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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-400', Anonymous Referee #1, 03 Jul 2022
    • AC1: 'Reply on RC1', Dongwon Lee, 08 Aug 2022
  • RC2: 'Comment on egusphere-2022-400', Anonymous Referee #2, 05 Jul 2022
    • AC2: 'Reply on RC2', Dongwon Lee, 08 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Dongwon Lee on behalf of the Authors (08 Aug 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (15 Aug 2022) by David Healy
ED: Publish as is (15 Aug 2022) by Federico Rossetti (Executive editor)
AR by Dongwon Lee on behalf of the Authors (16 Aug 2022)
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.