Skip to main content
SpringerLink
Log in

Knowledge-based image analysis for geophysical interpretation

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

Geophysical seismic interpretation is part of geophysical oil prospecting. It evaluates and analyses seismic reflection data, aiming at the detection of the position of hydrocarbon reservoirs. This paper provides a review of current efforts to automate, at least partially, seismic interpretation. As will be shown, this research area is very active and is a melting pot of various different approaches and techniques: artificial intelligence, pattern recognition, image processing, graphics, fuzzy set theory and, of course, geophysics and geology. Some methods of seismic pattern recognition (e.g. remote correlation, fuzzy seismic modeling, recognition of reservoir boundaries) and of seismic image processing (horizon following, texture analysis) are presented and some applications are shown. Expert systems used in geophysical interpretation (mainly in well log interpretation) are also briefly described. Finally, an automated system for knowledge-based image analysis for geophysical interpretation is dicussed. Its low-level vision techniques, its knowledge representation, and the control strategy for seismic pattern search are described.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Robinson, E.A. and Treitel, S.,Geophysical Signal Analysis, Prentice-Hall (1980).

  2. Dobrin, M.B.,Introduction to Geophysical Prospecting, McGraw-Hill (1976).

  3. Coffeen, J.A.,Seismic Exploration Fundamentals, PCC Books (1978).

  4. Waters, K.H.,Reflection Seismology, Wiley (1981).

  5. Sheriff, R.E. and Geldart, L.P.,Exploration Seismology, Vol. 2, Cambridge University Press (1983).

  6. Anstey, N.A.,Simple Seismics, International Human Resources Development Corp. (1982).

  7. Royce Nelson, H., Jr.,New technologies in Exploration Geophysics, gulf Publishing (1983).

  8. Tou, J.T. and Gonzales, R.C.,Pattern Recognition Principles, Addison-Wesley (19074).

  9. Ricker, N., The form and laws of propagation of seismic wavelets,Geophysics 18, 10–40 (1953).

    Google Scholar 

  10. Bois P and La Porte, M. Pointe automatique,Geophysical Prospecting 19, 592–611 (1971).

    Google Scholar 

  11. Bois, P., Détermination de l'impulsion sismique,Geophysical Prospecting 16, 4–20 (1968).

    Google Scholar 

  12. Bois, P., Correlation à distance,Geophysical Prospecting 19, 592–611 (1971).

    Google Scholar 

  13. Bois, P., Reconnaissance des horizons sismiques par analyse factorielle descriminante,Geophysical Prospecting 24, 696–718 (1976).

    Google Scholar 

  14. Bois, P., Autoregressive pattern recognition applied to the delimitation of oil and gas reservoirs,Geophysical Prospecting 28, 572–591 (1980).

    Google Scholar 

  15. Bois, P., Determination of the nature of reservoirs by use of pattern recognition algorithms with prior learning,Geophysical Prospecting 29, 687–701 (1981).

    Google Scholar 

  16. Bois, P., Some applications of pattern recognition to oil and gas exploration,IEEE Trans. Geosci. Remote Sensing GE-21(4), 416–426 (1983).

    Google Scholar 

  17. Bois, P., Fuzzy seismic interpretation,IEEE Trans. Geosci. Remote Sensing GE-22(6), 692–697 (1984).

    Google Scholar 

  18. Chen, C.H., Application of pattern recognition to seismic wave interpretation, inApplications of Pattern Recognition (ed. K.S. Fu), CRC Press (1982).

  19. Fu, K.S.,Syntactic Methods in Pattern Recognition, Academic Press (1974).

  20. Bezdek, J.C.,Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum (1981).

  21. Pratt, W.K.,Digital Image Processing, Wiley (1978).

  22. Ballard, D.H. and Brown, C.M.,Computer Vision, Prentice-Hall (1982).

  23. Keskes, N., Boulanmar, A. and Faugeras, O., Application of image analysis techniques to seismic data,Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Paris (1982).

  24. Keskes, N. and Mermey, P., Seismic horizon extraction by heuristic methods,Proc. IEEE Conf. Pattern Recognition (1983).

  25. Cooper, D.B., Elliot, H., Cohen, F.S. and Symosek, P.G., Stochastic boundary estimation and object recognition,Comp. Vision, Graphics and Image Processing, Apr. 1980, 326–355.

  26. Love, P.L. and Simaan, M., Seismic signal character recognition using texture analysis techniques,Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (1984).

  27. Barr, A., Cohen, P.R. and Feigenbaum, E.A.,The Handbook of Artificial Intelligence, Vols. I, II, III, Heuristech Press (1981).

  28. Duda, R.O., Development of the prospector consultation system for mineral exploration (final report), Artificial Intelligence Center, SRI Int'I (1978).

  29. Davis, R., Austin, H., Carlborm, I., Frawley, B., Pruchmik, P., Sneiderman, R. and Gilreath, J.A., The DIPMETER ADVISOR: Interpretation of geologic signals,Proc. Int. Joint Conf. Artificial Intelligence pp. 846–849 (1981).

  30. Smith, R. and Baker, J.D., The DIPMETER ADVISOR system: A case study in commercial expert system development,Proc. Int. Joint Conf. Artificial Intelligence, pp. 125–129 (1983).

  31. Bonnet, A. and Dahan, C., Oil-well data interpretation using expert system and pattern recognition technique,Proc. Int. Joint Conf. Artificial Intelligence, pp. 185–189 (1983).

  32. Simmons, R.G., Representing the reasoning about change in geologic interpretation, MSc thesis, MIT (1983).

  33. Barstow, D., A perspective on automatic programming,Al Magazine, Spring 1984, pp. 5–26.

  34. Ikonomopoulos, A. and Kunt, M., Directional filtering, zero crossing edge detection and image coding,Signal Processing II: Theories and Applications, (ed. H.W. Schuessler), Elsevier (1983).

  35. Pitas, I. and Venetsanopoulos, A.N., Nonlinear mean filters in image processing,IEEE Trans. Acoust. Speech Signal Process. ASSP-34(3), 573–584 (1986).

    Google Scholar 

  36. Levy-Mandel, A., Tsotsos, J.K. and Venetsanopoulos, A.N., Knowledge-based landmarking of cephalograms,Proc. 1985 Computer Assisted Radiology Conference, Belin, BRD, pp. 473–478, June 1986.

  37. Pitas, I and Venetsanopoulos, A.N., Edge detectors based on nonlinear filters,IEEE Trans. Pattern Anal. Machine Intell. PAMI-8(4) 538–550 (1986).

    Google Scholar 

  38. Faugeras, O.D., Pratt, W.K., Decorrelation methods of texture feature extraction,IEEE Trans. Pattern Anal. Machine Intell. PAMI-2(4), 323–332 (1980).

    Google Scholar 

  39. Haralick, R.M., Statistical and structural approach to texture,Proc. IEEE 67, 786–804 (1979).

    Google Scholar 

  40. Special Issue on Knowledge Representation,Computer, October 1983.

  41. Minsky, M., A framework for representing knowledge,Psychology of Computer Vision (ed. Winston) McGraw Hill (1975).

  42. Levesque, H. and Mylopoulos, J., A procedural semantics for semantic networks,Representation and Understanding: Studies in Cognitive Science, (eds D. Bodrow and A. Collins), Academic Press (1979).

  43. Tsotsos, I.K., Temporal event recognition: An application to left ventricular performance evaluation,Proc. Int. Joint Conf. Artificial Intelligence (1981).

  44. Mylopoulos, J., Shibahara, T. and Tsotsos, J.D., Building knowledge-based systems: The PSN experience,Computer 16, 83–89 (1983).

    Google Scholar 

  45. Shibahara, T.,et al., CAA: A knowledge-based system with casual knowledge to diagnose rhythm disorders in the heart,Proc. Canadian Soc. Computational Studies Intelligence Conf. (1982).

  46. McGalla, G. and Cercone, N., Approaches to knowledge representation,Computer 16, 12–18 (1983).

    Google Scholar 

  47. Brachman, R.J., What IS-A is and isn't: An analysis of taxonomic links in semantic networks,Computer 16, 30–36 (1983).

    Google Scholar 

  48. Tsotsos, J.K., Knowledge organization: Its role in representation, decision-making and explanation schemes for expert systems, Technical Report LCM-TR83-3, University of Toronto (1983).

  49. Tsotsos, J.K., Representational axes and temporal cooperative processes, inVision, Brain and Cooperative Computation, (eds M. Arbib and A. Hanson), MIT Press.

  50. Pitas, I. and Venetsanopoulos, A.N., Towards a knowledge-based system for automated geophysical interpretation of seismic data (AGIS),Signal Processing 13, 229–253 (1987).

    Google Scholar 

  51. Ikonomopoulos, A. and Unser, M., A directional approach to texture discrimination,Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, pp. 87–89 (1984).

  52. Ikonomopoulos, A. and Kunt, M., High compression of image coding via directional filtering,Signal Processing 8, 179–203 (1985).

    Google Scholar 

  53. Gopal, N., Emmoth, T. and Bovik, A.C., Channel interactions in visible pattern analysis,Proc. IEEE Int., Conf. Acoustics, Speech and Signal Processing, Glasgow, pp. 1643–1646 (1989).

  54. Pitas, I., Milios, E., and Venetsanopoulos, A.N., A minimum entropy approach to rule learning from examples,IEEE Trans. Systems Man Cybernet. (in press).

  55. Pitas, I and Kotropoulos, K., Texture description rules for geophysical image segmentation,Signal Processing V: Theories and Applications, (eds L. Toreset al.), Elsevier, pp. 597–600 (1990).

  56. Pitas, I. and Kotropoulos, K., Texture analysis and segmentation of seismic images,Proc. IEEE Int., Conf. Acoustics, Speech and Signal Processing, Glasgow, pp. 1437–1440 (1989).

  57. Pitas, I. and Venetsanopoulos, A.N.,Nonlinear Digital Filters: Principles and Applications, Kluwer Academic (1990).

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Thessaloniki, 54006, Thessaloniki, Greece

    I. Pitas

  2. Department of Electrical Engineering, University of Toronto, M5S 1A4, Toronto, Canada

    A. N. Venetsanopoulos

Authors
  1. I. Pitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. N. Venetsanopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pitas, I., Venetsanopoulos, A.N. Knowledge-based image analysis for geophysical interpretation. J Intell Robot Syst 7, 115–137 (1993). https://doi.org/10.1007/BF01257815

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01257815

Key words

Search

Navigation