A predictive GIS model for potential mapping of copper, lead, and zinc in langping area, China

Abstract

Mineral resource potential mapping is a complex analytical process, which requires the consideration and the integration of a number of spatial evidences like geological, geomorphological, and wall rock alteration. The aim of this paper is to establish mineral exploration model for copper, lead, and zinc in Lanping basin area using the capability of analytical tools of Geographic Information System (GIS) and remote sensing data to generate maps showing favorable mineralized area. The geo-exploration dataset used for the research includes copper, lead, and zinc deposits, geological maps, topographic maps, structural maps, and ETM+ imagery. Geological features indicative of potential copper, lead, and zinc were extracted from the datasets input in the predictive model. The method of weights of evidence modeling is a probability-based technique for generating mineral potential maps using the spatial distribution of indicative features with respect to the known mineral occurrences. The method of weights of evidence probabilistic modeling provides a quantitative method for delineating areas with potential of copper, lead, and zinc mineral deposits in the Lanping Basin area. weights (W+, W-) and contrast (C=(W+)-(W-)) calculations guide the data-driven modeling. The four most important spatial features for exploration guide for copper, lead, and zinc mineralization hosted in the Lanping Basin area are alteration zones, faults, host rocks, and lineaments. The host rocks and deep faults have the strongest spatial association with the known copper, lead, and zinc deposits. The hydrothermal alteration zones have the moderate spatial association with the copper, lead, and zinc deposits. The predicted high-favorability zones do not show the strong affinity with lineaments. The distribution of 22 (copper, lead, and zinc) occurrences in the Lanping Basin was examined in terms of spatial association with various geological phenomena. The analysis of these relationships using GIS and weights of evidence modeling has predicted areas of high and moderate mineral potential, where a little or no mining activities exist. © Wuhan University and Springer Berlin Heidelberg 2009.