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Seismic Data Interpretation using Digital Image Processing

Seismic Data Interpretation using Digital Image Processing

Abdullatif A. Al-Shuhail, Saleh A. Al-Dossary, Wail A. Mousa

ISBN: 978-1-119-12559-4

Jun 2017

184 pages

Description

Bridging the gap between modern image processing practices by the scientific community at large and the world of geology and reflection seismology

This book covers the basics of seismic exploration, with a focus on image processing techniques as applied to seismic data. Discussions of theories, concepts, and algorithms are followed by synthetic and real data examples to provide the reader with a practical understanding of the image processing technique and to enable the reader to apply these techniques to seismic data. The book will also help readers interested in devising new algorithms, software and hardware for interpreting seismic data.

Key Features:

  • Provides an easy to understand overview of popular seismic processing and interpretation techniques from the point of view of a digital signal processor.
  • Presents image processing concepts that may be readily applied directly to seismic data.
  • Includes ready-to-run MATLAB algorithms for most of the techniques presented.

The book includes essential research and teaching material for digital signal and image processing individuals interested in learning seismic data interpretation from the point of view of digital signal processing. It is an ideal resource for students, professors and working professionals who are interested in learning about the application of digital signal processing theory and algorithms to seismic data.

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Foreword xi

Preface xv

1 Introduction 1

1.1 Image Processing of Exploration Seismic Data 1

1.2 Exploration Seismic Data: From Acquisition to Interpretation 1

1.2.1 Seismic Data Acquisition 2

1.2.2 Seismic Data Processing 2

1.2.3 Seismic Data Interpretation 2

1.3 The Seismic Convolution Model 3

1.4 Summary 6

2 Seismic Data Interpretation 7

2.1 Introduction 7

2.2 Structural Features 11

2.2.1 Faults 11

2.2.2 Folds 18

2.2.3 Diapirs 21

2.3 Stratigraphic Features 22

2.3.1 Channels 24

2.3.2 Reefs 26

2.3.3 Truncation 27

2.4 Seismic Interpretation Tools 27

2.4.1 Seismic Sequence Stratigraphy 29

2.4.2 Seismic Facies Analysis 33

2.4.3 Direct Hydrocarbon Indicators 35

2.4.4 Tying Seismic andWell Data 35

2.4.5 Seismic Modeling 35

2.4.6 Time-to-Depth Conversion 41

2.4.7 Seismic Attributes 44

2.5 Pitfalls in Seismic Interpretation 44

2.6 Summary 48

2.7 Problems and Computer Assignments 49

3 Seismic Image Enhancement in the Spatial Domain 59

3.1 Introduction 59

3.1.1 The Mean (Running-Average) Filter 60

3.2 The Median Filter 63

3.3 The Edge-Preserving Smoothing Algorithm 66

3.3.1 Two-Dimensional Structure-Preserving Smoothing 67

3.4 Wavelet-Based Smoothing 70

3.4.1 Method 70

3.4.2 Sharpening Filter 71

3.5 Summary 72

3.6 Problems and Computer Assignments 73

4 Seismic Image Enhancement in the Spectral Domain 77

4.1 Introduction 77

4.2 The Fourier Transform 77

4.3 Filtering in the Spectral Domain 80

4.4 Spectral Attributes 83

4.5 Summary 85

4.6 Problems and Computer Assignments 85

5 Seismic Attributes 87

5.1 Introduction 87

5.2 Detection of Interesting Regions from Time or DepthThree-Dimensional

Slices using Seismic Attributes 87

5.3 Two-Dimensional Numerical Gradient Edge-Detector Operators 89

5.4 Application to Real Seismic Data 91

5.5 Two-Dimensional Second-Order Derivative Operator 96

5.5.1 The Coherence Attribute 96

5.5.2 The Dip Attribute 100

5.6 The Curvature Attribute 101

5.7 Curvature of the Surface 103

5.7.1 Curve, Velocity, and Curvature 103

5.7.2 Surface, Tangent Plane, and Norm 104

5.8 Shape Operator, Normal Curvature, and Principal Curvature 105

5.8.1 Normal Curvature 105

5.8.2 Shape Operator 105

5.8.3 The Principal Curvatures 106

5.8.4 Calculation of the Principal Curvatures 106

5.8.5 Summary of Calculation of Principal Curvature for a Surface 107

5.9 The Randomness Attribute 108

5.10 Technique for Two-Dimensional Images 109

5.10.1 Problem Statement and Preliminaries 109

5.10.2 Review of Fast Noise Variance Estimation Algorithm 110

5.10.3 Design Mask by Constrained Optimization 111

5.11 The Spectral Decomposition Attribute 113

5.12 Summary 115

5.13 Problems and Computer Assignments 116

6 Color Display of Seismic Images 123

6.1 Introduction 123

6.2 Color Models and Useful Color Bars 124

6.2.1 The RGB Model 125

6.2.2 The CMY Model 125

6.2.3 The HSI Model 126

6.2.4 Useful Color Bars 127

6.3 Overlay and Mixed Displays of Seismic Attribute Images 127

6.4 Summary 130

6.5 Problems and Computer Assignments 130

7 Seismic Image Segmentation 133

7.1 Introduction 133

7.2 Basic Seismic Image Segmentation 134

7.3 Advanced Seismic Image Segmentation 136

7.3.1 Color-Based Segmentation 136

7.3.1.1 The Imposed Constraints for the POCS Color SegmentationMethod 137

7.3.2 Graph-Based Segmentation 139

7.4 Automatic Fault Extraction 140

7.5 Summary 143

Glossary 145

References 151

Index 157