Computational neural networks for geophysical data processing

✍ Scribed by Mary M. Poulton (Eds.)

• Publisher: Pergamon
• Year: 2001
• Leaves: 351
• Series: Handbook of Geophysical Exploration: Seismic Exploration 30
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Content:
Preface
Pages xi-xii
Mary M. Poulton

Contributing authors
Page xiii

Chapter 1 A brief history Original Research Article
Pages 3-18
Mary M. Poulton

Chapter 2 Biological versus computational neural networks Original Research Article
Pages 19-25
Mary M. Poulton

Chapter 3 Multi-layer perceptrons and back-propagation learning Original Research Article
Pages 27-53
Mary M. Poulton

Chapter 4 Design of training and testing sets Original Research Article
Pages 55-65
Mary M. Poulton

Chapter 5 Alternative architectures and learning rules Original Research Article
Pages 67-88
Mary M. Poulton

Chapter 6 Software and other resources Original Research Article
Pages 89-98
Mary M. Poulton

Chapter 7 Seismic interpretation and processing applications Original Research Article
Pages 101-118
Meghan S. Miller, Kathy S. Powell

Chapter 8 Rock mass and reservoir characterization Original Research Article
Pages 119-127
Mary M. Poulton, Kathy S. Powell

Chapter 9 Identifying seismic crew noise Original Research Article
Pages 129-154
Vinton B. Buffenmyer

Chapter 10 Self-Organizing Map (SOM) network for tracking horizons and classifying seismic traces Original Research Article
Pages 155-170
Lin Zhang, John Quieren, James Schuelke

Chapter 11 Permeability estimation with an RBF network and Levenberg-Marquardt learning Original Research Article
Pages 171-186
Fred K. Boadu

Chapter 12 Caianiello neural network method for geophysical inverse problems Original Research Article
Pages 187-215
Li-Yun Fu

Chapter 13 Non-seismic applications Original Research Article
Pages 219-233
Mary M. Poulton

Chapter 14 Detection of AEM anomalies corresponding to dike structures Original Research Article
Pages 235-256
Andreas Ahl, Wolfgang Seiberl

Chapter 15 Locating layer boundaries with unfocused resistivity tools Original Research Article
Pages 257-285
Lin Zhang

Chapter 16 A neural network interpretation system for near-surface geophysics electromagnetic ellipticity soundings Original Research Article
Pages 287-306
Ralf A. Birken

Chapter 17 Extracting IP parameters from TEM data Original Research Article
Pages 307-326
Hesham El-Kaliouby

Author index
Pages 327-329

Index
Pages 331-335