Skip to main content
  • 144 Accesses

Abstract

Decision-theoretic and syntactic pattern recognition techniques are employed to detect the physical anomalies (bright spots) and to recognize the structural seismic patterns in two-dimensional seismograms. Here, decision-theoretic methods include Bayes classification, linear and quadratic classifications, tree classification, partitioning-method and tree classification, and sequential classification. The generated features are envelope, instantaneous frequency, polarity, moments, and contrasts from cooccurrence matrix. A hierarchical system is proposed for seismic syntactic pattern recognition. Syntactic methods include error-correcting finite state automaton, picture description language (PDL), and tree automaton. Experiments using simulated and real seismograms are presented. The recognition results are quite encouraging.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Aminzadeh, F., Ed., 1987, Handbook of Geophysical Exploration: Section I. Seismic Exploration, 20, Pattern Recognition & Image Processing, Geophysical Press, London.

    Google Scholar 

  • Bois, P., 1980, Autoregressive pattern recognition applied to the delimitation of oil and gas reservoirs: Geophys. Prosp. 28, 572–591.

    Google Scholar 

  • Bois, P., 1983, Some application of pattern recognition to oil and gas exploration: Inst. Electr. Electron. Eng., Trans. Geosci. Remote Sensing, GE-21, 416–426.

    Article  Google Scholar 

  • Chen, C.H., Ed., 1985, Pattern Recognition, 18, Perga-mon Press, Oxford.

    Google Scholar 

  • deFigueiredo, R.J.P., 1982, Pattern recognition approach to exploration: in Concepts and Techniques in Oil and Gas Exploration, K.C. Jain, and R.J.P. deFigueiredo, Soc. Expl. Geophys.

    Google Scholar 

  • Dobrin, M.B., 1976, Introduction to Geophysical Prospecting, 3rd ed., Chapter 10. McGraw-Hill, New York.

    Google Scholar 

  • Don, Hon-Son, Fu, King-sun, Liu, C.R., and Lin, Wei-Chung, 1984, Metal surface inspection using image processing techniques: Inst. Electr. Electron. Eng. Trans., System, Man, Cybernet. SMC-14, 139–146.

    Google Scholar 

  • Farnback, J.S., 1975, The complex envelope in seismic signal analysis: Bull. Seismol. Soc. Am. 65 951–962.

    Google Scholar 

  • Fu, K.S., 1968, Sequential Methods in Pattern Recognition and Machine Learning- Academic Press, New York.

    Google Scholar 

  • Fu, K.S., 1982, Syntactic Pattern Recognition and Applications: Prentice-Hall, Englewood Cliffs, NJ.

    Google Scholar 

  • Hagen, D.C., 1981, The application of principal components analysis to seismic data sets: Proc. 2nd Int. Symp. Comput. Aided Seismic Anal. Discrimination, North Dartmouth, 98–109.

    Google Scholar 

  • Hough, P.V.C., 1962, Method and means for recognizing complex patterns: U.S. Paten 3,069,654.

    Google Scholar 

  • Huang, K.Y., 1990, Branch and bound search for automatic linking process of seismic horizons: pattern recognition, 23 657–667.

    Google Scholar 

  • Huang, K.-Y., McGillem, C.D., and Anuta, P.E., 1981, Detection of bright spots using pattern recognition techniques: presented at the 51st Annu. Int. Mtg., Soc. Expl. Geophys.

    Google Scholar 

  • Huang, K.Y., and Fu, K.S., 1982, Decision-theoretic pattern recognition for the classification of Ricker wavelets and the detection of bright spots: presented at the 52nd Annu. Int. Mtg., Soc. Expl. Geophys.

    Google Scholar 

  • Huang, K.Y., and Fu, K.S., 1983, Detection of bright spots in seismic signal using pattern recognition techniques: TR-EE83–35, Purdue Univ.

    Google Scholar 

  • Huang, K.Y., and Fu, K.S., 1984, Detection of bright spots in seismic signal using tree classifiers: Geoexploration 23, 121–145.

    Google Scholar 

  • Huang, K.Y., and Fu, K.S., 1985a, Syntactic pattern recognition for the classification of Ricker wavelets: Geophysics 50, 1548–1555.

    Google Scholar 

  • Huang, K.Y., and Fu, K.S., 1985b, Syntactic pattern recognition for the recognition of bright spots: Pattern Recognition 18, 421–428.

    Google Scholar 

  • Huang, K.Y., and Fu, K.S., 1987a, Decision-theoretic approach for classification of Ricker wavelets and detection of seismic anomalies: Inst. Electr. Electron. Eng., Geosci. Remote Sensing GE-25(2), 118–123.

    Google Scholar 

  • Huang, K.Y., and Fu, K.S., 1987b, Detection of seismic bright spots using pattern recognition techniques: in Handbook of Geophysical Exploration: Section I. Seismic Exploration, 20, Pattern Recognition and Image Processing, F. Aminzadeh (Ed.): Geophysical Press, London.

    Google Scholar 

  • Huang, K.Y., and Sheen, T.H., 1986, A tree automaton system of syntactic pattern recognition for the recognition of seismic patterns: presented at the 56th Annu. Int. Mtg., Soc. Expl. Geophys.

    Google Scholar 

  • Huang, K.Y., Fu, K.S., Cheng, S.W., and Sheen T.H., 1985a, Image processing of seismogram: (A) Hough transformation for the detection of seismic patterns; (B) Thinning processing in the seismogram: Pattern Recognition 18, 429–440.

    Google Scholar 

  • Illingworth, J. and Kittler, J., 1988, A survey of the Hough transform: Comput. Vision, Graphics, Image Process. 44, 87–116.

    Article  Google Scholar 

  • Payton, C.E., Ed., 1977, Seismic StratigraphyApplication to Hydrocarbon Exploration: Am. Assn. Petr. Geol. Mem. 16.

    Google Scholar 

  • Robertson, J.D., and Nogami, H.H., 1984, Complex seismic trace analysis of thin beds: Geophysics 49, 344–352.

    Google Scholar 

  • Shaw, A.C., 1969, A formal picture description scheme as a basis for picture processing system: Inform. Control 14, 9–52.

    Article  Google Scholar 

  • Taner, M.T., Koehler, F., and Sheriff, R.E., 1979, Complex, seismic trace analysis: Geophysics 44, 1041–1063.

    Google Scholar 

  • Vogel, M.A., and Wong, A.K.C., 1979, PFS clustering method: IEEE Trans. Patt. Anal. Mach. Intel. PAMI-1(3), 237–245.

    Google Scholar 

  • Winston, P.H., 1984, Artificial Intelligence: Addison-Wesley, Reading, MA.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag New York Inc.

About this chapter

Cite this chapter

Huang, KY. (1992). Pattern Recognition to Seismic Exploration. In: Palaz, I., Sengupta, S.K. (eds) Automated Pattern Analysis in Petroleum Exploration. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4388-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-4388-5_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-8751-3

  • Online ISBN: 978-1-4612-4388-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics