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1st Edition - June 13, 2001

**Editor:** M.M. Poulton

eBook ISBN:

9 7 8 - 0 - 0 8 - 0 5 2 9 6 5 - 3

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… Read more

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

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.

1. Introduction.

2. Historical development.

1. Computational neural networks.

2. Biological neural networks.

3. Evolution of the computational neural network.

1. Vocabulary.

2. Back-propagation.

3. Parameters.

4. Time-varying data.

1. Introduction.

2. Re-Scaling.

3. Data distribution.

4. Size reduction.

5. Data coding.

6. Order of data.

1. Improving on back-propagation.

2. Hybrid networks.

3. Alternative architectures.

1. Introduction.

2. Commercial software packages.

3. Open source software.

4. News groups.

1. Introduction.

2. Waveform recognition.

3. Picking arrival times.

4. Trace editing.

5. Velocity analysis.

6. Elimination of multiples.

7. Deconvolution.

8. Inversion.

1. Introduction.

2. Horizon tracking and facies maps.

3. Time-lapse interpretation.

4. Predicting log properties.

5. Rock/reservoir characterization.

1. Introduction.

2. Training set design and network architecture.

3. Testing.

4. Analysis of training and testing.

5. Validation.

6. Conclusions.

1. Introduction.

2. Self-organizing map network.

3. Horizon tracking.

4. Classification of the seismic traces.

5. Conclusions.

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.

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.

1. Introduction.

2. Well logging.

3. Gravity and magnetics.

4. Electromagnetics.

5. Resistivity.

6. Multi-sensor data.

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.

1. Introduction.

2. Layer boundary picking.

3. Modular neural network.

4. Training with multiple logging tools.

5. Analysis of results.

6. Conclusions.

1. Introduction.

2. Function approximation.

3. Neural network training.

4. Case history.

5. Conclusion.

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.

- No. of pages: 352
- Language: English
- Published: June 13, 2001
- Imprint: Pergamon
- eBook ISBN: 9780080529653

MP

Affiliations and expertise

Department of Mining & Geological Engineering, Computational Intelligence & Visualization Lab., University of Arizona, Tucson, AZ 85721-0012, USA