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Application of Surface Topological Segmentation to Seismic Imaging

  • Timothée Faucon
  • Etienne Decencière
  • Cédric Magneron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4245)

Abstract

An original and efficient method to segment and label horizontal structures in 3D seismic images is presented. It is based on a morphological hierarchical segmentation. The initial extracted surfaces are post-processed using the topological segmentation method proposed by Malandain et al [1]. A last post-processing step allows to separate remaining multi-layered surfaces.

Keywords

Seismic Data Surface Point Mathematical Morphology Junction Point Grey Level Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Malandain, G., Bertrand, G., Ayache, N.: Topological segmentation of discrete surfaces. International Journal of Computer Vision 10(2), 183–197 (1993)CrossRefGoogle Scholar
  2. 2.
    Faucon, T., Decenciére, E., Magneron, C.: Morphological segmentation applied to 3D seismic data. In: Ronse, C., Najman, L., Decenciére, E. (eds.) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, pp. 475–484 (2005)Google Scholar
  3. 3.
    Taner, M.: Attributes revisited. Rock Solid Image Houston Texas (1992)Google Scholar
  4. 4.
    Bakker, P.: Image structure analysis for seismic interpretation. PhD thesis, Technische Universiteit Delft (2002)Google Scholar
  5. 5.
    Bouchereau, I.B.: Analyse d’images par transformée en ondelettes; application aux images sismiques. PhD thesis, Université Joseph Fourier Grenoble (1997)Google Scholar
  6. 6.
    Hale, D., Emmanuel, J.: Seismic interpretation using global image segmentation. In: 73th Annual International Meeting, Society of Exploration Geophysicists (2003)Google Scholar
  7. 7.
    Valet, L., Mauris, G., Bolon, P., Keskes, N.: Seismic image segmentation by fuzzy fusion of attributes. IEEE Transactions on Instrumentation and Measurement 50, 1014–1018 (2001)CrossRefGoogle Scholar
  8. 8.
    Moueddene, K.: Analyse d’images en sismique: pretraitement et extraction d’informations par la morphologie mathématique. PhD thesis, Université Paul Sabatier, Toulouse, France (1987)Google Scholar
  9. 9.
    N’Guyen, M.: Analyse multi-dimensionnelle et analyse par les ondelettes des signaux sismiques. PhD thesis, Institut National Polytechnique de Grenoble (2000)Google Scholar
  10. 10.
    Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: International workshop on image processing, real-time edge and motion detection (1979)Google Scholar
  11. 11.
    Meyer, F.: Minimal spanning forests for morphological segmentation. In: Serra, J., Soille, P. (eds.) Mathematical Morphology and its applications to signal processing (Proceedings ISMM 1994), Fontainebleau, France. Kluwer Academic Publishers, Dordrecht (1994)Google Scholar
  12. 12.
    Beucher, S.: Decenciére, E., Sandjivy, L., Magneron, C., Faucon, T.: Demande de brevet français no 05 03793 pour un procédé de détermination hiérarchique d’événements cohérents dans une image sismiqueGoogle Scholar
  13. 13.
    Kong, T., Rosenfeld, A.: Digital topology: Introduction and survey. Computer Vision, Graphics, And Image Processing 48(1), 357–393 (1989)CrossRefGoogle Scholar
  14. 14.
    Svensson, S., Nyström, I., di Baja, G.S.: Curve skeletonization of surface-like objects in 3d images guided by voxel classification. Pattern Recognition Letter 23, 1419–1426 (2002)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Timothée Faucon
    • 1
    • 2
  • Etienne Decencière
    • 1
  • Cédric Magneron
    • 2
  1. 1.Centre de Morphologie Mathémathique, Ecole des Mines de ParisFontainebleauFrance
  2. 2.Earth Resource Management Services (ERM.S)FontainebleauFrance

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