1887
  1. Home
  2. A-Z Publications
  3. Geophysical Prospecting
  4. Volume 40, Issue 6
  5. Article
Volume 40 Number 6
  • E-ISSN: 1365-2478

Abstract

A

A back‐propagation neural network is successfully applied to pick first arrivals (first breaks) in a background of noise. Network output is a decision whether each half‐cycle on the trace is a first or not. 3D plots of the input attributes allow evaluation of the attributes for use in a neural network. Clustering and separation of first break from non‐break data on the plots indicate that a neural network solution is possible, and therefore the attributes are suitable as network input.

Application of the trained network to actual seismic data (Vibroseis and Poulter sources) demonstrates successful automated first‐break selection for the following four attributes used as neural network input: (1) peak amplitude of a half‐cycle; (2) amplitude difference between the peak value of the half‐cycle and the previous (or following) half‐cycle; (3) rms amplitude ratio for a data window (0.3 s) before and after the half‐cycle; (4) rms amplitude ratio for a data window (0.06 s) on adjacent traces. The contribution of the attributes based on adjacent traces (4) was considered significant and future work will emphasize this aspect.

Loading

Article metrics loading...

/content/journals/10.1111/j.1365-2478.1992.tb00543.x
2006-04-27
2024-04-25

  • From This Site

    /content/journals/10.1111/j.1365-2478.1992.tb00543.x
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Allen, R.1982. Automatic phase pickers: their present use and future prospects. Bulletin of the Seismological Society of America72, 225–242.
    [Google Scholar]
  2. Carpenter, G.1989. Neural network models for pattern recognition and associative memory. Neural Networks2, 243–257.
    [Google Scholar]
  3. Coppens, F.1985. First‐arrival picking on common‐offset trace collections for automatic estimations of static corrections. Geophysical Prospecting33, 1212–1231.
    [Google Scholar]
  4. DARPA Neural Network Study1988. afcea International Press, Fairfax , Virginia .
    [Google Scholar]
  5. Dayhoff, J. E.1990. Neural Network Architecture: An Introduction. D. Van Nostrand Co.
    [Google Scholar]
  6. Dorrin, M.B.1976. Introduction to Geophysical Prospecting. McGraw‐Hill Book Co.
    [Google Scholar]
  7. Dowla, F.U., Taylor, S.R. and Anderson, R.W.1990. Seismic discrimination with artificial neural networks: preliminary results with regional spectral data. Bulletin of the Seismological Society of America80, 1346–1373.
    [Google Scholar]
  8. Gelchinsky, B. and Shtivelman, V.1983. Automatic picking of first arrivals and paramaterization of traveltime curves. Geophysical Prospecting31, 915–928.
    [Google Scholar]
  9. Hatherly, P.J.1982. A computer method for determining seismic first‐arrival times. Geophysics47, 1431–1436.
    [Google Scholar]
  10. Hecht‐Nielsen, R.1988. Applications of counterpropagation networks. Neural Networks1, 131–140.
    [Google Scholar]
  11. Lippmann, R.P.1987. An introduction to computing with neural nets. ieee Transactions assp4, 4–22.
    [Google Scholar]
  12. McClelland, J. and Rumelhart, D.1988. Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises. MIT Press.
    [Google Scholar]
  13. Peraldi, R. and Clement, A.1972. Digital processing of refraction data‐study of first arrivals. Geophysical Prospecting20, 529–248.
    [Google Scholar]
  14. Ramananantoandro, R. and Bernitsas, N.1987. A computer algorithm for automatic picking of refraction first‐arrival time. Geoexploration24, 147–151.
    [Google Scholar]
  15. Rumelhart, D.E., Hinton, G.E. and Williams, R.J.1986. Learning internal representations by error propagation. In: Parallel Distributed Processing, D.E.Rumelhart and J.E.McClelland (eds), 1, 318–362. MIT Press.
    [Google Scholar]
  16. Simpson, P.1990. Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations. Pergamon Press, Inc.
    [Google Scholar]
  17. Spagnolini, U.1991. Adaptive picking of refracted first arrivals. Geophysical Prospecting39, 293–312.
    [Google Scholar]
  18. Veezhinathan, J. and Wagner, D.1990. A neural network approach to first‐break picking. Proceedings of the International Joint Conference on Neural Networks, San Diego CA, 1, 235–240.
    [Google Scholar]
  19. Williams, R.J.1986. The logic of activation functions. In: Parallel Distributed Proceedings, D.E.Rumelhart and J.E.McClelland (eds), 1, 423–443. MIT Press.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/j.1365-2478.1992.tb00543.x
Loading
  • Article Type: Research Article

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error