Geophysical Applications of Artificial Neural Networks and Fuzzy Logic

1. Front Matter

   Pages i-xxi

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2. Exploration Seismology

   1. Front Matter

      Pages xxiii-xxiii

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   2. A Review of Automated First-Break Picking and Seismic Trace Editing Techniques

      • Michael D. McCormack

      Pages 1-12

   3. Automated Picking of Seismic First-Arrivals with Neural Networks

      • Douglas I. Hart

      Pages 13-30

   4. Automated 3-D Horizon Tracking and Seismic Classification Using Artificial Neural Networks

      • Miles Leggett, William A. Sandham, Tariq S. Durrani

      Pages 31-44

   5. Seismic Horizon Picking Using a Hopfield Network

      • Kou-Yuan Huang

      Pages 45-56

   6. Refinement of Deconvolution by Neural Networks

      • Enders A. Robinson

      Pages 57-70

   7. Identification and Suppression of Multiple Reflections in Marine Seismic Data with Neural Networks

      • Robert Essenreiter, Martin Karrenbach, Sven Treitel

      Pages 71-88

   8. Application of Artificial Neural Networks to Seismic Waveform Inversion

      • Qiaodeng He, Hui Zhou

      Pages 89-101

   9. Seismic Principal Components Analysis Using Neural Networks

      • Kou-Yuan Huang

      Pages 103-122

3. Lithology, Well Logs, Prospectivity Mapping and Reservoir Characterisation

   1. Front Matter

      Pages 123-123

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   2. Fuzzy Classification for Lithology Determination from Well Logs

      • Antoine Toumani

      Pages 125-142

   3. Reservoir Property Estimation Using the Seismic Waveform and Feedforword Neural Networks

      • Ping An, Wooil M. Moon, Fotis Kalantzis

      Pages 143-156

   4. An Information Integrated Approach for Reservoir Characterization

      • Li-Yun Fu

      Pages 157-178

   5. An Artificial Neural Network Method for Mineral Prospectivity Mapping: A Comparison with Fuzzy Logic and Bayesian Probability Methods

      • Warick M. Brown, David I. Groves, Tamás D. Gedeon

      Pages 179-196

   6. Oil Reservoir Porosity Prediction Using a Neural Network Ensemble Approach

      • Curtis A. Link, Phillip A. Himmer

      Pages 197-213

   7. Interpretation of Shallow Stratigraphic Facies Using a Self-Organizing Neural Network

      • Curtis A. Link, Stuart Blundell

      Pages 215-230

   8. Neural Network Inversion of EM39 Induction Log Data

      • Lin Zhang, Mary Poulton

      Pages 231-249

4. Electromagnetic Exploration

   1. Front Matter

      Pages 251-251

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   2. Interpretation of Airborne Electromagnetic Data with Neural Networks

      • Edmund Winkler, Wolfgang Seiberl, Andreas Ahl

      Pages 253-265

The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 1940s and 1960s, respectively, the most rapid progress has only been achieved in the last fifteen years. This has been due to significant theoretical advances in our understanding of ANNs and FL, complemented by major technological developments in high-speed computing. In geophysics, ANNs and FL have enjoyed significant success and are now employed routinely in the following areas (amongst others): 1. Exploration Seismology. (a) Seismic data processing (trace editing; first break picking; deconvolution and multiple suppression; wavelet estimation; velocity analysis; noise identification/reduction; statics analysis; dataset matching/prediction, attenuation), (b) AVO analysis, (c) Chimneys, (d) Compression I dimensionality reduction, (e) Shear-wave analysis, (f) Interpretation (event tracking; lithology prediction and well-log analysis; prospect appraisal; hydrocarbon prediction; inversion; reservoir characterisation; quality assessment; tomography). 2. Earthquake Seismology and Subterranean Nuclear Explosions. 3. Mineral Exploration. 4. Electromagnetic I Potential Field Exploration. (a) Electromagnetic methods, (b) Potential field methods, (c) Ground penetrating radar, (d) Remote sensing, (e) inversion.

• Fuzzy
• Information
• Map
• Reservoir
• artificial intelligence
• classification
• fuzzy logic
• geophysics
• intelligence

• University of Strathclyde, Glasgow, Scotland, UK

  William A. Sandham

• Jason Geosystems bv, Rotterdam, The Netherlands

  Miles Leggett

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