Advertisement

Automated Picking of Seismic First-Arrivals with Neural Networks

  • Douglas I. Hart
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

Abstract

Neural network implementations of first-arrival picking use a representative set of data and associated first-arrival pick times to create a multilayer perceptron neural network. The success of this technique depends on the statistical properties of the features input to the neural network, and the ability of the neural network to approximate a wide class of functions. Using methods from statistical pattern recognition, it is possible to determine the properties of the features input to the neural network that impact upon the reliability of the picking process. Interpreting the output of the neural network as the probability that the associated feature is the first-arrival, requires an appropriate probability model. This model is based on the multinomial distribution, since it can reflect the fact that there is only one first-arrival event on each data trace. Optimization of the neural network weights with an error function based on this probability distribution, produces a neural network that properly estimates the probability that the associated feature is a first-arrival event.

Keywords

Neural Network Instantaneous Amplitude Seismic Trace Multilayer Perceptron Neural Network Neural Network Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bishop, C. M., 1995, Neural networks for pattern recognition: Oxford University Press.Google Scholar
  2. Coppens, F., 1985, First arrival picking on common-offset trace collections for automatic estimation of static corrections: Geophys. Prosp., 33, 1212–1231.CrossRefGoogle Scholar
  3. Gelchinsky, B., and Shtivelman, V., 1983, Automatic picking for first arrivals and parameterization of traveltime curves: Geophys. Prosp., 31, 915–928.CrossRefGoogle Scholar
  4. Hart, D. I., 1996, Enhancing the reliability of first-break picking using neural networks: 66`s Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1662–1665.Google Scholar
  5. Hatherly, P. J., 1982, A computer method for determining seismic first arrival times: Geophysics, 47, 1431–1436.Google Scholar
  6. Hirota, K., and Iijima, T., 1978, The bounded variation quantity (B.V.Q.) and its application to feature extraction: Proceedings of the Fourth International Conference on Pattern Recognition, November 7–10, Kyoto, Japan, 457–461. (Also available through the Colorado Technical Reference Center, University of Colorado, Boulder (303) 492–8774 Engineering TK 7882.P3.)Google Scholar
  7. McCormack, M. D., Zaucha, D. E., and Dushek, D. W., 1993, First-break refraction event picking and seismic data trace editing using neural networks: Geophysics, 58, 67–78.Google Scholar
  8. Murat, M. E., and Rudman A. J., 1992, Automated first arrival picking: a neural network approach: Geophys. Prosp., 40, 587–604.CrossRefGoogle Scholar
  9. Ripley, B. D., 1996, Pattern recognition and neural networks: Cambridge University Press.Google Scholar
  10. Salle, W. S., ed., 1999, Neural Network FAQ, part 2 of 7: Learning: periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ.html Google Scholar
  11. Spagnolini, U., 1991, Adaptive picking of refracted first arrivals: Geophys. Prosp., 40, 587–604.Google Scholar
  12. Stresau, W. R., Farrell, R. C., and Ford, K. P., 1992, 3-D refraction analysis and modeling using surface-consistent decomposition on the workstation: 62n1 Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1116–1119.Google Scholar
  13. Veezhinathan, J., Wagner, D., and Ehlers, J., 1991, First break picking using a neural network, in Aminzadeh, F., and Simaan, M., Eds., Expert systems in exploration: Soc. Expl. Geophys., 179–202.CrossRefGoogle Scholar
  14. White, H., 1992, Artificial neural networks: approximation and learning theory: Blackwell Publishers.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Douglas I. Hart
    • 1
  1. 1.Western GeophysicalDenverUSA

Personalised recommendations