Dongliang Zhang Xin Wang Yunsong Huang Gerard Schuster KAUST Outline Conclusions Motivation Flatten the common image gathers Numerical Example Test on Marmousi model and GOM data ID: 285139
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Slide1
Warping for Trim Statics
Dongliang
Zhang,
Xin
Wang,
Yunsong
Huang, Gerard Schuster
KAUSTSlide2
Outline
Conclusions
Motivation
Flatten the common image gathers
Numerical Example
Test
on
Marmousi
model and GOM data
Theory
Use warping to flatten the CSGs or warping between
prestack
imageSlide3
Outline
Conclusions
Motivation
Flatten the common image gathers
Numerical Example
Test
on
Marmousi
model and GOM data
Theory
Use warping to flatten the CSGs or warping between
prestack
imagesSlide4
Motivation
Image is distorted
Velocity Model with Errors Slide5
Motivation
Common Image
G
ather
Flattened CIG
Problem: After stacking the
unflattened
CIGs, the image is blurred
Solution: Before stacking, use warping to flatten the CIGsSlide6
Outline
Conclusions
Motivation
Flatten the common image gathers
Numerical Example
Test
on
Marmousi
model and GOM data
Theory
Use warping to flatten the CSGs or warping between
prestack
imageSlide7
Trace 2
Dynamic Warping
Trace 1
0
z
(km)
3.5
Space Shifts
-50 (m) 50Slide8
Pilot: Warping of CIGs
CIG
Flattened CIG
Vertical
S
pace Shifts
3.5
Z (km)
0
-60 Shifts (m) 80
Reference trace: summation of all traces of CIG; summation of part traces of CIG; one trace of CIG
pilotSlide9
Auto-pilot: Warping of
P
restack
Images
Vertical Space Shifts
Prestack
Image #15
Prestack
Image #16
-75 Shifts (m) 75Slide10
Auto-pilot: Warping of
P
restack
ImagesSlide11
Outline
Conclusions
Motivation
Flatten the common image gathers
Numerical Example
Test
on
Marmousi
model and GOM data
Theory
Use warping to flatten the CSGs or warping between
prestack
imageSlide12
1.5 km/s 5.5
True Velocity Model
CIG Warping
：
Marmousi
Model
3.5
Z (km)
0
1.5 km/s 5.5
Migration Velocity with Errors
0 X (km)
93.5 Z (km) 0Slide13
Warping of CIG #200
CIG
Flattened CIG
Vertical
S
pace Shifts
3.5
Z (km)
0
-60 Shifts (m) 80Slide14
Warping of CIG #450
CIG
Flattened CIG
Vertical
S
pace Shifts
3.5 Z
(km)
0
-100 Shifts (m) 100Slide15
Migration Image before warping
3.5
Z (km)
0
0 X (km) 9
CIG Warping:
Comparison of Images
Migration Image after warping
3.5
Z (km)
0 Slide16
Zoomed Views
Image before Warping
Image after Warping
Image before
W
arping
Image after WarpingSlide17
GOM Data
CIGs before Warping
CIGs after Warping
3.5
Z (km)
0
3.5
Z (km)
0 Slide18
GOM Data
Migration Image before Warping
Migration Image after Warping
3.5
Z (km)
0
0
X (km)
16
3.5
Z (km) 0 Slide19
Zoomed Views
Image before Warping
Image after Warping
Image before Warping
Image after WarpingSlide20
Warping of Prestack
Images:
GoM
Data
Vertical Space Shifts
Prestack
Image #15
Prestack
Image #16
-75 Shifts (m) 75Slide21
Comparison of Images
Migration Image before Warping
Migration Image after Warping
3.5
Z (km)
0
0
X (km)
16
3.5
Z (km) 0 Slide22
Zoomed Views
Image before Warping
Image after Warping
Image before Warping
Image after WarpingSlide23
Outline
Conclusions
Motivation
Flatten the common image gathers
Numerical Example
Test
on
Marmousi
model and GOM data
Theory
Use warping to flatten the CSGs or warping between
prestack
imagesSlide24
Conclusions
Signal-to-noise ratio is enhanced in the migration image
Some
structures are more
clearly delineated
Warping of prestack images changes image more than warping of CIGs
Position of the structures in the migration image may be still wrong
Warping for MVASlide25
Thank you!