Open Access Repository

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

Downloads

Downloads per month over past year

Barker, RD, Barker, SLL ORCID: 0000-0002-4383-8861, Cracknell, MJ ORCID: 0000-0001-9843-8251, Stock, ED and Holmes, G 2020 , '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 , doi: 10.5382/econgeo.4804.

[img]
Preview
PDF (Online first)
143681 - Quanti...pdf | Download (7MB)

| Preview

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 Type: Article
Authors/Creators:Barker, RD and Barker, SLL and Cracknell, MJ and Stock, ED and Holmes, G
Journal or Publication Title: Economic Geology and the Bulletin of the Society of Economic Geologists
Publisher: Economic Geology Publ Co
ISSN: 0361-0128
DOI / ID Number: 10.5382/econgeo.4804
Copyright Information:

© 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/).

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP