| Document Type
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BL
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| Record Number
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956001
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| Doc. No
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b710371
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| Title & Author
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Computational neural networks for geophysical data processing /\ edited by Mary M. Poulton.
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| Edition Statement
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1st ed.
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| Publication Statement
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Amsterdam ;New York :: Pergamon,, 2001.
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| Series Statement
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Seismic exploration,; v. 30
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| Page. NO
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1 online resource (xiii, 335 pages) :: illustrations.
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| ISBN
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0080439861
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: 0080529658
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: 1281038091
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: 9780080439860
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: 9780080529653
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: 9781281038098
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| Bibliographies/Indexes
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Includes bibliographical references and indexes.
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| Contents
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Front Cover; Computational Neural Networks for Geophysical Data Processing; Copyright Page; Table of Contents; Preface; Contributing Authors; Part I: Introduction to Computational Neural Networks; Chapter 1. A Brief History; Chapter 2. Biological Versus Computational Neural Networks; Chapter 3. Multi-Layer Perceptrons and Back-Propagation Learning; Chapter 4. Design of Training and Testing Sets; Chapter 5. Alternative Architectures and Learning Rules; Chapter 6. Software and Other Resources; Part II: Seismic Data Processing; Chapter 7. Seismic Interpretation and Processing Applications.
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Chapter 8. Rock Mass and Reservoir CharacterizationChapter 9. Identifying Seismic Crew Noise; Chapter 10. Self-Organizing Map (SOM) Network for Tracking Horizons and Classifying Seismic Traces; Chapter 11. Permeability Estimation with an RBF Network and Levenberg-Marquardt Learning; Chapter 12. Caianiello Neural Network Method for Geophysical Inverse Problems; Part III: Non-Seismic Applications; Chapter 13. Non-Seismic A.
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| Abstract
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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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| Subject
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Neural networks (Computer science)
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| Subject
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Prospecting-- Geophysical methods-- Data processing.
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| Subject
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Neural networks (Computer science)
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Prospecting-- Geophysical methods-- Data processing.
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| Subject
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TECHNOLOGY ENGINEERING-- Mining.
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| Dewey Classification
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622/.15/0285632
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| LC Classification
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TN269.C59 2001eb
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| Added Entry
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Poulton, Mary M.
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