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

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

doi:https://doi.org/10.5194/gi-14-193-2025

Articles | Volume 14, issue 2

https://doi.org/10.5194/gi-14-193-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gi-14-193-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 2

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

Supplement

https://doi.org/10.5194/gi-14-193-2025-supplement

Data sets

Supplement to "A free, open-source method for automated mapping of quantitative mineralogy from energy-dispersive X-ray spectroscopy scans of rock thin sections" submitted to Geoscientific Instrumentation, Methods, and Data Systems Reed, M. M., Ferrier, K. L., Nachlas, W. O., Schneider, B., Arson, C., Xu, T., Shen, X., West, N https://doi.org/10.5281/zenodo.10912628

Model code and software

Supplement to "A free, open-source method for automated mapping of quantitative mineralogy from energy-dispersive X-ray spectroscopy scans of rock thin sections" submitted to Geoscientific Instrumentation, Methods, and Data Systems Reed, M. M., Ferrier, K. L., Nachlas, W. O., Schneider, B., Arson, C., Xu, T., Shen, X., West, N https://doi.org/10.5281/zenodo.10912628

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.