UID:
almahu_9948026615402882
Format:
1 online resource (351 p.)
Edition:
1st ed.
ISBN:
1-281-03809-1
,
9786611038090
,
0-08-052965-8
Series Statement:
Handbook of geophysical exploration. Seismic exploration, v. 30
Content:
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
Note:
Description based upon print version of record.
,
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; 1. Introduction; 2. Historical Development; Chapter 2. Biological Versus Computational Neural Networks; 1. Computational Neural Networks; 2. Biological Neural Networks; 3. Evolution of the Computational Neural Network; Chapter 3. Multi-Layer Perceptrons and Back-Propagation Learning; 1. Vocabulary; 2. Back-Propagation; 3. Parameters; 4. Time-Varying Data
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Chapter 4. Design of Training and Testing Sets1. Introduction; 2. Re-Scaling; 3. Data Distribution; 4. Size Reduction; 5. Data Coding; 6. Order of Data; Chapter 5. Alternative Architectures and Learning Rules; 1. Improving on Back-Propagation; 2. Hybrid Networks; 3. Alternative Architectures; Chapter 6. Software and Other Resources; 1. Introduction; 2. Commercial Software Packages; 3. Open Source Software; 4. News Groups; Part II: Seismic Data Processing; Chapter 7. Seismic Interpretation and Processing Applications; 1. Introduction; 2. Waveform Recognition; 3. Picking Arrival Times
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4. Trace Editing5. Velocity Analysis; 6. Elimination of Multiples; 7. Deconvolution; 8. Inversion; Chapter 8. Rock Mass and Reservoir Characterization; 1. Introduction; 2. Horizon Tracking and Facies Maps; 3. Time-Lapse Interpretation; 4. Predicting Log Properties; 5. Rock/Reservoir Characterization; Chapter 9. Identifying Seismic Crew Noise; 1. Introduction; 2. Training Set Design and Network Architecture; 3. Testing; 4. Analysis of Training and Testing; 5. Validation; 6. Conclusions; Chapter 10. Self-Organizing Map (SOM) Network for Tracking Horizons and Classifying Seismic Traces
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1. Introduction2. Self-Organizing Map Network; 3. Horizon Tracking; 4. Classification of the Seismic Traces; 5. Conclusions; Chapter 11. Permeability Estimation with an RBF Network and Levenberg-Marquardt Learning; 1. Introduction; 2. Relationship Between Seismic and Petrophysical Parameters; 3. Parameters That Affect Permeability: Porosity, Grain Size, Clay Content; 4. Neural Network Modeling of Permeability Data; 5. Summary and Conclusions; Chapter 12. Caianiello Neural Network Method for Geophysical Inverse Problems; 1. Introduction; 2. Generalized Geophysical Inversion
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3. Caianiello Neural Network Method4. Inversion With Simplified Physical Models; 5. Inversion With Empirically-Derived Models; 6. Example; 7. Discussions and Conclusions; Part III: Non-Seismic Applications; Chapter 13. Non-Seismic Applications; 1. Introduction; 2. Well Logging; 3. Gravity and Magnetics; 4. Electromagnetics; 5. Resistivity; 6. Multi-Sensor Data; Chapter 14. Detection of AEM Anomalies Corresponding to Dike Structures; 1. Introduction; 2. Airborne Electromagnetic Method- Theoretical Background; 3. Feedforward Computational Neural Networks (CNN); 4. Concept
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5. CNNs to Calculate Homogeneous Halfspaces
,
English
Additional Edition:
ISBN 0-08-043986-1
Language:
English
