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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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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Seitenzahl: 214
Veröffentlichungsjahr: 2017
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Table of Contents
Cover
Title Page
Copyright
Dedication
Foreword
Preface
Chapter 1: Introduction
1.1 Image Processing of Exploration Seismic Data
1.2 Exploration Seismic Data: From Acquisition to Interpretation
1.3 The Seismic Convolution Model
1.4 Summary
Chapter 2: Seismic Data Interpretation
2.1 Introduction
2.2 Structural Features
2.3 Stratigraphic Features
2.4 Seismic Interpretation Tools
2.5 Pitfalls in Seismic Interpretation
2.6 Summary
2.7 Problems and Computer Assignments
Chapter 3: Seismic Image Enhancement in the Spatial Domain
3.1 Introduction
3.2 The Median Filter
3.3 The Edge-Preserving Smoothing Algorithm
3.4 Wavelet-Based Smoothing
3.5 Summary
3.6 Problems and Computer Assignments
Chapter 4: Seismic Image Enhancement in the Spectral Domain
4.1 Introduction
4.2 The Fourier Transform
4.3 Filtering in the Spectral Domain
4.4 Spectral Attributes
4.5 Summary
4.6 Problems and Computer Assignments
Chapter 5: Seismic Attributes
5.1 Introduction
5.2 Detection of Interesting Regions from Time or Depth Three-Dimensional Slices using Seismic Attributes
5.3 Two-Dimensional Numerical Gradient Edge-Detector Operators
5.4 Application to Real Seismic Data
5.5 Two-Dimensional Second-Order Derivative Operator
5.6 The Curvature Attribute
5.7 Curvature of the Surface
5.8 Shape Operator, Normal Curvature, and Principal Curvature
5.9 The Randomness Attribute
5.10 Technique for Two-Dimensional Images
5.11 The Spectral Decomposition Attribute
5.12 Summary
5.13 Problems and Computer Assignments
Chapter 6: Color Display of Seismic Images
6.1 Introduction
6.2 Color Models and Useful Color Bars
6.3 Overlay and Mixed Displays of Seismic Attribute Images
6.4 Summary
6.5 Problems and Computer Assignments
Chapter 7: Seismic Image Segmentation
7.1 Introduction
7.2 Basic Seismic Image Segmentation
7.3 Advanced Seismic Image Segmentation
7.4 Automatic Fault Extraction
7.5 Summary
Glossary
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Begin Reading
List of Illustrations
Chapter 1: Introduction
Figure 1.1 During the acquisition stage, a seismic wavelet is convolved with the reflectivity function of the rocks and recorded via many receivers to obtain a seismogram. The seismogram is processed to yield a seismic image. This image is then analyzed by interpreters. Note that the reflectivity function is related to the geologic section of the subsurface through the reflection coefficient of each geologic boundary and the two-way traveltime.
Figure 1.2 Various displays for a seismic section image: (a) wiggle display, (b) variable area display, (c) wiggle-variable area display, (d) gray-scaled variable density display, and (e) colored variable density display. Note that the vertical axis represents the two-way traveltime increasing downward, while the horizontal axis represents the distance from left to right. (
See color plate section for the color representation of this figure.
)
Chapter 2: Seismic Data Interpretation
Figure 2.1 Relationship among the requirements of interpretation, processing and acquisition.
Figure 2.2 Temperature and depth generation windows of petroleum (intechopen.com).
Figure 2.3 (a) Micrograph showing primary intergranular porosity. (b) Micrograph showing primary intragranular porosity.
Figure 2.4 (a) A rock core exhibiting vuggy porosity. (b) A rock core exhibiting fracture porosity.
Figure 2.5 Main types of faults; (a) normal fault; (b) reverse (thrust) fault; (c) strike-slip (wrench) fault; (d) oblique (combined) fault.
Figure 2.6 Normal faults forming unlimited (a) and limited (b) petroleum traps; reverse faults forming unlimited (c) and limited (d) petroleum traps.
Figure 2.7 (a) Seismic section showing normal faults; (b) interpreted section (VSA author: Butler, 2015; data courtesy of Fugro N.V.).
Source
: Courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.8 (a) Seismic section showing a reverse fault; (b) interpreted section (VSA author: Butler, 2015; data courtesy of CGG Veritas).
Source
: Courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.9 (a) Seismic section showing a flower structure associated with a strike-slip fault; (b) interpreted section (VSA author: Stewart, 2015).
Source
: Courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.10 (a) Seismic section showing reflection termination at faults; (b) interpreted section (VSA author: Butler, 2015; data courtesy of Fugro N.V.).
Source
: Courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.11 Seismic section showing reflection offset across faults (VSA author: Butler, VSA, 2015; data courtesy of Fugro N.V.).
Source
: Courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.12 (a) Stacked seismic section showing differential reflection dip across faults; (b) interpretation showing fault locations (VSA author: Butler, 2015; data courtesy of Fugro N.V.).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.13 (a) Diffractions on an unmigrated seismic section; (b) diffractions removed after seismic migration.
Figure 2.14 (a) Seismic section showing a fault producing a reflection; (b) interpreted section. (VSA author: Butler, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.15 (a) Seismic section showing seismic fault zone exhibiting amplitude loss; (b) interpreted section (VSA author: Butler, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.16 (a) Seismic section showing compressional folds; (b) interpreted section (VSA author: Butler, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.17 (a) Seismic section showing a fold generated by a salt diapir; (b) interpreted section (VSA author: Stewart, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.18 (a) Seismic section showing a compactional folds; (b) interpreted section (VSA author: Jackson, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.19 (a) Seismic section showing a mud diapir; (b) interpreted section (VSA author: Jackson, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.20 (a) A 3D seismic horizon slice showing channels; (b) interpreted slice (VSA author: Sylvester, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.21 A 2D seismic section showing channels (VSA author: Torvela, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.22 A 2D seismic section showing channels (indicated by arrows) with false synclines below them due to low velocity of channel fill material (VSA author: Posamentier, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.23 A 2D seismic section showing patch reefs with strong reflections from reef tops due to high contrast with overlying layers (VSA author: Posamentier, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.24 A 3D seismic volume showing a patch reef (VSA author: Posamentier, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.25 (a) A 2D seismic section of a truncation trap formed by an angular unconformity; (b) interpreted section (VSA author: Hunt, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.26 (a) Seismic section showing downlaps and toplaps; (b) interpreted section (VSA
Source
: Butler, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.27 (a) Seismic section showing onlaps; (b) interpreted section (VSA source: Jackson, 2015; data courtesy of CGG Veritas)
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.28 (a) Seismic section showing erosional truncation; (b) interpreted section (VSA source: Stewart, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.29 Seismic section showing concordance (VSA author: Calves, 2015; data courtesy of PeruPetro).
Figure 2.30 Seismic section showing parallel reflection configuration (VSA author: Jobe, 2015).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.31 Seismic section showing chaotic reflection configuration (VSA author: Butler, 2015; data courtesy of Fugro N.V.).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.32 Seismic section showing prograding (sigmoidal) reflection configuration (VSA author: Butler, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.33 (a) Seismic section showing bright spot; (b) interpreted section (VSA author: Butler, 2015; data courtesy of CGG Veritas).
Source
: courtesy of Virtual Seismic Atlas (VSA), 2015 (www.seismicatlas.org). (
See color plate section for the color representation of this figure.
)
Figure 2.34 (a) Seismic section showing a dim spot and phase reversal; (b) interpreted section (VSA author: Calves, 2015; data courtesy of PeruPetro).
Figure 2.35 Seismic section showing a flat spot due to an oil–water contact at about 2 s, as confirmed by drilling and logging results.
Source
: Yilmaz, http://wiki.seg.org/wiki/Seismic_Data_Analysis. Used under CC-BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en [1]. (
See color plate section for the color representation of this figure.
)
Figure 2.36 (a) Seismic section showing a gas chimney; (b) interpreted section (VSA author: Calves, 2015; data courtesy of PeruPetro).
Figure 2.37 Seismic volume showing P effect (left) but no S effect (right) due to fluid effects.
Source
: Yilmaz, http://wiki.seg.org/wiki/Seismic_Data_Analysis. Used under CC-BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en [1]. (
See color plate section for the color representation of this figure.
)
Figure 2.38 Seismic CMP gather showing AVO effect at 1.25 s.
Figure 2.39 Example of tying well and seismic data sets.
Figure 2.40 (a) Synthetic seismic data generated from the velocity model in (b) using finite-difference modeling.
Source
: Yilmaz, http://wiki.seg.org/wiki/Seismic_Data_Analysis. Used under CC-BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en [1]. (
See color plate section for the color representation of this figure.
)
Figure 2.41 (a) Synthetic seismic stacked section derived from the velocity–depth model in Figure 2.40b. (b) Stacking velocity field derived from (a), where colored curves indicate picked primary reflections.
Source
: Yilmaz, http://wiki.seg.org/wiki/Seismic_Data_Analysis. Used under CC-BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en [1]. (
See color plate section for the color representation of this figure.
)
Figure 2.42 (a) Velocity–depth model derived from the time and stacking velocities in Figure 2.41b using the Dix method for time-to-depth conversion. (b) True velocity–depth model.
Source
: Yilmaz, http://wiki.seg.org/wiki/Seismic_Data_Analysis. Used under CC-BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en [1]. (
See color plate section for the color representation of this figure.
)
Figure 2.43 (Left) Unmigrated seismic section showing a gentler and wider dome; (right) migrated section with sharper and narrower dome.
Figure 2.44 (a) Unmigrated seismic section showing bowties. (b) Migrated section with bowties removed.
Figure 2.45 An inline (top left) and a crossline (top right) stacked section from a land 3D survey. Also shown are the 2D (center row) and 3D (bottom row) migrations of the two stacked sections.
Figure 2.46 (a) Velocity–depth model showing lateral velocity variation in the near-surface layer; (b) synthetic stacked seismic section derived from this model. Note the false structural high under the near-surface lateral velocity increase.
Source
: Yilmaz, http://wiki.seg.org/wiki/Seismic_Data_Analysis. Used under CC-BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en [1]. (
See color plate section for the color representation of this figure.
)
Figure 2.47 Stacked seismic section showing a false structural high of horizon B under a salt diapir due to the velocity increase of the diapir relative to surrounding rocks.
Figure 2.48 (a) Marine stacked seismic section contaminated with water-bottom multiple. (b) Section after multiple removal.
Figure 2.49 Image for Problem 2.1.
Figure 2.50 Image for Problem 2.2.
Figure 2.51 Image for Problem 2.3. (
See color plate section for the color representation of this figure.
)
Figure 2.52 Image for Problem 2.4. (
See color plate section for the color representation of this figure.
)
Figure 2.53 Image for Problem 2.5. (
See color plate section for the color representation of this figure.
)
Figure 2.54 Image for Problem 2.6. (
See color plate section for the color representation of this figure.
)
Figure 2.55 Image for Problem 2.7. (
See color plate section for the color representation of this figure.
)
Figure 2.56 Image for Problem 2.8.
Figure 2.57 Image for Problem 2.9.
Figure 2.58 Image for Problem 2.10.
Figure 2.59 Image for Problem 2.11. (
See color plate section for the color representation of this figure.
)
Figure 2.60 Image for Problem 2.12. (
See color plate section for the color representation of this figure.
)
Figure 2.61 Image for Problem 2.13.
Chapter 3: Seismic Image Enhancement in the Spatial Domain
Figure 3.1 Illustration of window selection and mean calculation.
Figure 3.2 (a) Original matrix. (b) Mean-filtered conventionally. (c) The mean-filtered matrix after the application of the zero-padding technique.
Figure 3.3 (a) Zero padding a matrix for a window. (b) The matrix from the example.
Figure 3.4 Convolution matrices for mean filters of window sizes (a) , (b) , and (c) .
Figure 3.5 (a) Original image. (b) Image after the application of the mean filter. (c) Sobel edge detection of the original image without filtering. (d) Sobel edge detection of the image after mean filtering.
Figure 3.6 Median-filtering operation. (a) Original matrix. (b) Extracting elements from the first window. (c) Sorting the elements. (d) The median-filtered matrix with the result of the first window highlighted.
Figure 3.7 (a) Original image. (b) Image after the application of the median filter. (c) Sobel edge detection of the original image without filtering. (d) Sobel edge detection of the image after median filtering.
Figure 3.8 (a) Original image. (b) Image after the application of the alpha-trimmed mean filter for . (c) Sobel edge detection of the original image without filtering. (d) Sobel edge detection of the image after the application of the alpha-trimmed mean filter for .
Figure 3.9 Nine masks of Nagao.
Figure 3.10 (a) Original image. (b) Image after the application of the EPS algorithm. (c) Sobel edge detection of the original image without filtering. (d) Sobel edge detection of the image after applying the EPS algorithm.
Figure 3.11 Sobel edge detection of the (a) original image without filtering, (b) image after mean filtering, (c) image after median filtering, and (d) image after applying the EPS algorithm.
Figure 3.12 The operators with size of .
Figure 3.13 (a) Original image. (b) Image after the application of the SPS algorithm. (c) Sobel edge detection of the original image without filtering. (d) Sobel edge detection of the image after applying the SPS algorithm.
Figure 3.14 (a) Original image. (b) Image after the application of the wavelet smoothing algorithm. (c) Sobel edge detection of the original image without filtering. (d) Sobel edge detection of the image after applying the wavelet smoothing algorithm.
Figure 3.15 Effect of sharpening kernel on seismic coherence data (). (a) Original image (b) Sharpened image; edges are much thinner and contrast is increased. A slight ringing effect appears. Noise in the initial data set is amplified as well. (c) Sobel edge detection of the original image. (d) Sobel edge detection of the image after applying the sharpening filter.
Figure 3.16 Matrix for Problems 3.1–3.4.
Figure 3.17 Matrix for Problem 3.7.
Figure 3.18 Image for Problem 3.9.
Chapter 4: Seismic Image Enhancement in the Spectral Domain
Figure 4.1 (a) in Equation (4.5). (b) its magnitude spectrum given by Equation (4.6).
Figure 4.2 (a) Smoothing of the seismic image. (b) The magnitude spectrum of (a) using the 2D DFT. The smoothing operator filter in Equation (4.12) magnitude spectrum is shown in (c). (d) The filtered seismic image in the spectrum domain. (e) The smoothed image after applying the inverse 2D DFT on (d).
Figure 4.3 (a) Sharpening of the seismic image. (b) The magnitude spectrum of (a) using the 2D DFT. The sharpening operator filter in Equation (4.13) magnitude spectrum is shown in (c). (d) The filtered seismic image in the spectrum domain. (e) The sharpened image after applying the inverse 2D DFT on (d).
Figure 4.4 (a) A seismic image, (b) its instantaneous amplitude, (c) its instantaneous phase, and (d) its instantaneous frequency. (
See color plate section for the color representation of this figure.
)
Figure 4.5 (a) A seismic image, (b) its instantaneous amplitude, (c) its instantaneous phase, and (d) its instantaneous frequency. (
See color plate section for the color representation of this figure.
)
Chapter 5: Seismic Attributes
Figure 5.1 Edge detection example: (a) original image, (b) result of simple finite difference horizontal kernel, (c) result of simple finite difference vertical kernel, and (d) gradient magnitude.
Figure 5.2 Edge detection example: (a) original image, (b) result of Sobel horizontal kernel, (c) result of Sobel vertical kernel, and (d) gradient magnitude. (
See color plate section for the color representation of this figure.
)
Figure 5.3 Edge detection example: (a) original image, (b) result of Prewitt horizontal kernel, (c) result of Prewitt vertical kernel, and (d) gradient magnitude. (
See color plate section for the color representation of this figure.
)
Figure 5.4 Edge detection using the Canny edge detector: (a) original image, (b) default values, , , , (c) , , , and (d) , , . (
See color plate section for the color representation of this figure.
)
Figure 5.5 Edge detection using the LOG edge detector: (a) original image, (b) default value (), (c) , and (d) . (
See color plate section for the color representation of this figure.
)
Figure 5.6 Edge detection using the Kirsch compass mask detector. Results in (a) north, (b) northeast, (c) east, (d) southeast, (e) south, (f) southwest, (g) west, and (h) northwest directions. (i) Resultant edge.
Figure 5.7 (a) Input of time slice seismic data; (b) after applying the Laplacian operator (Equation (5.20)); (c) after applying the Laplacian operator (Equation (5.21)). (
See color plate section for the color representation of this figure.
)
Figure 5.8 (a) Input time slice; (b) after applying the difference method of calculating coherence using Luo
et al
. algorithm.
Source:
[43]. (
See color plate section for the color representation of this figure.
)
Figure 5.9 Pixel operation to find the dip angle.
Figure 5.10 (a) Input time slice. (b) Dip attribute slice computed using Equation (5.23). (
See color plate section for the color representation of this figure.
)
Figure 5.11 Illustration of curvature of a curve. At point A on the left side the curvature is small; hence, the curvature radius is large. At point B on the right side the curvature is large; hence, the curvature radius is small.
Figure 5.12 A surface along with a tangent plane at a given point P.
Figure 5.13 Illustration of the idea of normal curvature.
Figure 5.14 The principal curvatures and the principal directions. For (a), along the direction of vertical plane P1, the curvature is the maximum. For (b), following the direction along the vertical plane P2, the curvature is the minimum.
Figure 5.15 Noisy image model.
Figure 5.16 The behavior of the mask on the step edge boundary.
Figure 5.17 (a) Input time slice. (b) Randomness attribute slice computed using Immerkær mask. Note that in image (b) the noise is more emphasized than the channels. (
See color plate section for the color representation of this figure.
)
Figure 5.18 (a) Seismic time slice. (b–d) Single-frequency time slices of (b) 30 Hz, (c) 50 Hz, and (d) 100 Hz. The channels are clearly visible on the 100 Hz time slice. (
See color plate section for the color representation of this figure.
)
Figure 5.19 (a) Matrix for Problems 5.1–5.5. (b) Matrix for Problem 5.6.
Figure 5.20 Image for Problem 5.9.
Figure 5.21 Image for Problem 5.9(a).
Figure 5.22 Images for Problem 5.9(b).
Figure 5.23 Images for Problem 5.10(a).
Figure 5.24 Images for Problem 5.10(b).
Chapter 6: Color Display of Seismic Images
Figure 6.1 Color wavelengths comprising the visible range of the electromagnetic spectrum.
Source
: https://9-4fordham.wikispaces.com/Electro+Magnetic+Spectrum+and+light. (
See color plate section for the color representation of this figure.
)
Figure 6.2 A 3D subspace normalized color cube of the color space, where red, green, blue, cyan, magenta, yellow, black, and white are at the corners of the cube. Note also that the gray scale is only a diagonal linking the black corner to the white one. By coloring seismic images and seismic image attributes, one will be able to see more details than with gray-scaled ones. (
See color plate section for the color representation of this figure.
)
Figure 6.3 The HSI model defined by a double-sided cone. The hue axis rotates through various colors at various angles ranging from 0 to so it can be used to map seismic attributes such as azimuth. The saturation axis is useful for dip–azimuth attribute images. The intensity is perpendicular to saturation. (
See color plate section for the color representation of this figure.
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Figure 6.4 Examples of commonly used color bar maps for various seismic attribute images. (
See color plate section for the color representation of this figure.
)
Figure 6.5 Variable-density displays using various color maps for (a) seismic image amplitudes with a gray-color map, (b) seismic image amplitudes in (a) but with a dual-property attribute color map, (c) the dip magnitude of (a) with a single-polarity gray-color map, and (d) the instantaneous amplitude of (a) with a single-polarity color map. (
See color plate section for the color representation of this figure.
)
Figure 6.6 Overlay of the instantaneous phase attribute of a seismic image with (a) black positive peaks variable area and (b) black positive peaks and gray negative lobes variable area. (
See color plate section for the color representation of this figure.
)
Figure 6.7 Variable-density displays using various color maps for (a) seismic image amplitudes, (b) its instantaneous amplitude, (c) a blend-mix of both (a) and (b) with , and (d) part of (a) mixed with (b) at the same locations with . (
See color plate section for the color representation of this figure.
)
Figure 6.8 Images for Problem 6.3.
Chapter 7: Seismic Image Segmentation
Figure 7.1 The basic principle of image segmentation. (
See color plate section for the color representation of this figure.
)
Figure 7.2 (a) A seismic slice image showing a channel, (b) the slice after applying some preprocessing enhancement, and (c) the channel after segmentation.
Figure 7.3 (a) A seismic image showing a salt diapir, (b) the eigenvector of the amplitude envelope attribute, (c) the boundary detected between the salt diapir and the layers above it, and (d) the segmented salt diapir. (
See color plate section for the color representation of this figure.
)
Figure 7.4 Assuming that we are in the RGB space, our proposed constraint sets are (a) , which describes the set of all color vectors that are close to the given reference color vector within a sphere of radius , (b) , which describes the set of all color vectors that are statistically similar to the given reference color vector by a cone, where its correlation coefficient and is the angle between any vector and , and (c) the solution set , which includes all color vectors that are close and similar to the given reference color vector.
Figure 7.5 (a) A seismic image showing a salt diapir, (b) and (c) the two selected color classes, and (d) the segmented salt diapir. Note that the POCS parameters were , , and . (
See color plate section for the color representation of this figure.
)
Figure 7.6 (a) A seismic image showing a salt diapir, (b) the computed edges, (c) the boundary of the segmented salt diapir, and (d) the segmented salt diapir. There were three eigenvector images that were used to segment the salt diapir from the other layers. These are seen in (e)–(g). (
See color plate section for the color representation of this figure.
)
Figure 7.7 The basic principle of image segmentation for automatic fault detection. (
See color plate section for the color representation of this figure.
)
List of Tables
Chapter 2: Seismic Data Interpretation
Table 2.1 Types of structural traps
Table 2.2 Main types of stratigraphic traps
Table 2.3 Associations of some seismic forms with their corresponding depositional environments
Chapter 5: Seismic Attributes
Table 5.1 Algorithm for calculating the principal curvatures, Gaussian, and the mean curvatures
