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Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning

Citation

Barker, RD and Barker, SLL and Cracknell, MJ and Stock, ED and Holmes, G, Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning, Economic Geology and the Bulletin of the Society of Economic Geologists pp. 1-16. ISSN 0361-0128 (2020) [Refereed Article]


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Copyright Statement

2021 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.4804

Abstract

Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest 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 to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (7-15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, 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, improving the accuracy and speed of infrared data interpretation.

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)
UTAS Author:Cracknell, MJ (Dr Matthew Cracknell)
ID Code:143681
Year Published:2020
Web of Science® Times Cited:3
Deposited By:Earth Sciences
Deposited On:2021-03-30
Last Modified:2021-09-30
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