Abstract
Native gold is a significant indicator mineral for Au-bearing deposits. The chemical composition of gold in diverse geological settings is a function of fluid composition under various physicochemical conditions. Native gold grains from various Au-bearing deposits and districts, including orogenic, volcanogenic massive sulfide (VMS), porphyry Cu-Au deposits, epithermal Au-Ag, and reduced intrusion-related gold (RIRG) deposits, were investigated by electron probe microanalyzer (EPMA) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to establish discriminant chemical signatures to apply indicator mineral surveys for exploration for gold-bearing deposits. Detailed textural observation of gold identifies compositional heterogeneities in gold grains from epithermal and VMS deposits. Partial least square-discriminant analysis (PLS-DA) identifies that Pd, Ag, Sb, Pb, Cu, Hg, and Te contents in gold grains have a high potential to discriminate gold from different deposit types. Comparison of gold compositional data from different deposit types using Random Forests classification models can be used as a predictive modeling tool to provide an accurate indication of gold-bearing deposit types.
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