Solid Earth
Solid Earth
SE
Articles | Volume 13, issue 9
Articles | Volume 13, issue 9
https://doi.org/10.5194/se-13-1475-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-13-1475-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 9
Research article
 | 
27 Sep 2022
Research article |  | 27 Sep 2022

Detecting micro fractures: a comprehensive comparison of conventional and machine-learning-based segmentation methods

Dongwon Lee, Nikolaos Karadimitriou, Matthias Ruf, and Holger Steeb

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,318 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,087 203 28 1,318 28 22
  • HTML: 1,087
  • PDF: 203
  • XML: 28
  • Total: 1,318
  • BibTeX: 28
  • EndNote: 22
Views and downloads (calculated since 01 Jun 2022)
Cumulative views and downloads (calculated since 01 Jun 2022)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,318 (including HTML, PDF, and XML) Thereof 1,178 with geography defined and 140 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Apr 2024
Download
Short summary
This research article focuses on filtering and segmentation methods employed in high-resolution µXRCT studies for crystalline rocks, bearing fractures, or fracture networks, of very small aperture. Specifically, we focus on the identification of artificially induced (via quenching) fractures in Carrara marble samples. Results from the same dataset from all five different methods adopted were produced and compared with each other in terms of their output quality and time efficiency.
Read more