Download to read offline
![12
1,2,...
N
with
notch
of
Frequency
1
-
N
thickness
Time
Periodical sequence of notches from a simple
thin-bed layer (wedge model). Note the
interference on the amplitude spectrum of the
wedge.
V-shaped amplitude notches occur where
frequency is equal to 1/time-thickness of the
layer
Adopted from Partyka et. al, 1999 (TLE)
Thickness increase 0 – 40 [ms]
Frequency
[Hz]
Application: Interference Pattern](https://image.slidesharecdn.com/optimizingseismicattributesinterpretationusinghsvrotationppt-230505094416-fb131b6c/75/Optimizing-Seismic-Attributes-Interpretation-using-HSV-Rotation_PPT-pdf-12-2048.jpg)
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Similar to Optimizing Seismic Attributes Interpretation using HSV Rotation_PPT.pdf
Optimizing Seismic Attributes Interpretation using HSV Rotation_PPT.pdf
- 1. Optimizing Seismic Attributes Interpretationby Shifting Hue Channel in the HSV Color Space to Match Human Color Spectral Sensitivity Awal Mandong
- 2. 2 • Introduction toSpectral Decomposition. • Application of Spectral Decomposition: Feature delineation using RGB blending. • Discontinuity detection on Spectral Decomposition result and the use RGB blending to aid interpretation. • HSV Rotation or Hue Color Shift. How to aid interpretation by optimizing eye sensitivity. • Highlighting and Delineating Feature. • Summary. Outline
- 3. 3 Images are retrievedfrom https://www.pxfuel.com/en/free-photo-xcepr Camouflage: The Art of Disguising to Blend with Surrounding
- 4. 4 • Introduction toSpectral Decomposition. • Application of Spectral Decomposition: Feature delineation using RGB blending. • Discontinuity detection on Spectral Decomposition result and the use RGB blending to aid interpretation. • HSV Rotation or Hue Color Shift. How to aid interpretation by optimizing eye sensitivity. • Highlighting and Delineating Feature. • Summary. Outline
- 5. 5 • Spectral Decompositionis a process to decompose a signal into constituent sines and cosines components. • The frequency spectrum of a recorded seismic signal is affected by several factors such as energy absorption, tuning etc. • Since a particular geological features may be better recognized with a specific frequency band, visualizing these features becomes easier with RGB blending of different frequency volumes. • RGB blending of Spectral Decomposition results has been very popular in seismic interpretation especially in delineating geological features or lithology such as thin channel sands, carbonate, etc. • The use of discontinuity attribute aid the interpretation using Spectral decomposition results. • Further more, high quality as well as robust interpretation can be achieved by matching the color to the most sensitive range of human eye sensitivity. Introduction to Spectral Decomposition
- 6. 6 Seismic Section 010 20 80 90 100 Hz Frequency Time (ms) Time (ms) Time (ms) The Spectral Decomposition decompose a seismic trace spectrum and returns an array one dimension larger than the input data. The spectral decomposition of one seismic trace results one time-frequency gather. The signal frequencies evolution during the duration of the signal due to the effect of energy loss or tuning thickness can be analyzed using this Frequency gathers. Introduction to Spectral Decomposition
- 7. 7 Time Introduction to SpectralDecomposition The Spectral Decomposition decompose a seismic trace spectrum and returns an array one dimension larger than the input data. The spectral decomposition of one seismic trace results one time-frequency gather. The signal frequencies evolution during the duration of the signal due to the effect of energy loss or tuning thickness can be analyzed using this Frequency gathers.
- 8. 8 For the purposeof visualization and interpretation (i.e. RGB blending), the Time- Frequency gather is re- sorted as iso-frequency volumes. Displaying the iso- frequency volumes can be very useful to analyze and interpret the change of amplitude across different frequency. Time Introduction to Spectral Decomposition
- 9. 9 • Introduction toSpectral Decomposition. • Application of Spectral Decomposition: Feature delineation using RGB blending. • Discontinuity detection on Spectral Decomposition result and the use RGB blending to aid interpretation. • HSV Rotation or Hue Color Shift. How to aid interpretation by optimizing eye sensitivity. • Highlighting and Delineating Feature. • Summary. Outline
- 10. 10 Analyzing the interferenceof the amplitude spectrum using long and short time window: Long time window Short time window Adopted from Partyka et. al, 1999 (TLE) Application: Interference Pattern
- 11. 11 Application: Interference Pattern Amplitude spectrum of a thin-bed layer: – The thin-bed response includes reflectivity overprint – The actual temporal thickness can be estimated from the interference in the spectrum Adopted from Partyka et. al, 1999 (TLE)
- 12. 12 1,2,... N with notch of Frequency 1 - N thickness Time Periodical sequence of notches from a simple thin-bed layer (wedge model). Note the interference on the amplitude spectrum of the wedge. V-shaped amplitude notches occur where frequency is equal to 1/time-thickness of the layer Adopted from Partyka et. al, 1999 (TLE) Thickness increase 0 – 40 [ms] Frequency [Hz] Application: Interference Pattern
- 13. 13 Commonly used workflowon advanced seismic interpretation using spectral decomposition and RGB blending Proposed extended workflow on advanced seismic interpretation using spectral decomposition and RGB blending Input: Seismic Data Perform Spectral Decomposition and assign 3 frequencies to each color channel. Perform RGB blending using 3 color channels (3 frequencies). Perform RGB blending using 3 color channels (3 discontinuities). Perform discontnuity / edge detection on each frequency. Perform HSV Rotation / Hue Color shift on the RGB blending result Perform analysis and interpretation on both frequencies and discontinuities from 0 to 359 degree. Make note on optimum rotation angle which gives optimum separation of particular feature. Perform feature highlighting / delineation and perform body capture on interpreted feature based on feature color (Hue value) or another attribute. This step is optional. Workflow: Advanced Seismic Interpretation using RGB Blending
- 14. 14 The Time-Frequency spectrumresponse is affected by energy absorption and tuning thickness. Therefore, optimum selection of frequencies may dellineate certain geological feature (i.e. thin channel sand) or lithology. Application: Frequency Selection for RGB Blending
- 15. 15 High Low Color Scale Mean P-Impedance over10m window Mean Effective Porosity over 10m window RGB Blending P-Impedance Section Mean Vp/Vs over 10m window Vp/Vs Section Effective Porosity Section Application: RGB Blending Using Properties
- 16. 16 • Introduction toSpectral Decomposition. • Application of Spectral Decomposition: Feature delineation using RGB blending. • Discontinuity detection on Spectral Decomposition result and the use RGB blending to aid interpretation. • HSV Rotation or Hue Color Shift. How to aid interpretation by optimizing eye sensitivity. • Highlighting and Delineating Feature. • Summary. Outline
- 17. 17 • Since humaneye has limitation on separating certain color (i.e. features), discontinuity / edge detection on each color channel volume may delineate the particular feature or lithology better compared to the conventional RGB blending. • Horizon Edge Stacking was used to identify edges or features in the input amplitude volume of each color channel. • The Horizon Edge Stacking results then used as RGB blending input to get better visual separation of the features of lithology. • Changing the frequency combination could help the interpretation. Discontinuity Detection Using Horizon Edge Stacking
- 18. 18 Blend the RGBcomponents Perform Spectral Decomposition and assign an Iso-Frequency Volume to each RGB component Input: Seismic Data Red Channel: 20 Hz Green Channel: 30 Hz Blue Channel: 40 Hz Note: the frequency selection is just an example Conventional RGB Blending Workflow
- 19. 19 Perform Spectral Decompositionand assign an Iso-Frequency Volume to each RGB component Blend the new RGB components Input: Seismic Data Red Channel: 20 Hz Green Channel: 30 Hz Blue Channel: 40 Hz Note: the frequency selection is just an example Perform Discontinuity/ Edge Detection (Horizon Edge Stacking) on each RGB component Edges on Red Channel: 20 Hz Edges on Green Channel: 30 Hz Edges on Blue Channel: 40 Hz RGB Blending of Discontinuity Data
- 20. 20 Seismic data RGBBlend from 20 30 40 Hz Iso Freq Volume Discontinuity from Seismic data Discontinuity from 20 30 40 Hz Iso Freq Volume Since human eye has limitation on separating certain color (i.e. features), discontinuity / edge detection on each color channel volume may delineate the particular feature or lithology better compared to the conventional RGB blending. Horizon Edge Stacking can be used to identify edges or features in the input amplitude volume of each color channel. The Horizon Edge Stacking results then used as RGB blending input to get better visual separation of the features of lithology. Changing the frequency combination could help the interpretation. Discontinuity from 25 35 45 Hz Iso Freq Volume Discontinuity from 30 40 50 Hz Iso Freq Volume RGB Blend from 25 35 45 Hz Iso Freq Volume RGB Blend from 30 40 50 Hz Iso Freq Volume Frequency Selection on RGB Blending
- 21. 21 • Introduction toSpectral Decomposition. • Application of Spectral Decomposition: Feature delineation using RGB blending. • Discontinuity detection on Spectral Decomposition result and the use RGB blending to aid interpretation. • HSV Rotation or Hue Color Shift. How to aid interpretation by optimizing eye sensitivity. • Highlighting and Delineating Feature. • Summary. Outline
- 22. 22 • A challengingsituation is when two geological features show up in two different colors which are difficult for human eye to discriminate such as blue- cyan-violet. • If the spectrum can be shifted to the colors where the human eye is sensitive most such as yellow or green, the geological features can be distinguished easier. • Transforming the three frequencies volume from RGB to HSV (Hue- Saturation-Value) color space and shift the color spectrum greatly improves the seismic interpretation. HSV Rotation or Hue Color Shift
- 23. 23 Human vision sensitivity.Adapted from Spectral ray tracing by J. C. Cecilia Zhang, Ashwinlal Sreelal. 2016. Generally, human eye is more sensitive to green, yellow, and red color, and less sensitive to blue, cyan and purple color. Changing the native color of certain feature to the color where the human eye is the most sensitive will greatly improve the discrimination of the features. RGB Blending of Constant Frequency which is commonly used to interpret geological feature (i.e. channels or faults). RGB Blending and Human Eye Sensitivity
- 24. 24 RGB color model Inthe RGB color model, a color is determined by combination / blending the 3 basic colors. In the HSV color model, a color is determined dominantly by hue value (i.e. in degree azimuth where 0 or 360 is red.) Images are retrieved from https://en.wikipedia.org/wiki/HSL_and_HSV RGB component RGB to HSV HSV component HSV component RGB and HSV Color Space
- 25. 25 If the spectrumcan be shifted to the colors where the human eye is sensitive most, the geological features can be distinguished easier. Note that certain feature can be distinguished easier in certain color. HSV Rotation or Hue Color Shift
- 26.
- 27. 27 Hor izon Edge Stack Seismic Data Slice on 2100 ms Hor Edge Stack RGB Blend Spec Decomp RGB blend on 2100 ms The use ofHSV color spectral shift / rotation on Spectral Decomposition results and it’s discontinuity attributes dramatically improve the interpretability. Note that some features can be delineated better. Conventional Seismic Data RGB Blend of Frequency Data HSV Rotation or Hue Color Shift
- 28. 28 Hor izon Edge Stack Seismic Data Slice on 2100 ms The use ofHSV color spectral shift / rotation on Spectral Decomposition results and it’s discontinuity attributes dramatically improve the interpretability. Note that some features can be delineated better. Conventional Seismic Data RGB Blend of Frequency Data Hor Edge Stack RGB Blend Spec Decomp RGB blend on 2100 ms HSV Rotation or Hue Color Shift
- 29. 29 Hor izon Edge Stack Seismic Data Slice on 2100 ms The use ofHSV color spectral shift / rotation on Spectral Decomposition results and it’s discontinuity attributes dramatically improve the interpretability. Note that some features can be delineated better. Conventional Seismic Data RGB Blend of Frequency Data Hor Edge Stack RGB Blend Spec Decomp RGB blend on 2100 ms HSV Rotation or Hue Color Shift
- 30. 30 Hor izon Edge Stack Seismic Data Slice on 2100 ms The use ofHSV color spectral shift / rotation on Spectral Decomposition results and it’s discontinuity attributes dramatically improve the interpretability. Note that some features can be delineated better. Conventional Seismic Data RGB Blend of Frequency Data Hor Edge Stack RGB Blend Spec Decomp RGB blend on 2100 ms HSV Rotation or Hue Color Shift
- 31. 31 Hor izon Edge Stack Seismic Data Slice on 2100 ms The use ofHSV color spectral shift / rotation on Spectral Decomposition results and it’s discontinuity attributes dramatically improve the interpretability. Note that some features can be delineated better. Conventional Seismic Data RGB Blend of Frequency Data Hor Edge Stack RGB Blend Spec Decomp RGB blend on 2100 ms HSV Rotation or Hue Color Shift
- 32. 32 Hor izon Edge Stack Seismic Data Slice on 2100 ms The use ofHSV color spectral shift / rotation on Spectral Decomposition results and it’s discontinuity attributes dramatically improve the interpretability. Note that some features can be delineated better. Conventional Seismic Data RGB Blend of Frequency Data Hor Edge Stack RGB Blend Spec Decomp RGB blend on 2100 ms HSV Rotation or Hue Color Shift
- 33. 33 Hor izon Edge Stack Seismic Data Slice on 2100 ms The use ofHSV color spectral shift / rotation on Spectral Decomposition results and it’s discontinuity attributes dramatically improve the interpretability. Note that some features can be delineated better. Conventional Seismic Data RGB Blend of Frequency Data Hor Edge Stack RGB Blend Spec Decomp RGB blend on 2100 ms HSV Rotation or Hue Color Shift
- 34. 34 Horizon Edge Stack Seismic Slice HSV Rotation orHue Color Shift Conventional Seismic Data 0 degree 60 degree 120 degree 180 degree 240 degree 300 degree Horizon Edge Stack Spectral Decomposition RGB Blending RGB blending
- 35. 35 • Introduction toSpectral Decomposition. • Application of Spectral Decomposition: Feature delineation using RGB blending. • Discontinuity detection on Spectral Decomposition result and the use RGB blending to aid interpretation. • HSV Rotation or Hue Color Shift. How to aid interpretation by optimizing eye sensitivity. • Highlighting and Delineating Feature. • Summary. Outline
- 36. 36 Since certain featuremight be associated with certain color, it is possible to highlight the feature by masking the other feature associated with other color. This can be done by lowering the saturation to make it displayed in greyscale, lowering the value to make it displayed darker, or lowering the transparency. The use of an attribute as 4th volume can also be used to help highlighting, Delineating, and Body Capturing certain feature Highlighting and Delineating Features
- 37. 37 Negative Positive Negative Positive RGB Blending fromFrequency data Single Constant Phase data Validation with Well data Note that the sand A1 and sand A2 can be separated. Final RGB Blending. Enhanced. Sand A1 Sand A2 Sand A1 Sand A2 Muliani, Ratih et. al., 2018, New Approach: Using Relative Inversion With Spectral Decomposition to Distinguish Thin Layers in The 33-Series Sand Reservoirs of The Widuri Field, Southeast Sumatra, Indonesia. 42nd IPA Annual Convention & Exhibition. Highlighting and Delineating Features
- 38. 38 • Introduction toSpectral Decomposition. • Application of Spectral Decomposition: Feature delineation using RGB blending. • Discontinuity detection on Spectral Decomposition result and the use RGB blending to aid interpretation. • HSV Rotation or Hue Color Shift. How to aid interpretation by optimizing eye sensitivity. • Highlighting and Delineating Feature. • Summary. Outline
- 39. 39 • RGB blendingof Spectral Decomposition results has been popular in seismic interpretation especially in delineating geological features or lithology such as thin channel sands and carbonate build ups. • Since some geological features may show up in different frequencies, visualizing these in RGB blending will facilitate easier delineation of those features. • Transforming the RGB to HSV color spectrum and shifting it to other colors where the human eye is the most sensitive would make the delineation of the geological features much easier and robust. • Furthermore, since certain geological feature may be associated with certain color, it is possible to highlight it by masking the other interfering geological feature associated to other color by manipulating the Hue, Saturation and Value. Summary
- 40.