Abstract
Abstract Mineral distributions can be determined in drill core samples from a Carlin-type gold deposit, using micro-X-ray fluorescence (µXRF) raster data. Micro-XRF data were collected using a Bruker Tornado µXRF scanner on split drill core samples (~25 × 8 cm) with data collected at a spatial resolution of ~100 µm. Bruker AMICS software was used to identify mineral species from µXRF raster data, which revealed that many individual sample spots were mineral mixtures due to the fine-grained nature of the samples. In order to estimate the mineral abundances in each pixel, we used a linear programming (LP) approach on quantified µXRF data. Quantification of µXRF spectra was completed using a fundamental parameters (FP) standardless approach. Results of the FP method compared to standardized wavelength dispersive spectrometry (WDS)-XRF of the same samples showed that the FP method for quantification of µXRF spectra was precise (R2 values of 0.98–0.97) although the FP method gave a slight overestimate of Fe and K and an underestimate of Mg abundance. Accuracy of the quantified µXRF chemistry results was further improved by using the WDS-XRF data as a calibration correction before calculating mineralogy using LP. The LP mineral abundance predictions were compared to Rietveld refinement results using X-ray diffraction (XRD) patterns collected from powders of the same drill core samples. The root mean square error (RMSE) for LP-predicted mineralogy compared to quantitative XRD results ranges from 0.91 to 7.15% for quartz, potassium feldspar, pyrite, kaolinite, calcite, dolomite, and illite. The approaches outlined here demonstrates that µXRF maps can be used to determine mineralogy, mineral abundances, and mineralogical textures not visible with the naked eye from fine-grained sedimentary rocks associated with Carlin-type Au deposits. 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.
Highlights
Understanding the distribution of minerals within ore deposits is important to determine the mineralization and alteration paragenesis, recognize the distribution and type of ore minerals, and understand how mineralogy influences ore processing
In this study we show that μXRF can be used to accurately quantify elemental abundances via a fundamental parameters (FP) method by comparing FP results of rock samples with traditional whole-rock geochemistry analyses (WDS-XRF and 4-acid digest methods)
For the second method we show that μXRF spectra can be accurately quantified and used to predict and map the quantitative mineral abundance across samples using a linear programming (LP) approach
Summary
Understanding the distribution of minerals within ore deposits is important to determine the mineralization and alteration paragenesis, recognize the distribution and type of ore minerals, and understand how mineralogy influences ore processing (e.g., silicate mineral abundances; Johnson et al, 2019). Examples of applications of geochemical analyses using μXRF include studies of volcanogenic massive sulfides (Genna et al, 2011), shale-hosted uranium (Xu et al, 2015), greenstone-hosted Cu-Co-Au (Fox et al, 2019), and environmental sciences (Croudace and Rothwell, 2015; Flude et al, 2017) In each of these studies, μXRF is used to visualize and, in some cases, quantify (Flude et al, 2017) the distribution of elements over the surface of mineral and rock samples, which in turn reveals textures and patterns that cannot be observed in hand
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