Articles | Volume 14, issue 2
https://doi.org/10.5194/gi-14-193-2025
https://doi.org/10.5194/gi-14-193-2025
Research article
 | 
02 Sep 2025
Research article |  | 02 Sep 2025

A free, open-source method for automated mapping of quantitative mineralogy from energy-dispersive X-ray spectroscopy scans of rock thin sections

Miles M. Reed, Ken L. Ferrier, William O. Nachlas, Bil Schneider, Chloé Arson, Tingting Xu, Xianda Shen, and Nicole West

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Cited articles

Aligholi, S., Lashkaripour, G. R., and Ghafoori, M: Estimating engineering properties of igneous rocks using semi-automatic petrographic analysis. Bull. Eng. Geol. Environ., 78, 2299–2314, https://doi.org/10.1007/s10064-018-1305-7, 2019. 
Behrens, R., Wirth, R., and von Blanckenburg, F: Rate limitations of nano-scale weathering front advance in the slow-eroding Sri Lankan Highlands, Geochim. Cosmochim. Acta, 311, 174–197, https://doi.org/10.1016/j.gca.2021.06.003, 2021. 
Berrezueta, E., Domínguez-Cuesta, M. J., and Rodríguez-Rey, Á: Semi-automated procedure of digitalization and study of rock thin section porosity applying optical image analysis tools, Comput. Geosci., 124, 14–26, https://doi.org/10.1016/j.cageo.2018.12.009, 2019. 
Bjørlykke, K: Relationships between depositional environments, burial history and rock properties. Some principal aspects of diagenetic process in sedimentary basins, Sediment. Geol., 301, 1–14, https://doi.org/10.1016/j.sedgeo.2013.12.002, 2014. 
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Short summary
We constructed an easy-to-use, open-source method for mapping minerals in rock thin sections. We implemented the method within the geographical information system QGIS and the Orfeo ToolBox plugin using random forest image classification on scanning electron microscope data. We applied the method to 14 rock thin sections. Mineral abundance estimates from our method compare favorably to previously published estimates, and 96 % spatially and categorically agree with manually derived mineral maps.
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