Deep learning for extracting micro-fracture: Pixel-level detection by convolutional neural network

Yejin Kim, Seong Jun Ha, Tae Sup Yun

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


Hydraulic stimulation has been a key technique in enhanced geothermal systems (EGS) and the recovery of unconventional hydrocarbon resources to artificially generate fractures in a rock formation. Previous experimental studies present that the pattern and aperture of generated fractures vary as the fracking pressure propagation. The recent development of three-dimensional X-ray computed tomography allows visualizing the fractures for further analysing the morphological features of fractures. However, the generated fracture consists of a few pixels (e.g., 1-3 pixels) so that the accurate and quantitative extract of micro-fracture is highly challenging. Also, the high-frequency noise around the fracture and the weak contrast across the fracture makes the application of conventional segmentation methods limited. In this study, we adopted an encoder-decoder network with a convolutional neural network (CNN) based on deep learning method for the fast and precise detection of micro-fractures. The conventional image processing methods fail to extract the continuous fractures and overestimate the fracture thickness and aperture values while the CNN-based approach successfully detects the barely seen fractures. The reconstruction of the 3D fracture surface and quantitative roughness analysis of fracture surfaces extracted by different methods enables comparison of sensitivity (or robustness) to noise between each method.

Original languageEnglish
Article number03007
JournalE3S Web of Conferences
Publication statusPublished - 2020 Nov 18
Event2nd International Conference on Energy Geotechnics, ICEGT 2020 - La Jolla, United States
Duration: 2020 Sept 202020 Sept 23

Bibliographical note

Publisher Copyright:
© The Authors, published by EDP Sciences, 2020.

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Energy(all)
  • Earth and Planetary Sciences(all)


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