized zones in Daralu copper deposit (SE Iran) using sequential indicator simulation

Abstract

Deterministic modeling of the geological domains is often restricted to the uncertainty assessment. Using stochastic modeling can be considered as an effective solution in order to overcome this restriction. It can also be effectively used for evaluation of ore bodies. Sequential indicator simulation as a stochastic modeling method is a widely used technique to characterize the categorical variables such as facies, rock types, alterations, and mineralized zones. Inverting the categorical variables to indicators proposes the global and local variability of the variable under study by descriptive and spatial statistics. In this study, this approach has been applied to a set of experimental data acquired from Daralu ore deposit located in southern part of the Urumieh-Dokhtar magmatic arc, south of Kerman province, SE Iran. Kerman province hosts several porphyry copper deposits in which calculation of probabilistic description of four normally presented mineralized zones (hypogene, supergene, oxide, and leached zones) for evaluation of relevant ore bodies would be advisable.