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Mineral distributions can be determined in drill core samples from a Carlin-type gold deposit using micro-X-ray fluorescence raster data and long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine-learning approach to predict mineral species and abundances. Bruker AMICS software was used to identify mineral species from micro-XRF raster data, which revealed that many individual sample spots were mineral mixtures due to the fine-grained nature of the samples. To estimate the mineral abundances in each pixel, a linear programming (LP) approach was used on quantified micro-XRF data. Quantification of spectra was completed using a fundamental parameters (FP) standardless approach. Results of the FP method compared with standardised wavelength dispersive spectrometry (WDS)-XRF of the same samples showed that the FP method was precise (R2 values of 0.98–0.97) although giving a slight overestimate of Fe and K and an underestimate of Mg abundance. This approach is transferable to any ore deposit, but particularly useful in sedimentary-hosted ore deposits where ore and gangue minerals are often fine-grained and difficult to distinguish in hand specimen. In the second part of the study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analysed by LWIR and LP-derived mineral abundances from quantified micro-XRF data were used as training data to construct a series of Random Forest regression models. The models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared with quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. Using the method presented, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, imp, Mineral distributions can be determined in drill core samples from a Carlin-type gold deposit using micro-X-ray fluorescence raster data and long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine-learning approach to predict mineral species and abundances. Bruker AMICS software was used to identify mineral species from micro-XRF raster data, which revealed that many individual sample spots were mineral mixtures due to the fine-grained nature of the samples. To estimate the mineral abundances in each pixel, a linear programming (LP) approach was used on quantified micro-XRF data. Quantification of spectra was completed using a fundamental parameters (FP) standardless approach. Results of the FP method compared with standardised wavelength dispersive spectrometry (WDS)-XRF of the same samples showed that the FP method was precise (R2 values of 0.98–0.97) although giving a slight overestimate of Fe and K and an underestimate of Mg abundance. This approach is transferable to any ore deposit, but particularly useful in sedimentary-hosted ore deposits where ore and gangue minerals are often fine-grained and difficult to distinguish in hand specimen. In the second part of the study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analysed by LWIR and LP-derived mineral abundances from quantified micro-XRF data were used as training data to construct a series of Random Forest regression models. The models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared with quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. Using the method presented, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, imp

Research projects are described involving the use of QEMSCAN at the Advanced Mineralogy Research Center, Colorado School of Mines, USA. Geometallurgical applications include investigations into the relationships between mineralogy and geology and processing characteristics such as hardness and grinding properties. Quantitative mineralogy for kimberlite exploration and development and diamond petrogenesis shows the complex secondary silicate mineralogy in volcanic and mantle materials, and provides a means of viewing garnets from exploration samples. The distribution of arsenian pyrite in Carlin-type Au deposits can serve as a proxy for the distribution of Au. Monzonites from porphyry Cu deposits reveal pervasive potassic alteration and quartz veining, which may affect the behaviour of the materials during processing. A new view of feldspar zoning in granites has broad implications for understanding the petrogenetic evolution of magmatic systems and is of relevance in processing feldspar-bearing materials. Environmental and biological applications include soil mineralogy, the effect of soil chemistry on vegetation and studies of mammalian tissues., Research projects are described involving the use of QEMSCAN at the Advanced Mineralogy Research Center, Colorado School of Mines, USA. Geometallurgical applications include investigations into the relationships between mineralogy and geology and processing characteristics such as hardness and grinding properties. Quantitative mineralogy for kimberlite exploration and development and diamond petrogenesis shows the complex secondary silicate mineralogy in volcanic and mantle materials, and provides a means of viewing garnets from exploration samples. The distribution of arsenian pyrite in Carlin-type Au deposits can serve as a proxy for the distribution of Au. Monzonites from porphyry Cu deposits reveal pervasive potassic alteration and quartz veining, which may affect the behaviour of the materials during processing. A new view of feldspar zoning in granites has broad implications for understanding the petrogenetic evolution of magmatic systems and is of relevance in processing feldspar-bearing materials. Environmental and biological applications include soil mineralogy, the effect of soil chemistry on vegetation and studies of mammalian tissues.