Optimizing Seismic Attributes Interpretation by Shifting Hue Channel in the HSV Color Space to Match Human Color Spectral Sensitivity

1. Optimizing Seismic Attributes
Interpretation by Shifting Hue Channel
in the HSV Color Space to Match
Human Color Spectral Sensitivity
Awal Mandong

2. 2
• Introduction to Spectral 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 retrieved from https://www.pxfuel.com/en/free-photo-xcepr
Camouflage:
The Art of Disguising to Blend with Surrounding

4. 4
• Introduction to Spectral 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 Decomposition is 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 0 10 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 Spectral Decomposition
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 purpose of
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 to Spectral 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 interference of 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 workflow on
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 spectrum response 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 over 10m 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 to Spectral 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 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 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 RGB components
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 Decomposition and
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 RGB Blend 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 to Spectral 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 challenging situation 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
In the 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 spectrum can 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. 26
Geo

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 of HSV 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 of HSV 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 of HSV 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 of HSV 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 of HSV 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 of HSV 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 of HSV 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 or Hue 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 to Spectral 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 feature might 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 from Frequency 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 to Spectral 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 blending of 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. Thank You
Awal.Mandong@geosoftware.com