01 Jun 2022
01 Jun 2022
Status: this preprint is open for discussion.

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

Dongwon Lee1, Nikolaos Karadimitriou1, Matthias Ruf1, and Holger Steeb1,2 Dongwon Lee et al.
  • 1Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart,70569, Germany
  • 2Stuttgart Center for Simulation Science, Pfaffenwaldring 5a, University of Stuttgart,70569, Germany

Abstract. Studying porous rocks with X-Ray Computed Tomography (XRCT) has been established as a standard procedure for the non-destructive characterization of flow and transport in porous media. Despite the recent advances in the field of XRCT, various challenges still remain due to the inherent noise and imaging artefacts in the produced data. These issues become even more profound when the objective is the identification of fractures, and/or fracture networks. One challenge is the limited contrast between the regions of interest and the neighboring areas, which can mostly be attributed to the minute aperture of the fractures. In order to overcome this challenge, it has been a common approach to apply various digital image processing steps, such as filtering, to enhance the signal-to-noise ratio. Additionally, segmentation methods based on threshold/morphology schemes have been employed to obtain enhanced information from the features of interest. However, this workflow needs a skillful operator to fine-tune its input parameters, and the required computation time significantly increases due to the complexity of the available methods, and the large volume of an XRCT data-set. In this study, based on a data-set produced by the successful visualization of a fracture network in Carrara marble with μXRCT, we present the results from five segmentation methods, three conventional and two machine learning-based ones. The objective is to provide the interested reader with a comprehensive comparison between existing approaches, while presenting the operating principles, advantages and limitations, to serve as a guide towards an individualized segmentation workflow. The segmentation results from all five methods are compared to each other in terms of quality and time efficiency. Due to memory limitations, and in order to accomplish a fair comparison, all the methods are employed in a 2D scheme. The output of the 2D U-net model, which is one of the adopted machine learning-based segmentation methods, shows the best performance regarding the quality of segmentation and the required processing time.

Dongwon Lee et al.

Status: open (until 13 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Dongwon Lee et al.

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

Dongwon Lee et al.


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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 data-set from all adopted five different methods were produced and compared with each other in terms of their output quality and time efficiency.