1887

Abstract

Summary

Interpretation of fractures in raw outcrop maps is a tedious and time-consuming task. A few semi-automatic or automatic interpretation methods based on image processing are available; however, they are usually sensitive to the contrast of the image that, in turn, causes under or over-interpretation of fracture geometry. A successful interpretation of fractures from a raw outcrop image requires two stages: (1) conversion of a multi-bit per pixel raw outcrop image to a binary map that preserves fracture geometry and connectivity, and (2) replacement of the binary fracture images with line segments or polylines. These two stages are fracture recognition and fracture detection, respectively. We apply the U-net architecture to recognize fractures in a raw outcrop map. When 200 training epochs are applied to our images, the training accuracy reaches 0.94, while the mean square error decreases to 0.02. The implementation of U-net yields good results for fracture recognition. We propose a pixel-based fracture detection algorithm. The algorithm can automatically interpret the fractures in the recognized binary map as line segments or polylines. By combining fracture recognition and detection, we can interpret automatically fractures in a complex raw outcrop map.

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/content/papers/10.3997/2214-4609.2020622013
2020-02-11
2024-06-25
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References

  1. Prabhakaran, R., Boersma, Q., Bezerra, F. and Bertotti, G.
    [2019] Fracture Network Patterns from the BrejÃţes Outcrop, IrecÃł Basin, Brazil. 4TU.Centre for Research Data.
    [Google Scholar]
  2. Ronneberger, O., Fischer, P. and Brox, T.
    [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation.
    [Google Scholar]
  3. Vasuki, Y., Holden, E.J., Kovesi, P. and Micklethwaite, S.
    [2014] Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach.Computers & Geosciences, 69, 22–32.
    [Google Scholar]
  4. Yi, S., Labate, D., Easley, G.R. and Krim, H.
    [2009] A shearlet approach to edge analysis and detection.IEEE Transactions on Image Processing, 18(5), 929–941.
    [Google Scholar]
  5. Zhu, W., Yalcin, B., Khirevich, S. and Patzek, T.
    [2019] Correlation Analysis of Fracture Intensity Descriptors with Different Dimensionality in a Geomechanics-constrained 3D Fracture Network. In: Petroleum Geostatistics 2019.
    [Google Scholar]
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