Computational Neural Networks for Geophysical Data Processing, Volume 30

1st Edition

Editors: M.M. Poulton
Hardcover ISBN: 9780080439860
eBook ISBN: 9780080529653
Imprint: Pergamon
Published Date: 13th June 2001
Page Count: 352
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Table of Contents


Contributing Authors.

Part I. Introduction to Computational Neural Networks.

Chapter 1. A Brief History.

  1. Introduction.
  2. Historical development.Chapter 2. Biological Versus Computational Neural Networks.
  3. Computational neural networks.
  4. Biological neural networks.
  5. Evolution of the computational neural network. Chapter 3. Multi-Layer Perceptrons and Back-Propagation Learning.
  6. Vocabulary.
  7. Back-propagation.
  8. Parameters.
  9. Time-varying data. Chapter 4. Design of Training and Testing Sets.
  10. Introduction.
  11. Re-Scaling.
  12. Data distribution.
  13. Size reduction.
  14. Data coding.
  15. Order of data. Chapter 5. Alternative Architectures and Learning Rules.
  16. Improving on back-propagation.
  17. Hybrid networks.
  18. Alternative architectures. Chapter 6. Software and Other Resources.
  19. Introduction.
  20. Commercial software packages.
  21. Open source software.
  22. News groups. Part II. Seismic Data Processing. Chapter 7. Seismic Interpretation and Processing Applications.
  23. Introduction.
  24. Waveform recognition.
  25. Picking arrival times.
  26. Trace editing.
  27. Velocity analysis.
  28. Elimination of multiples.
  29. Deconvolution.
  30. Inversion.Chapter 8. Rock Mass and Reservoir Characterization.
  31. Introduction.
  32. Horizon tracking and facies maps.
  33. Time-


This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances in network design, data set design, testing strategy, and error analysis.

Computational, rather than artificial, modifiers are used for neural networks in this book to make a distinction between networks that are implemented in hardware and those that are implemented in software. The term artificial neural network covers any implementation that is inorganic and is the most general term. Computational neural networks are only implemented in software but represent the vast majority of applications.

While this book cannot provide a blue print for every conceivable geophysics application, it does outline a basic approach that has been used successfully.


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About the Editors

M.M. Poulton Editor

Affiliations and Expertise

Department of Mining & Geological Engineering, Computational Intelligence & Visualization Lab., University of Arizona, Tucson, AZ 85721-0012, USA