Search

Magnetite is an important ore and gangue mineral in many economic deposits, and the ability to model the modal magnetite abundance from cost-effective data, such as hyperspectral reflectance spectra, has widespread applications. However, magnetite reflectance spectra collected with field and drill core sensors, which are increasingly used by the mineral resources industry, are characterised by very broad and poorly defined diagnostic features. Magnetite content could be modelled indirectly from mixed mineral spectra, but these efforts have been limited by the lack of quantified training datasets of mixed mineral assemblages. We created two hyperspectral libraries (n=104) of magnetite mixed with quartz, chlorite, and siderite collected from two different wavelength ranges to address this knowledge gap. Mineral ratios and particle size variation, which are important when modelling hyperspectral data, were determined using quantitative X-ray diffraction (QXRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) analysis. Hyperspectral data were acquired across the visible-near, shortwave, mid to thermal infrared (VNIR-SWIR-MIR-TIR, 380-16669 nm) wavelength range, using HyLogger-3 and Bruker Vertex 80v Fourier transform infrared (FTIR) instruments. Predictive modelling was carried out using CSIRO's The Spectral Geologist (TSG) software partial least squares (PLS) modelling tool, which allows for modelling of one variable (e.g., magnetite wt%) from another (e.g., reflectance spectra) via calibration using a training dataset (magnetite mixture spectral library), and subsequent model validation using other hyperspectral data (e.g., from drillhole) of a similar wavelength range. Use of magnetite mixture PLS calibration enabled prediction of magnetite wt% from drill core VNIR-SWIR data that that matches the Fe2O3 assay and magnetic susceptibility detected from the core. We also noted several mineral diagnostic features in the MIR wavelength region, which can provide lower detection limits and an improvement in the accuracy of predictive modelling for magnetite and gangue minerals in the future., Open-Access Online Publication: May 31, 2023

CSIRO Mineral Resources evaluates geochemical and optical sensing technologies for cost-effective exploration and ore body characterisation of a wide range of critical metals deposits, such as pegmatite-hosted Li-deposits. This paper presents varieties of ore and gangue mineral assemblages of five drill cores from the Greenbushes, King Col and Londonderry LCT pegmatites, located in the Yilgarn and Pilbara Cratons of Western Australia, respectively. The mineralogical drill core data were collected by the Geological Survey of Western Australia (GSWA) using a hyperspectral drill core scanner, HyLogger3, which is part of AuScope's National Virtual Core Library research infrastructure program. The HyLogger3 data are publicly available through the AuScope portal (http://portal.auscope.org.au/) and GSWA's GeoView portal (GeoVIEW.WA). The thermal infrared hyperspectral data collected by HyLogger3 allow cost-effective mapping of Li-host minerals, such as spodumene, petalite, holmquistite and amblygonite. Spodumene represents the main Li-host at Greenbushes, whereas considerable amounts of petalite can be found at King Col in addition to spodumene. Holmquistite and amblygonite are minor Li-host minerals. A range of spectral signatures of spodumene and petalite can be observed in both mineral deposits and are potentially due to variations of chemical composition of the Li-host minerals, including Fe:Al and Li:Na ratios. Feldspars are common gangue minerals in both deposits and show different Na:Ca ratios down hole, indicating zoning of the ore body. The HyLogger3 data were used to identify 101 samples from Greenbushes drill core C3DD024 for FTIR and geochemical analysis, which allowed calibration of the HyLogger3 data and Partial Least Squares-modelling based prediction of Li, Na and other key geochemical elements across the five drill cores. Our case study demonstrates the potential for advanced but cost-effective Li-resource characterisation by means of reflectance spectroscopy, helping to assess the economic value of different ore bodies quickly., Open-Access Online Publication: May 29, 2023