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Solid Earth
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Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/se-15-493-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-15-493-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
Research article
 | 
09 Apr 2024
Research article |  | 09 Apr 2024

Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions

Roberto Emanuele Rizzo, Damien Freitas, James Gilgannon, Sohan Seth, Ian B. Butler, Gina Elizabeth McGill, and Florian Fusseis

Supplement

https://doi.org/10.5194/se-15-493-2024-supplement

Data sets

Deep learning model Roberto Emanuele Rizzo https://doi.org/10.7488/ds/7493

Metamorphic fabrics can be formed by stress without significant strain - sample VA17 Florian Fusseis https://doi.org/10.16907/8ca0995b-d09b-46a7-945d-a996a70bf70b

Metamorphic fabrics can be formed by stress without significant strain - sample VA19 Florian Fusseis https://doi.org/10.16907/a97b5230-7a16-4fdf-92f6-1ed800e45e37

Model code and software

Scripts and data for recreating the figures Roberto Emanuele Rizzo https://doi.org/10.7488/ds/7493

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Short summary
Here we introduce a new approach for analysing time-resolved 3D X-ray images tracking mineral changes in rocks. Using deep learning, we accurately identify and quantify the evolution of mineral components during reactions. The method demonstrates high precision in quantifying a metamorphic reaction, enabling accurate calculation of mineral growth rates and porosity changes. This showcases artificial intelligence's potential to enhance our understanding of Earth science processes.
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