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dc.contributor.authorBarker, Rocky D.en_NZ
dc.contributor.authorBarker, Shaun L.L.en_NZ
dc.contributor.authorCracknell, Matthew J.en_NZ
dc.contributor.authorStock, Elizabeth D.en_NZ
dc.contributor.authorHolmes, Geoffreyen_NZ
dc.date.accessioned2021-02-17T01:44:38Z
dc.date.available2021-02-17T01:44:38Z
dc.date.issued2020en_NZ
dc.identifier.citationRocky D. Barker, Shaun L.L. Barker, Matthew J. Cracknell, Elizabeth D. Stock, Geoffrey Holmes; Quantitative Mineral Mapping of Drill Core Surfaces II: Long-Wave Infrared Mineral Characterization Using μXRF and Machine Learning. Economic Geology 2021; 116 (4): 821–836. doi: https://doi.org/10.5382/econgeo.4804en
dc.identifier.issn0361-0128en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/14118
dc.description.abstractLong-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.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherSociety of Economic Geologistsen_NZ
dc.rights© 2021 Gold Open Access: This paper is published under the terms of the CC-BY-NC license.
dc.subjectcomputer scienceen_NZ
dc.titleQuantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learningen_NZ
dc.typeJournal Article
dc.identifier.doi10.5382/econgeo.4804en_NZ
dc.relation.isPartOfEconomic Geologyen_NZ
pubs.elements-id259591
pubs.publication-statusPublished onlineen_NZ
pubs.volumeOnline Firsten_NZ
dc.identifier.eissn1554-0774en_NZ


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