Using computer-aided image processing to estimate chemical composition of igneous rocks: A potential tool for large-scale compositional mapping

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dc.citation.firstpage12en_US
dc.citation.issueNumber1en_US
dc.citation.journalTitleSolid Earth Sciencesen_US
dc.citation.lastpage26en_US
dc.citation.volumeNumber6en_US
dc.contributor.authorZhang, Julinen_US
dc.contributor.authorLee, Cin-Ty A.en_US
dc.contributor.authorFarner, Michaelen_US
dc.contributor.orgEarth, Environmental and Planetary Sciencesen_US
dc.date.accessioned2021-12-02T15:09:16Zen_US
dc.date.available2021-12-02T15:09:16Zen_US
dc.date.issued2021en_US
dc.description.abstractDigital cameras, particularly on smartphones, have led to the proliferation of amateur photographers. Of interest here is the use of smartphone cameras to conduct rapid, low-cost compositional mapping of geologic bedrock, such as plutons and batholiths, in combination with chemical analyses of rocks in the laboratory. This paper discusses some of the challenges in geochemical mapping using image analysis. We discuss methods for color calibration through a series of experiments under different light intensities and conditions (spectra). All indoor and outdoor experiments show good reproducibility, but suffer from biases imparted by different light intensities, light conditions, and camera exposure times. These biases can be greatly reduced with a linear color calibration method. Over-exposed and under-exposed images, however, cannot be fully calibrated, so we discuss methods that ensure images are properly exposed. We applied our method to 59 natural granitoid and mafic enclave samples of known chemical composition. Multivariate linear regression has been explored for relating calibrated rock images with chemical compositions. Among all the chromatic and texture features of rock images, we show that average gray levels strongly correlate with major oxide concentrations. Subtle variations in bulk composition can potentially be rapidly assessed using calibrated photographs of outcrops.en_US
dc.identifier.citationZhang, Julin, Lee, Cin-Ty A. and Farner, Michael. "Using computer-aided image processing to estimate chemical composition of igneous rocks: A potential tool for large-scale compositional mapping." <i>Solid Earth Sciences,</i> 6, no. 1 (2021) Elsevier: 12-26. https://doi.org/10.1016/j.sesci.2020.12.003.en_US
dc.identifier.digital1-s2-0-S2451912X20300623-mainen_US
dc.identifier.doihttps://doi.org/10.1016/j.sesci.2020.12.003en_US
dc.identifier.urihttps://hdl.handle.net/1911/111709en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.titleUsing computer-aided image processing to estimate chemical composition of igneous rocks: A potential tool for large-scale compositional mappingen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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