Solid Earth
Solid Earth
SE
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

Viewed

Total article views: 1,604 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,130 412 62 1,604 51 59 44
  • HTML: 1,130
  • PDF: 412
  • XML: 62
  • Total: 1,604
  • Supplement: 51
  • BibTeX: 59
  • EndNote: 44
Views and downloads (calculated since 14 Aug 2023)
Cumulative views and downloads (calculated since 14 Aug 2023)

Viewed (geographical distribution)

Total article views: 1,604 (including HTML, PDF, and XML) Thereof 1,560 with geography defined and 44 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2025
Download
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.
Read more
Share