Material fingerprinting as a tool to investigate between and within material type variability with a focus on material hardness

Abstract

Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been used to define Au-mineralised fingerprints. VNIR-SWIR spectral data were represented by four normalised wavelength regions and were clustered to form spectral classes. Sequentially, these spectral class proportions within a block and co-located pXRF data were clustered to discriminate material types (fingerprints). The hardness of each type was further explored using collocated BWi, Axb, Equotip rebound hardness and penetration rate datasets, but also by considering spatial contextual relationships and the within material type variability. The Tropicana orebody example gave a good illustration of how a phengitic-epidote K-feldspar rich domain (schistosity and softer, ∼15–18 kWh/t) separated from a harder (>20 kWh/t), shorter wavelength phengitic plagioclase-rich feldspar dominated domain. Exploring the within material type differences using the white mica composition (wAlOH) and a new w605 spectral feature demonstrated how the effects of shearing were captured within material types. Such findings will ultimately improve the understanding of the constitutive material hardness and have significance for process optimisation and blending strategy design.