Preprints
https://doi.org/10.5194/egusphere-2024-1017
https://doi.org/10.5194/egusphere-2024-1017
09 Apr 2024
 | 09 Apr 2024
Status: this preprint is open for discussion.

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

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

Abstract. Quantitative mapping of minerals in rock thin sections delivers data on mineral abundance, size, and spatial arrangement that are useful for many geoscience and engineering disciplines. Although automated methods for mapping mineralogy exist, these are often expensive, associated with proprietary software, or require programming skills, which limits their usage. Here we present a free, open-source method for automated mineralogy mapping from energy dispersive spectroscopy (EDS) scans of rock thin sections. This method uses a random forest machine learning image classification algorithm within the QGIS geographic information system and Orfeo Toolbox, which are both free and open source. To demonstrate the utility of this method, we apply it to 14 rock thin sections from the well-studied Rio Blanco tonalite lithology of Puerto Rico. Measurements of mineral abundance inferred from our method compare favourably to previous measurements of mineral abundance inferred from X-ray diffraction and point counts on thin sections. The model-generated mineral maps agree with independent, manually-delineated mineral maps at a mean rate of 95 %, with accuracies as high as 96 % for the most abundant phase (plagioclase) and as low as 72 % for the least abundant phase (apatite) in these samples. We show that the default random forest hyperparameters in Orfeo Toolbox yielded high accuracy in the model-generated mineral maps, and we demonstrate how users can determine the sensitivity of the mineral maps to hyperparameter values and input features. These results show that this method can be used to generate accurate maps of major mineral phases in rock thin sections using entirely free and open-source applications.

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

Status: open (until 31 May 2024)

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  • RC1: 'Comment on egusphere-2024-1017', Anonymous Referee #1, 25 Apr 2024 reply
Miles Mark Reed, Ken L. Ferrier, William O. Nachlas, Bil Schneider, Chloé Arson, Tingting Xu, Xianda Shen, and Nicole West
Miles Mark Reed, Ken L. Ferrier, William O. Nachlas, Bil Schneider, Chloé Arson, Tingting Xu, Xianda Shen, and Nicole West

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Short summary
We constructed an easy-to-use, open-source method for mapping minerals in rock thin sections. This method is implemented with 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 agree 96 % in space and mineral type to manually derived mineral maps.