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

Data sets

micro-XRCT data set of Carrara marble with artificially created crack network: fast cooling down from 600°C Matthias Ruf;Holger Steeb

Model code and software

Fracture network segmentation Dongwon Lee;Nikolaos Karadimitriou;Holger Steeb

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.