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Part I. Introduction to Computational Neural Networks.
Chapter 1. A Brief History.
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.
4. Time-varying data.
Chapter 4. Design of Training and Testing Sets.
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.
2. Commercial software packages.
3. Open source software.
4. News groups.
Part II. Seismic Data Processing.
Chapter 7. Seismic Interpretation and Processing Applications.
2. Waveform recognition.
3. Picking arrival times.
4. Trace editing.
5. Velocity analysis.
6. Elimination of multiples.
Chapter 8. Rock Mass and Reservoir Characterization.
2. Horizon tracking and facies maps.
3. Time-lapse interpretation.
4. Predicting log properties.
5. Rock/reservoir characterization.
Chapter 9. Identifying Seismic Crew Noise.
2. Training set design and network architecture.
4. Analysis of training and testing.
Chapter 10. Self-Organizing Map (SOM) Network for Tracking.
2. Self-organizing map network.
3. Horizon tracking.
4. Classification of the seismic traces.
Chapter 11. Permeability Estimation with an RBF Network and Levenberg-Marquardt Learning.
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.
2. Generalized geophysical inversion.
3. Caianiello neural network method.
4. Inversion with simplified physical models.
5. Inversion with empirically-derived models.
7. Discussions and conclusions.
Part III. Non-Seismic Applications.
Chapter 13. Non-Seismic Applications.
2. Well logging.
3. Gravity and magnetics.
6. Multi-sensor data.
Chapter 14. Detection of AEM Anomalies Corresponding to Dike Structures.
2. Airborne electromagnetic method - theoretical background.
3. Feedforward computational neural networks (CNN).
5. CNNs to calculate homogeneous halfspaces.
6. CNN for detecting 2D structures.
Chapter 15. Locating Layer Boundaries with Unfocused Resistivity Tools.
2. Layer boundary picking.
3. Modular neural network.
4. Training with multiple logging tools.
5. Analysis of results.
Chapter 16. A Neural Network Interpretation System for Near-Surface Geophysics Electromagnetic Ellipticity Soundings.
2. Function approximation.
3. Neural network training.
4. Case history.
Chapter 17. Extracting IP Parameters From TEM Data.
2. Forward modeling.
3. Inverse modeling with neural networks.
4. Testing results.
5. Uncertainty evaluation.
6. Sensitivity evaluation.
7. Case study.
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.
- No. of pages:
- © Pergamon 2001
- 13th June 2001
- eBook ISBN:
Department of Mining & Geological Engineering, Computational Intelligence & Visualization Lab., University of Arizona, Tucson, AZ 85721-0012, USA
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