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Quantitative mineral mapping of drill core surfaces I: a method for XRF mineral calculation and mapping of hydrothermally altered, fine-grained sedimentary rocks from a Carlin-type gold deposit

Citation

Barker, RD and Barker, SLL and Wilson, SA and Stock, ED, Quantitative mineral mapping of drill core surfaces I: a method for uXRF mineral calculation and mapping of hydrothermally altered, fine-grained sedimentary rocks from a Carlin-type gold deposit, Economic Geology and the Bulletin of the Society of Economic Geologists, 116, (4) pp. 803-819. ISSN 0361-0128 (2021) [Refereed Article]


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Copyright Statement

2021 Economic Geology. Gold Open Access: This paper is published under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license (https://creativecommons.org/licenses/by/3.0/)

DOI: doi:10.5382/econgeo.4803

Abstract

Mineral distributions can be determined in drill core samples from a Carlin-type gold deposit, using micro-X-ray fluorescence (XRF) raster data. Micro-XRF data were collected using a Bruker Tornado XRF scanner on split drill core samples (∼25 8cm) with data collected at a spatial resolution of ∼100 m. Bruker AMICS software was used to identify mineral species from XRF 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 XRF data. Quantification of XRF 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 XRF spectra was precise (R2 values of 0.980.97) although the FP method gave a slight overestimate of Fe and K and an underestimate of Mg abundance. Accuracy of the quantified XRF 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 XRF 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 transferrable 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.

Item Details

Item Type:Refereed Article
Research Division:Earth Sciences
Research Group:Geoinformatics
Research Field:Geoinformatics not elsewhere classified
Objective Division:Mineral Resources (Excl. Energy Resources)
Objective Group:Primary mining and extraction of minerals
Objective Field:Mining and extraction of precious (noble) metal ores
UTAS Author:Barker, SLL (Dr Shaun Barker)
ID Code:143705
Year Published:2021 (online first 2020)
Web of Science® Times Cited:3
Deposited By:CODES ARC
Deposited On:2021-03-30
Last Modified:2022-08-23
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