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# 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

**View all volumes in this series:**Handbook of Geophysical Exploration: Seismic Exploration

## 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.

**Chapter 4. Design of Training and Testing Sets.**1. 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. 4. Trace editing. 5. 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.**1. Introduction. 2. 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. 3. Caianiello neural network method. 4. 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. 5. CNNs to calculate homogeneous halfspaces. 6. CNN for detecting 2D structures. 7. Testing. 8. Conclusion.

**Chapter 15. Locating Layer Boundaries with Unfocused Resistivity Tools.**1. Introduction. 2. Layer boundary picking. 3. Modular neural network. 4. Training with multiple logging tools. 5. Analysis of results. 6. Conclusions.

**Chapter 16. A Neural Network Interpretation System for Near-Surface Geophysics Electromagnetic Ellipticity Soundings.**1. Introduction. 2. Function approximation. 3. Neural network training. 4. Case history. 5. Conclusion.

**Chapter 17. Extracting IP Parameters From TEM Data.**1. Introduction. 2. Forward modeling. 3. Inverse modeling with neural networks. 4. Testing results. 5. Uncertainty evaluation. 6. Sensitivity evaluation. 7. Case study. 8. Conclusions.

**Author Index.**

**Index.**

## Description

**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.

**Chapter 4. Design of Training and Testing Sets.**1. 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. 4. Trace editing. 5. 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.**1. Introduction. 2. 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. 3. Caianiello neural network method. 4. 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. 5. CNNs to calculate homogeneous halfspaces. 6. CNN for detecting 2D structures. 7. Testing. 8. Conclusion.

**Chapter 15. Locating Layer Boundaries with Unfocused Resistivity Tools.**1. Introduction. 2. Layer boundary picking. 3. Modular neural network. 4. Training with multiple logging tools. 5. Analysis of results. 6. Conclusions.

**Chapter 16. A Neural Network Interpretation System for Near-Surface Geophysics Electromagnetic Ellipticity Soundings.**1. Introduction. 2. Function approximation. 3. Neural network training. 4. Case history. 5. Conclusion.

**Chapter 17. Extracting IP Parameters From TEM Data.**1. Introduction. 2. Forward modeling. 3. Inverse modeling with neural networks. 4. Testing results. 5. Uncertainty evaluation. 6. Sensitivity evaluation. 7. Case study. 8. Conclusions.

**Author Index.**

**Index.**

## Details

- No. of pages:
- 352

- Language:
- English

- Copyright:
- © Pergamon 2001

- Published:
- 13th June 2001

- Imprint:
- Pergamon

- eBook ISBN:
- 9780080529653

## 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