عنوان

Computational neural networks for geophysical data processing /

(Number (ISBN

0080439861

(Number (ISBN

0080529658

(Number (ISBN

1281038091

(Number (ISBN

9780080439860

(Number (ISBN

9780080529653

(Number (ISBN

9781281038098

Number

b710371

Title Proper

Computational neural networks for geophysical data processing /

General Material Designation

[Book]

First Statement of Responsibility

edited by Mary M. Poulton.

Edition Statement

1st ed.

Place of Publication, Distribution, etc.

New York :

Name of Publisher, Distributor, etc.

Pergamon,

Date of Publication, Distribution, etc.

2001.

Specific Material Designation and Extent of Item

1 online resource (xiii, 335 pages) :

Other Physical Details

illustrations.

Series Title

Seismic exploration,

Volume Designation

v. 30

ISSN of Series

0950-1401 ;

Text of Note

Includes bibliographical references and indexes.

Text of Note

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.

Text of Note

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.

0

8

Text of Note

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.

Source for Acquisition/Subscription Address

00991439

Title

Computational neural networks for geophysical data processing.

International Standard Book Number

9780080439860

Neural networks (Computer science)

Prospecting-- Geophysical methods-- Data processing.

Neural networks (Computer science)

Prospecting-- Geophysical methods-- Data processing.

TECHNOLOGY & ENGINEERING-- Mining.

QC

RBG

TEC-- 026000

Number

Edition

Class number

Book number

Poulton, Mary M.

Date of Transaction

20201208010734.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y