Computational Geosciences

, Volume 10, Issue 3, pp 265–277 | Cite as

Self-organizing maps for geoscientific data analysis: geological interpretation of multidimensional geophysical data

Article

Abstract

Data interpretation is a common task in geoscientific disciplines. Interpretation difficulties occur especially if the data that have to be interpreted are of arbitrary dimension. This paper describes the application of a statistical method, called self-organizing mapping (SOM), to interpret multidimensional, non-linear, and highly noised geophysical data for purposes of geological prediction. The underlying theory is explained, and the method is applied to a six-dimensional seismic data set. Results of SOM classifications can be represented as two-dimensional images, called feature maps. Feature maps illustrate the complexity and demonstrate interrelations between single features or clusters of the complete feature space. SOM images can be visually described and easily interpreted. The advantage is that the SOM method considers interdependencies between all geophysical features at each instance. An application example of an automated geological interpretation based on the geophysical data is shown.

Keywords

geological interpretation multidimensional geophysical data neural information processing self-organizing mapping 

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References

  1. 1.
    Klose, C.D.: Engineering geological rock mass characterisation of granitic gneisses based on seismic in-situ measurements, Scientific Technical Report, GeoForschungsZentrum, Potsdam (2004). STR 04/08, http://www.gfz-potsdam.de/bib/pub/str0408/0408.htm
  2. 2.
    Ji, S., Salisbury, M.: Shear-wave velocities, anisotropy and splitting in high-grade mylonites. Tectonophysics 221, 453–473 (1993)CrossRefGoogle Scholar
  3. 3.
    Garbin, H.D., Knopoff, L.: The compressional modulus of a material permeated by a random distribution of circular cracks. Q. Appl. Math. 30, 453–464 (1973)Google Scholar
  4. 4.
    Garbin, H.D., Knopoff, L.: The shear modulus of a material permeated by a random distribution of free circular cracks. Q. Appl. Math. 33, 296–300 (1975)Google Scholar
  5. 5.
    O'Connel, R.J., Budiansk, B.: Seismic velocities in dry and saturated cracked solids. J. Geophys. Res. 79(35), 5412–5426 (1974)CrossRefGoogle Scholar
  6. 6.
    Henyey, T.H., Pomphrey, R.J.: Self-consistent moduli of a cracked soil. Geophys. Res. Lett. 9, 903–906 (1982)CrossRefGoogle Scholar
  7. 7.
    Moos, D., Zoback, M.D.: In situ studies of velocity in fractured crystalline rocks. J. Geophys. Res. 88(B3), 2345–2358 (1983)CrossRefGoogle Scholar
  8. 8.
    Stierman, D.J.: Geophysical and geological evidence for fracturing, water circulation and chemical alteration in granitic rocks adjacent to major strike slip faults. J. Geophys. Res. 89(B7), 5849–5857 (1984)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Self-organizing formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Kohonen, T.: Self-Organizing Maps, 3rd edition. Springer, Berlin (2001)MATHGoogle Scholar
  11. 11.
    Bieniawski, Z.T.: Engineering Rock Mass Classification. Wiley, New York (1989)Google Scholar
  12. 12.
    Vesanto, J.: Data Exploration Process Based on Self-Organizing Map. PhD thesis, Helsinki University of Technology (2002) http://lib.tkk.fi/Diss/2002/isbn9512258978/
  13. 13.
    Silverman, B.W.: Density Estimation. London (1986)Google Scholar
  14. 14.
    LeCun, Y.: Effective Learning and Second-order Methods, A Tutorial at NIPS 93. Denver (1993)Google Scholar
  15. 15.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)MATHCrossRefGoogle Scholar
  16. 16.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons Inc., New York (1990)Google Scholar
  17. 17.
    Klose, C.D., Loew, S.: Engineering geological rock mass characterisation of granitic gneisses based on seismic in-situ measurements during tunnel excavations. In: Schubert, W. (ed.) Rock Engineering Theory and Practice, ISRM Regional Symposium EUROCK '04, pp. 120–126. VGE (2004)Google Scholar

Copyright information

© Springer Science + Business Media B.V. 2006

Authors and Affiliations

  1. 1.GeoForschungsZentrumPotsdamGermany
  2. 2.Lamont-Doherty Earth ObservatoryPalisadesUSA

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