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Machine Vision and Applications

, Volume 15, Issue 4, pp 216–219 | Cite as

Horizon picking in 3D seismic data volumes

  • Maria Faraklioti
  • Maria PetrouEmail author
Article

Abstract.

In this paper, we present an automatic horizon-picking algorithm, based on a surface detection technique, to detect horizons in 3D seismic data. The surface detection technique, and the use of 6-connectivity, allows us to detect fragments of horizons that are afterwards combined to form full horizons. The criteria of combining the fragments are similarity of orientation of the fragments, as expressed by their normal vectors, and proximity using 18-connectivity. The identified horizons are interrupted at faults, as required by the experts.

Keywords:

3D seismic data Horizon picking Surface detection 

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References

  1. 1.
    Bondar I (1992) Seismic horizon detection using image processing algorithms. Geophys Prospect 40:785-800Google Scholar
  2. 2.
    Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Proceedings of medical image computing and computer-assisted intervention - MICCAI’98, 1496:130-137Google Scholar
  3. 3.
    Keskes N, Boulanouar A, Faugeras O (1982) Application of image analysis techniques to seismic data. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 855-858Google Scholar
  4. 4.
    Keskes N, Zaccagnino P, Rether D, Mermey P (1983) Automatic extraction of 3-D seismic horizons. In: Society of Exploration Geophysicists (SEG), Annual Meeting Expanded Abstracts, pp 557-559Google Scholar
  5. 5.
    Lavest P, Chipot Y (1993) Building complex horizons for 3-D seismic. In: Society of Exploration Geophysicists (SEG), Annual Meeting Expanded Abstracts, pp 159-161Google Scholar
  6. 6.
    Lindeberg T (1998) Edge detection and ridge detection with automatic scale selection. Int J Comput Vision 30(2):77-116Google Scholar
  7. 7.
    Maroni C, Quinquis A, Vinson S (2001) Horizon picking on subbottom profiles using multiresolution analysis. Digital Signal Process 11:269-287CrossRefGoogle Scholar
  8. 8.
    Petrou M (1993) Optimal convolution filters and an algorithm for the detection of wide linear features. IEE Proc 140:331-339Google Scholar
  9. 9.
    Pieper S, Berlage T (2002) Enhancing volume visualisation of 3-D seismic data by exploitation of 3-D texture features. In: Proceedings of 64th European Association of Geoscientists and Engineers (EAGE) annual conference and exhibitionGoogle Scholar
  10. 10.
    Pitas I, Kotropoulos C (1992) A texture-based approach to the segmentation of seismic images. Pattern Recog 25:929-945CrossRefGoogle Scholar
  11. 11.
    Pitas I, Venetsanopoulos AN (1987) AGIS: An expert system for automated geophysical interpretation of seismic images. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 2256-2259Google Scholar
  12. 12.
    Roberto V, Peron A, Fumis PL (1989) Low-level processing techniques in geophysical image interpretation. Pattern Recog Lett 10:111-122CrossRefzbMATHGoogle Scholar
  13. 13.
    Sato Y, Nakajima S, Shiraga N (1998) 3D multi-scale line filter for segmentation and visualisation of curvilinear structures in medical images. Med Image Anal 2(2):143-168CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin/Heidelberg 2004

Authors and Affiliations

  1. 1.School of Electronics and Physical SciencesUniversity of SurreyGuildfordUK
  2. 2.The Institute of Telematics and InformaticsCERTHThermi, ThessalonikiGreece

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