Three-dimensional weights of evidence modelling of a deep-seated porphyry Cu deposit

Abstract

Given the challenges in data acquisition and spatial modelling at the detailed exploration stage, it is difficult to develop a prospectivity model, particularly for disseminated ore deposits. Recently, the weights of evidence (WofE) method has demonstrated a high efficiency for modelling such deposits. In this study, we propose a framework for creating a three-dimensional (3D) WofE-based prospectivity model of the Nochoun porphyry Cu deposit in SE Iran. The input data include qualitative geological and quantitative geochemical information obtained from boreholes and field observations. We combine ordinary and fuzzy weights of evidence for integrating qualitative and quantitative exploration criteria in a 3D space constrained by a metallogenic model of the study area for identifying a deep-seated ore body. Ordinary weights of evidence are determined for geological data, including lithology, alteration, rock type and structure. Moreover, we determine the fuzzy weight of evidence for each class of the continuous geochemical models created based on the factors analysis of Fe, Mo and Zn concentration values derived from boreholes. We integrate the input evidential models using WofE and create the posterior probability model. We also determine anomalous voxels in the probability model using a concentration–volume fractal model and validate them using a prediction–volume plot and test boreholes. The modelling results indicate the efficiency of the posterior probability model in identifying the anomalous voxels representing copper mineralized rock volumes. We provide open source software for the proposed framework which can be used for exploring deep-seated ore bodies in other regions.Supplementary data: Python scripts for implementing the proposed framework and supplementary files including more details on the evidential models are available at https://github.com/intelligent-exploration/3D_WofE.