Delineation of potential exploration targets based on 3D geological modeling: A case study from the Laoangou Pb-Zn-Ag polymetallic ore deposit, China

The Laoangou Pb-Zn-Ag polymetallic ore deposit (LAG) is a hydrothermal vein-type deposit in the East Qinling polymetallic metallogenic belt, which is controlled by NW-, NE- and nearly SN-trending faults and mainly hosted in the dolomite marble of Meiyaogou and Baishugou formations within the Luanchuan ore district (LOD). Although district-scale metallogenic prediction related to the Pb-Zn-Ag deposits in the LOD has been attempted, there have been no studies to formulate a detailed deposit scale metallogenic prediction of the Pb-Zn-Ag deposit in 3D space. Here we selected the LAG to formulate a metallogenic prediction model for Pb-Zn-Ag deposit based on data from 1:10,000 scale geological map, and 66 boreholes and 39 sections of exploration lines on 1:1000 scale, to extract the salient spatial features of the deposit. We apply ordinary weights and weighted weights of evidence, followed by boosted weights of evidence, logistic regression and information entropy for integrating the features of the ore deposit for exploration targeting. The C-V fractal method is applied to classify the probabilities. Accordingly, three levels of exploration targets are delineated: the first level targets are mainly distributed in the periphery of known orebodies (M2, M3, II-3 and VI), which are also located in the ore controlling strata and fault zones; the second level targets are located in the region of known orebodies, indirectly confirming that the prediction result is reliable; arid the third level targets, which are mainly located at the contact between ore-controlling strata and metagabbro bodies, offer new targets for exploration in this ore district. Combining our results obtained in this study with those in previous studies, we provide some guidelines for exploration targets in the Pb-Zn-Ag deposits in the LOD, which might help in more effectively delineating the target zones. (C) 2017 Elsevier B.V. All rights reserved.