Articles | Volume 10, issue 6
https://doi.org/10.5194/se-10-2137-2019
© Author(s) 2019. 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-10-2137-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2019
Research article |
| 20 Dec 2019
| 20 Dec 2019
An automated fracture trace detection technique using the complex shearlet transform
Rahul Prabhakaran
CORRESPONDING AUTHOR
Department of Geoscience and Engineering, Delft University of Technology, Delft, the Netherlands
Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
Pierre-Olivier Bruna
Department of Geoscience and Engineering, Delft University of Technology, Delft, the Netherlands
Giovanni Bertotti
Department of Geoscience and Engineering, Delft University of Technology, Delft, the Netherlands
David Smeulders
Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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Cited
20 citations as recorded by crossref.
- Best practices towards the digitization of 3D traces from virtual outcrop models S. Tavani et al. 10.1016/j.jsg.2024.105222
- An Advanced Discrete Fracture Methodology for Fast, Robust, and Accurate Simulation of Energy Production From Complex Fracture Networks S. de Hoop et al. 10.1029/2021WR030743
- Spatiotemporal variations in frost cracking measures in two dimensions: A case study for rock walls in Jotunheimen, southern Norway J. Czekirda et al. 10.1016/j.geomorph.2024.109112
- Scanline intersection similarity: A similarity metric for joint trace maps J. Kim et al. 10.1016/j.cageo.2023.105358
- Geomechanical perspectives and reviews on the development and evolution of cross-scale discontinuities in the Earth's crust: Patterns, mechanisms and models S. Li et al. 10.1016/j.jgsce.2024.205412
- Mapping the fracture network in the Lilstock pavement, Bristol Channel, UK: manual versus automatic C. Weismüller et al. 10.5194/se-11-1773-2020
- Workflow for capturing information and characterizing difficult-to-access geological outcrops using unmanned aerial vehicle-based digital photogrammetric data C. Villarreal et al. 10.1016/j.jii.2021.100292
- Adaptive Segmentation for Discontinuity Detection on Karstified Carbonate Outcrop Images From UAV-SfM Acquisition and Detection Bias Analysis A. Marques et al. 10.1109/ACCESS.2022.3151897
- Semi-universal geo-crack detection by machine learning Y. Shi et al. 10.3389/feart.2023.1073211
- Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics R. Prabhakaran et al. 10.5194/se-12-2159-2021
- Scale-dependent fracture networks S. Forstner & S. Laubach 10.1016/j.jsg.2022.104748
- Automated mapping of bedrock-fracture traces from UAV-acquired images using U-Net convolutional neural networks B. Chudasama et al. 10.1016/j.cageo.2023.105463
- Are open-source aerial images useful for fracture network characterisation? Insights from a multi-scale approach in the Zagros Mts. M. Mercuri et al. 10.1016/j.jsg.2023.104866
- Utilizing semantic-level computer vision for fracture trace characterization of hard rock pillars in underground space C. Li et al. 10.1016/j.gsf.2023.101769
- Interactive Fracture Segmentation Based on Optimum Connectivity Between Superpixels A. Marques Junior et al. 10.1109/LGRS.2022.3200311
- A Novel Gaussian-Copula modeling for image despeckling in the shearlet domain A. Morteza & M. Amirmazlaghani 10.1016/j.sigpro.2021.108340
- Large-scale natural fracture network patterns: Insights from automated mapping in the Lilstock (Bristol Channel) limestone outcrops R. Prabhakaran et al. 10.1016/j.jsg.2021.104405
- Using fractured outcrops to calculate permeability tensors: implications for geothermal fluid flow and the influence of seismic-scale faults R. Smith et al. 10.1017/S0016756822000309
- FracAbut: A python toolbox for computing fracture stratigraphy using interface impedance P. Namongo Soro et al. 10.1016/j.cageo.2024.105656
- Detailed investigation of multi-scale fracture networks in glacially abraded crystalline bedrock at Åland Islands, Finland N. Ovaskainen et al. 10.5194/se-14-603-2023
20 citations as recorded by crossref.
- Best practices towards the digitization of 3D traces from virtual outcrop models S. Tavani et al. 10.1016/j.jsg.2024.105222
- An Advanced Discrete Fracture Methodology for Fast, Robust, and Accurate Simulation of Energy Production From Complex Fracture Networks S. de Hoop et al. 10.1029/2021WR030743
- Spatiotemporal variations in frost cracking measures in two dimensions: A case study for rock walls in Jotunheimen, southern Norway J. Czekirda et al. 10.1016/j.geomorph.2024.109112
- Scanline intersection similarity: A similarity metric for joint trace maps J. Kim et al. 10.1016/j.cageo.2023.105358
- Geomechanical perspectives and reviews on the development and evolution of cross-scale discontinuities in the Earth's crust: Patterns, mechanisms and models S. Li et al. 10.1016/j.jgsce.2024.205412
- Mapping the fracture network in the Lilstock pavement, Bristol Channel, UK: manual versus automatic C. Weismüller et al. 10.5194/se-11-1773-2020
- Workflow for capturing information and characterizing difficult-to-access geological outcrops using unmanned aerial vehicle-based digital photogrammetric data C. Villarreal et al. 10.1016/j.jii.2021.100292
- Adaptive Segmentation for Discontinuity Detection on Karstified Carbonate Outcrop Images From UAV-SfM Acquisition and Detection Bias Analysis A. Marques et al. 10.1109/ACCESS.2022.3151897
- Semi-universal geo-crack detection by machine learning Y. Shi et al. 10.3389/feart.2023.1073211
- Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics R. Prabhakaran et al. 10.5194/se-12-2159-2021
- Scale-dependent fracture networks S. Forstner & S. Laubach 10.1016/j.jsg.2022.104748
- Automated mapping of bedrock-fracture traces from UAV-acquired images using U-Net convolutional neural networks B. Chudasama et al. 10.1016/j.cageo.2023.105463
- Are open-source aerial images useful for fracture network characterisation? Insights from a multi-scale approach in the Zagros Mts. M. Mercuri et al. 10.1016/j.jsg.2023.104866
- Utilizing semantic-level computer vision for fracture trace characterization of hard rock pillars in underground space C. Li et al. 10.1016/j.gsf.2023.101769
- Interactive Fracture Segmentation Based on Optimum Connectivity Between Superpixels A. Marques Junior et al. 10.1109/LGRS.2022.3200311
- A Novel Gaussian-Copula modeling for image despeckling in the shearlet domain A. Morteza & M. Amirmazlaghani 10.1016/j.sigpro.2021.108340
- Large-scale natural fracture network patterns: Insights from automated mapping in the Lilstock (Bristol Channel) limestone outcrops R. Prabhakaran et al. 10.1016/j.jsg.2021.104405
- Using fractured outcrops to calculate permeability tensors: implications for geothermal fluid flow and the influence of seismic-scale faults R. Smith et al. 10.1017/S0016756822000309
- FracAbut: A python toolbox for computing fracture stratigraphy using interface impedance P. Namongo Soro et al. 10.1016/j.cageo.2024.105656
- Detailed investigation of multi-scale fracture networks in glacially abraded crystalline bedrock at Åland Islands, Finland N. Ovaskainen et al. 10.5194/se-14-603-2023
Latest update: 01 Nov 2024
Short summary
This contribution describes a technique to automatically extract digitized fracture patterns from images of fractured rock. Digitizing fracture patterns, accurately and rapidly with minimal human intervention, is a desirable objective in fractured rock characterization. Our method can extract fractures at varying scales of rock discontinuities, and results are presented from three different outcrop settings. The method enables faster processing of copious amounts of fractured outcrop image data.
This contribution describes a technique to automatically extract digitized fracture patterns...
