Application of neural nets to seismic signal analysis (original) (raw)

Abstract

We present a comparative study of the performance of reported neural net algorithms for the detection of first breaks in seismic reflection data with regard to accuracy, learning rate and generalisability. In addition we suggest a new approach that produces improved results

Key takeaways

sparkles

AI

  1. The PTDA method achieved 100% accuracy on dynamite data and 75% on Vibroseis data.
  2. Cascade-correlation nets demonstrate superior training speed, up to 40 times faster than back-propagation.
  3. First-break picking accuracy remains under 75% with traditional methods, highlighting neural nets' potential.
  4. Neural nets improve seismic signal analysis through effective preprocessing and architecture optimization.
  5. This study evaluates neural net algorithms for first-break detection compared to traditional methods.

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (9)

  1. M.D. McCormack. Seismic trace editing and first- break picking. In 60th. Ann. Internat. Mtg. Soc. Expl. Geophys, pages 321-324, 1990. Expanded Abstracts.
  2. K.Y. Huang, W.R. Chang, and H.T. Yen. Self organis- ing neural networks for picking seismic horizons. In 60th. Ann. Internat. Mtg. Soc. Expl. Geophys, pages 313-316, 1990. Expanded Abstracts.
  3. X. Liu, P. Yue, and L. Li. Neural network method for tracing seismic events. In 59th. Ann. Internat. Mtg. Soc. Expl. Geophys, pages 716-718, 1989. Expanded Abstracts.
  4. S.Y. Lu and J.G. Berryman. Inverse scattering, seis- mic traveltime and neural networks. Technical Report UCRL-JC-104358, Lawrence Livermore Natl. Lab., 1990.
  5. J. Veezhinathan, D. Wagner, and J. Ehlers. First break picking using a neural network. In F. Aminzadeh and H. Simann, editors, Expert Systems in Exploration, chapter 8, pages 179-202. Society of Exploration Geophysicists, 1991. ISBN 0-56080-023-2.
  6. T. Kusuma and M.M. Brown. First break picking and trace editing using cascade-correlation learning architecture. Int. Conf. on Petroleum Exploration and Production, 1992.
  7. M.E. Murat and A.J. Rudman. Automated first arrival picking: a neural network approach. Geophysical Prospecting, 40:587-604, 1992.
  8. D. Rumelhart, G. Hinton, and R. Williams. Learning internal representations by error propagation. In Par- allel Distributed Processing 1, chapter 8. MIT Press, 1986.
  9. S.E. Fahlman and C.Lebiere. The cascade-correlation learning architecture. In D.S. Touretzky, editor, Ad- vances in Neural Information Processing Systems 2, pages 524-532. Morgan Kaufmann,San Mateo,USA, 1990.