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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Sep 2022
Research article |
| 27 Sep 2022
| 27 Sep 2022
Detecting micro fractures: a comprehensive comparison of conventional and machine-learning-based segmentation methods
Dongwon Lee
CORRESPONDING AUTHOR
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
Nikolaos Karadimitriou
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
Matthias Ruf
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
Holger Steeb
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
SimTech, Stuttgart Center for Simulation Science, Pfaffenwaldring 5a, University of Stuttgart, 70569 Stuttgart, Germany
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Cited
19 citations as recorded by crossref.
- An integrated unsupervised–supervised learning framework for enhanced petrophysical prediction L. Qiao et al. https://doi.org/10.1063/5.0283683
- SEM-based analysis of pore uniformity: effects of pore positions and sizes E. Papia et al. https://doi.org/10.1016/j.mtcomm.2026.115342
- X-ray 3D imaging–based microunderstanding of granular mixtures: Stiffness enhancement by adding small fractions of soft particles K. Taghizadeh et al. https://doi.org/10.1073/pnas.2219999120
- Connectivity-aware three-dimensional fracture segmentation method for core computed tomography images X. Zhao et al. https://doi.org/10.1016/j.engappai.2026.113771
- Extraction of fractures in shale CT images using improved U-Net X. Wu et al. https://doi.org/10.1016/j.engeos.2023.100185
- From micro-pixels to macro-performance: An explainable machine learning framework for xanthan gum-stabilized expansive soils via AI-assisted trainable weka segmentation M. Hamza et al. https://doi.org/10.1016/j.jclepro.2026.147764
- Determining rock discontinuity fracture aperture and infilling characteristics using 3D X ray tomography with automated approach and multi method validation E. Mammoliti et al. https://doi.org/10.1038/s41598-025-21153-9
- Thermo-mechanical response of Sakesar limestone under cyclic steam injection: Insights from the Potwar Basin, Pakistan S. Shah https://doi.org/10.1016/j.pce.2026.104285
- Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media D. Lee et al. https://doi.org/10.1038/s41598-023-37523-0
- A hybrid deep learning framework to characterize 3D microfractures with high precision Y. Zhang & G. Ma https://doi.org/10.1016/j.ijrmms.2025.106317
- Evaluation of pore-fracture microstructure of gypsum rock fragments using micro-CT F. Košek et al. https://doi.org/10.1016/j.micron.2024.103633
- A hybrid unsupervised-to-supervised machine learning framework for fracture segmentation in natural gas hydrate-bearing sediments Z. Liu et al. https://doi.org/10.1016/j.enggeo.2026.108624
- Development of AI crack segmentation models for additive manufacturing T. Ledwaba et al. https://doi.org/10.1016/j.tmater.2025.100053
- Multiscale characterization of micro fracture connectivity and gas migration in volcanic reservoirs using µCT and hybrid learning segmentation J. Zhang et al. https://doi.org/10.1038/s41598-026-39657-3
- Super-resolution reconstruction of 3D digital rocks by deep neural networks S. You et al. https://doi.org/10.1016/j.geoen.2024.212781
- Lightweight prototype-fusion YOLACT++ for multi-scale fracture identification in Gonghe Basin hot-dry-rock reservoirs X. Jiang et al. https://doi.org/10.1016/j.geothermics.2026.103681
- Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions R. Rizzo et al. https://doi.org/10.5194/se-15-493-2024
- Digital rock modeling of deformed multi-scale media in deep hydrocarbon reservoirs based on in-situ stress-loading CT imaging and U-Net deep learning Y. Tian et al. https://doi.org/10.1016/j.marpetgeo.2024.107177
- Automated characterization of microfracture systems in organic-rich shales and their influence on porosity using convolutional neural networks on FIB-SEM images: A review B. Liu et al. https://doi.org/10.1016/j.earscirev.2026.105415
19 citations as recorded by crossref.
- An integrated unsupervised–supervised learning framework for enhanced petrophysical prediction L. Qiao et al. https://doi.org/10.1063/5.0283683
- SEM-based analysis of pore uniformity: effects of pore positions and sizes E. Papia et al. https://doi.org/10.1016/j.mtcomm.2026.115342
- X-ray 3D imaging–based microunderstanding of granular mixtures: Stiffness enhancement by adding small fractions of soft particles K. Taghizadeh et al. https://doi.org/10.1073/pnas.2219999120
- Connectivity-aware three-dimensional fracture segmentation method for core computed tomography images X. Zhao et al. https://doi.org/10.1016/j.engappai.2026.113771
- Extraction of fractures in shale CT images using improved U-Net X. Wu et al. https://doi.org/10.1016/j.engeos.2023.100185
- From micro-pixels to macro-performance: An explainable machine learning framework for xanthan gum-stabilized expansive soils via AI-assisted trainable weka segmentation M. Hamza et al. https://doi.org/10.1016/j.jclepro.2026.147764
- Determining rock discontinuity fracture aperture and infilling characteristics using 3D X ray tomography with automated approach and multi method validation E. Mammoliti et al. https://doi.org/10.1038/s41598-025-21153-9
- Thermo-mechanical response of Sakesar limestone under cyclic steam injection: Insights from the Potwar Basin, Pakistan S. Shah https://doi.org/10.1016/j.pce.2026.104285
- Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media D. Lee et al. https://doi.org/10.1038/s41598-023-37523-0
- A hybrid deep learning framework to characterize 3D microfractures with high precision Y. Zhang & G. Ma https://doi.org/10.1016/j.ijrmms.2025.106317
- Evaluation of pore-fracture microstructure of gypsum rock fragments using micro-CT F. Košek et al. https://doi.org/10.1016/j.micron.2024.103633
- A hybrid unsupervised-to-supervised machine learning framework for fracture segmentation in natural gas hydrate-bearing sediments Z. Liu et al. https://doi.org/10.1016/j.enggeo.2026.108624
- Development of AI crack segmentation models for additive manufacturing T. Ledwaba et al. https://doi.org/10.1016/j.tmater.2025.100053
- Multiscale characterization of micro fracture connectivity and gas migration in volcanic reservoirs using µCT and hybrid learning segmentation J. Zhang et al. https://doi.org/10.1038/s41598-026-39657-3
- Super-resolution reconstruction of 3D digital rocks by deep neural networks S. You et al. https://doi.org/10.1016/j.geoen.2024.212781
- Lightweight prototype-fusion YOLACT++ for multi-scale fracture identification in Gonghe Basin hot-dry-rock reservoirs X. Jiang et al. https://doi.org/10.1016/j.geothermics.2026.103681
- Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions R. Rizzo et al. https://doi.org/10.5194/se-15-493-2024
- Digital rock modeling of deformed multi-scale media in deep hydrocarbon reservoirs based on in-situ stress-loading CT imaging and U-Net deep learning Y. Tian et al. https://doi.org/10.1016/j.marpetgeo.2024.107177
- Automated characterization of microfracture systems in organic-rich shales and their influence on porosity using convolutional neural networks on FIB-SEM images: A review B. Liu et al. https://doi.org/10.1016/j.earscirev.2026.105415
Saved (final revised paper)
Latest update: 03 Jun 2026
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.
This research article focuses on filtering and segmentation methods employed in high-resolution...
