Sadhana

, Volume 16, Issue 3, pp 251–262 | Cite as

Syntactic pattern recognition based on spectrum of spectra

  • R Chhibber
  • K N Khattri
  • P S Moharir
Article
  • 20 Downloads

Abstract

From Fourier optics it follows that it is possible to obtain a Fourier transform of an amplitude distribution in a transparency readily. In intermediate planes an interpolation between the signal and its Fourier spectrum is available. Some pattern recognition schemes may work better in one of these intermediate domains than in the time or the frequency domain.

A syntactic pattern recognition scheme to achieve seismic discrimination among various hydrocarbon regimes was set up. It used autoregressive parameters as pattern primitives and defined an alphabet based on clusturing in the space of these primitives. Classification was conducted on the basis of nearest neighbour rule using Levenshtein distance between alphabetical strings. The pattern recognition scheme worked equally well in time and frequency domains for the unlabelled seismograms chosen and results for the intermediate domains were inferior. But the morphology of discrimination can be better studied with the help of Levenshtein triangles and as these triangles are very different for the different domains, the point that the interpolation between time and frequency domain is advantageous is definitely established.

Keywords

Syntactic pattern recognition seismic discrimination Fourier optics intermediate spectral domains pattern primitives cluster analysis 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akaike H 1969 Power spectrum estimation through autoregressive model fitting.Ann. Inst. Stat. Math. 21: 407–419MATHCrossRefMathSciNetGoogle Scholar
  2. Akaike H 1970 Statistical predictor identification.Ann. Inst. Stat. Math. 22: 203–217MATHCrossRefMathSciNetGoogle Scholar
  3. Akaike H 1972 Information theory and an extension of the maximum likelihood principle.Proc. 2nd Int. Symp. on Info. Theory Google Scholar
  4. Akaike H 1973 Maximum likelihood identification of Gaussian autoregressive moving average models.Biometrika 60: 255–265MATHCrossRefMathSciNetGoogle Scholar
  5. Akaike H 1974 A new look at statistical model identification.IEEE Trans. Autom. Control AC-19: 716–723CrossRefMathSciNetGoogle Scholar
  6. Bamler R, Glunder H 1983 The Wigner distribution function of two-dimensional signals coherent-optical generation and display.Opt. Acta 30: 1789–1803Google Scholar
  7. Bartelt H O, Brenner K-H, Lohmann W 1980 The Wigner distribution function and its optical production.Opt. Commun. 32: 32–38CrossRefGoogle Scholar
  8. Bois P 1980 Autoregressive pattern recognition applied to the delimitation of oil and gas reservoirs.Geophys. Prospect. 28: 572–591CrossRefGoogle Scholar
  9. Bois P 1982 Some comments on the applications of pattern recognition to oil and gas exploration.Geoexploration 20: 147–159CrossRefGoogle Scholar
  10. Chhibber R 1988Direct detection of subsurface hydrocarbons by syntactic pattern recognition of seismic signals, M Tech dissertation, Roorkee University, RoorkeeGoogle Scholar
  11. Cohen L 1989 Time-frequency distributions — a review.Proc. IEEE 77: 941–981CrossRefGoogle Scholar
  12. Cook C E, Bernfeld M 1967Radar signals: An introduction to theory and application (New York: Academic Press)Google Scholar
  13. Fu K S 1974Syntactic methods in pattern recognition (New York: Academic Press)MATHGoogle Scholar
  14. Fu K S, Liu H H 1983 An application of syntactic pattern recognition to seismic discrimination.IEEE Trans. Geosci. Electron. GE-21: 125–132Google Scholar
  15. Kapil S L, Moharir P S, Khattri K N 1985 BAR-ARMA predictive deconvolution of seismograms.J. Assoc. Explor. Geophys. 6: 1–6Google Scholar
  16. Gupta A K, Askura T 1986 New optical system for the efficient display of Wigner distribution functions using a single object transparency.Opt. Commun. 60: 265–268CrossRefGoogle Scholar
  17. Gutowski R R, Robinson E A, Trietel S 1978 Spectral estimation; fact or fiction.IEEE Trans. Geosci. Electron. GE-16: 80–84CrossRefGoogle Scholar
  18. Ishii N, Iwata A, Suzumura N 1978 Evaluation of an autoregressive process by information measure.Int. J. Syst. Sci. 9: 743–752MATHCrossRefMathSciNetGoogle Scholar
  19. Ishii N, Suzumura N 1977 Estimation of the order of autoregressive process.Int. J. Syst. Sci. 8: 905–914MATHCrossRefMathSciNetGoogle Scholar
  20. Khattri K N, Gaur V K, Mithal R, Tandon A K 1978 Seismogram synthesis in multilayered dissipative media.Geoexploration 16: 185–202CrossRefGoogle Scholar
  21. Khattri K N, Moharir P S 1989 Pattern recognition techniques for seismic detection of hydrocarbon deposits.Indian J. Geol. 61: 243–271Google Scholar
  22. Levenshtein 1966 Binary codes capable of correcting deletions, insertions and reversals,Sov. Phys. Dokl. (Engl. Transl.) 10: 707–710MathSciNetGoogle Scholar
  23. Moharir P S 1975 Normalized optical filtering.Indian J. Pure Appl. Phys. 13: 836–839Google Scholar
  24. Moharir P S 1988 Multilingual pattern recognition for geoexploration. InFrontiers in exploration geophysics (ed.) B B Bhattacharya (New Delhi: Oxford &IBH)Google Scholar
  25. Moharir P S 1990 Reciprocally conjunctive descriptions of signals. InSignal processing, communications and networking (eds) V U Reddy, A Paulraj (New Delhi: Tata McGraw-Hill)Google Scholar
  26. Nitzberg R 1979 Spectral estimation: an impossibility?Proc. IEEE 67: 437–438Google Scholar
  27. Ojeda-Castaneda J, Sicre E E 1984 Bilinear optical systems, Wigner distribution function and ambiguity function representations.Opt. Acta 31: 255–260MathSciNetGoogle Scholar
  28. Oldenburg D W, Scheuer T, Levy S 1983 Recovery of the acoustic impedance from reflection seismograms.Geophysics 48: 1318–1337CrossRefGoogle Scholar
  29. Pavlidis T 1977Structural pattern recognition (Berlin: Springer-Verlag)MATHGoogle Scholar
  30. Saleh B E A 1979 Optical bilinear transformations — general properties.Opt. Acta 26: 777–799MathSciNetGoogle Scholar
  31. Silvia M T and Robinson E A 1978 Use of kepstrum in signal analysis.Geoexploration 16: 55–73CrossRefGoogle Scholar
  32. Vakman D E 1968Sophisticated signals and uncertainty principle in radar (Berlin: Springer-Verlag)Google Scholar
  33. Van der Lugt A 1966 Operational notation for the analysis and systhesis of optical data-processing systems.Proc. IEEE 54: 1055–1063CrossRefGoogle Scholar
  34. Wigner E P 1932 On the quantum correction for thermodynamic equilibrium.Phys. Rev. 40: 749–759MATHCrossRefGoogle Scholar
  35. Wilpon J G, Rabiner L R 1985 A modified K-means clustering algorithm for use in isolated word recognition.Trans. IEEE Acoust., Speech Signal Process. ASSP-33: 587–594CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 1991

Authors and Affiliations

  • R Chhibber
    • 1
  • K N Khattri
    • 2
  • P S Moharir
    • 3
  1. 1.Schluberger (India)New DelhiIndia
  2. 2.Wadia Institute of Himalayan GeologyDehradunIndia
  3. 3.National Geophysical Research InstituteHyderabadIndia

Personalised recommendations