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Discussion papers
https://doi.org/10.5194/se-2019-104
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-2019-104
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Jun 2019

Research article | 19 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Solid Earth (SE).

An automated fracture trace detection technique using the complex shearlet transform

Rahul Prabhakaran1,2, Pierre-Olivier Bruna1, Giovanni Bertotti1, and David Smeulders2 Rahul Prabhakaran et al.
  • 1Department of Geoscience and Engineering, Delft University of Technology, Delft, the Netherlands
  • 2Department of Mechanical Engineering, Eindhoven University of Technology, the Netherlands

Abstract. Representing fractures explicitly using a discrete fracture network (DFN) approach is often necessary to model the complex physics that govern thermo-hydro-mechanical-chemical processes (THMC) in porous media. DFNs find applications in modelling geothermal heat recovery, hydrocarbon exploitation, and groundwater flow. It is advantageous to construct DFNs from photogrammetry of fractured outcrop analogues as the DFNs would capture realistic, fracture network properties. Recent advances in drone photogrammetry have greatly simplified the process of acquiring outcrop images, and there is a remarkable increase in the volume of image data that can be routinely generated. However, manually digitizing fracture traces is tedious and inevitably subject to interpreter bias. Additionally, variations in interpretation style can result in different fracture network geometries, which, may then influence modelling results depending on the use-case of the fracture study. In this paper, an automated fracture trace detection technique is introduced. The method consists of ridge detection using the complex shearlet transform coupled with post-processing algorithms that threshold, skeletonize, and vectorize fracture traces. The technique is applied to the task of automatic trace extraction at varying scales of rock discontinuities, ranging from 100–102 m. We present automatic trace extraction results from three different fractured outcrop settings. The results indicate that the automated approach enables extraction of fracture patterns at a volume beyond what is manually feasible. Comparative analysis of automatically extracted results with manual interpretations demonstrates that the method can eliminate the subjectivity that is typically associated with manual interpretation. The proposed method augments the process of characterizing rock fractures from outcrops.

Rahul Prabhakaran et al.
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Status: open (until 31 Jul 2019)
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Rahul Prabhakaran et al.
Data sets

Fracture Network Patterns from the Brejões Outcrop, Irecê Basin, Brazil R. Prabhakaran, Q. Boersma, F. H. R. Bezerra, and G. Bertotti https://doi.org/10.4121/uuid:67cde05c-9e99-4cc4-8cec-9f2666457d1f

Fracture Network Patterns from the Parmelan Anticline, France R. Prabhakaran, P.-O. Bruna, D. Smeulders, and G. Bertotti https://doi.org/10.4121/uuid:3f5e255f-edf7-441f-89f2-1adc7ac2f7d1

Model code and software

rahulprabhakaran/Automatic-Fracture-Detection-Code(supplement to Solid Earth Manuscript se-2019-104) R. Prabhakaran https://doi.org/10.5281/zenodo.3245452

Rahul Prabhakaran et al.
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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...
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