Automated Structural Interpretation Through Classification of Seismic Horizons

  • Hilde G. Borgos
  • Thorleif Skov
  • Lars Sønneland
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 7)


A novel method for extracting geometry primitives from seismic data is presented. All events in the 3D seismic cube will be detected and can be combined into geometric primitives based on similarities in the local wave form. No assumptions of continuity in the geometric primitives are required. The geometric primitives can therefore represent faulted horizons, which furthermore facilitates quantification of the fault displacement.

The accuracy with which the local waveform can be represented is defined as a user input, implying that subtle lateral changes in the reflectivity can be detected and exploited. This characteristic enables analysis of stratigraphic variations along horizons.


Fault Plane Attribute Vector Vertical Cross Section Fault Surface Seismic Attribute 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    H.G. Borgos, T. Skov, T. Randen, and L. Sønneland (2003) Automated geometry extraction from 3D seismic data. Expanded Abstr., Int. Mtg., Soc. Exploration Geophys., 1541–1544.Google Scholar
  2. 2.
    G. Celeux and G. Govaert (1995) Gaussian parsimonious clustering models. Pattern Recognition 28(5), 781–793.CrossRefGoogle Scholar
  3. 3.
    C. Fraley (1998) Algorithms for model-based Gaussian hierarchical clustering. SIAM Journal of Scientific Computing 20, 270–281.zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    C. Fraley and A.E. Raftery (2002) Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97(458), 611–631.MathSciNetCrossRefGoogle Scholar
  5. 5.
    R.A. Johnson and D.W. Wichern (1992) AppliedMultivariate Statistical Analysis. Prentice Hall International, Inc., 3rd edition.Google Scholar
  6. 6.
    S.I. Pedersen, T. Skov, T. Randen, and L. Sønneland (2004) Automatic fault extraction using artificial ants. This volume.Google Scholar
  7. 7.
    T. Randen, S.I. Pedersen, and L. Sønneland (2001) Automatic extraction of fault surfaces from three-dimensional seismic data. Expanded Abstr., Int. Mtg., Soc. Exploration Geophys., 551–554.Google Scholar
  8. 8.
    L. Sønneland, P. Tennebo, T. Gehrmann, and Ø. Yrke (1994) 3D model-based Bayesian classification. Extended Abstr., Eur. Assoc. Expl. Geophys.Google Scholar
  9. 9.
    L. Sønneland, P. Tennebø, T. Gehrmann, Ø. Yrke, K. Boge, and G. Berge (1994) Volume reflection spectral analysis. Technical report, Schlumberger, Research & Development, July 1994, 1–17.Google Scholar
  10. 10.
    L. Sønneland, P. Tennebø, T. Gehrmann, Ø. Yrke, K. Boge and G. Berge (1998) Orthogonal polynomial spectral decomposition. PCT Patent No. WO/9837437.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hilde G. Borgos
    • 1
  • Thorleif Skov
    • 1
  • Lars Sønneland
    • 1
  1. 1.Schlumberger Stavanger ResearchStavangerNorway

Personalised recommendations