super-virtual interferometric diffractions as guide stars
DESCRIPTION
Super-virtual Interferometric Diffractions as Guide Stars. Wei Dai 1 , Tong Fei 2 , Yi Luo 2 and Gerard T. Schuster 1. 1 KAUST 2 Saudi Aramco. Feb 9 , 2012. Outline. Introduction Super-virtual stacking theory Synthetic data examples Field data examples Summary. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
Super-virtual Interferometric Diffractions as Guide Stars
Wei Dai1, Tong Fei2, Yi Luo2 and Gerard T. Schuster1
1 KAUST 2 Saudi Aramco
Feb 9, 2012
Outline• Introduction
• Super-virtual stacking theory
• Synthetic data examples
• Field data examples
• Summary
Introduction• Diffracted energy contains valuable information
about the subsurface structure.• Goal: extract diffractions from seismic data and
enhance its SNR.
Previous Work• Reciprocity equation of correlation and convolution
types (Wapenaar et al., 2004).
• Diffracted waves detection (Landa et al., 1987)
• Diffraction imaging (Khaidukov et al.,
2004;Vermeulen et al., 2006; Taner et al., 2006; etc)
Flip
Guide Stars
Outline• Introduction
• Super-virtual stacking theory
• Synthetic data examples
• Field data examples
• Summary
Step 1: Virtual Diffraction Moveout + Stacking
y zw3
dt
w2 w1 y z
y’
dt
dt
dt
w
y z
y’
=
Benefit: SNR = N
Step 2: Dedatum virtual diffraction to known surface position
y z
y’
y zx y zx
=*
Convolution to restore diffractions
y zx
=
y z
y’
y zx
*
Stacking Over Geophone Location
x zDesired shot/
receiver combination
Common raypaths
Benefit: SNR = N
Super-virtual Diffraction Algorithm
=w z
1. Crosscorrelate and stack to generate virtual diffractions
w
w z w z
Virtual srcexcited at -tzz’ z’
Benefit: SNR = N
=*2. Convolve and stack to generate super-virtual diffractions
w z w z
w
z
WorkflowRaw data
Select a master trace
Cross-correlate to generate virtual diffractions
Repeat for all the shots and stack the result to give virtual diffractions
Convolve the virtual diffractions with the master trace
Stack to generate Super-virtual Diffractions
dt
dt
dtw=
=*
Outline• Introduction
• Super-virtual stacking theory
• Synthetic data examples
• Field data examples
• Summary
Synthetic Results: Fault Model
0 X (km) 6
0Z
(k
m)
3
3.4
1.8
km/s
Synthetic Shot Gather: Fault Model
0 Offset (km)
2
0Ti
me
(s)
3
Diffraction
Shot at Offset 0.2 km
Synthetic Shot Gather: Fault Model
0.5
Tim
e (s
)1.
5
Windowed Data
0 Offset (km) 2
0.5
Tim
e (s
)1.
5
Median Filter
0 Offset (km) 2
Our Method
0.5
Tim
e (s
)1.
5
0 X (km) 6
0Z
(k
m)
3
Estimation of Statics
0 Offset (km) 2
0.5
Tim
e (s
)1.
0
Picked Traveltimes
Predicted Traveltimes
Estimate statics
Outline
• Introduction
• Super-virtual stacking theory
• Synthetic data examples
• Field data examples
• Summary
Experimental Cross-well Data
0 Depth (m) 300
0.3
Tim
e (s
)1.
0
180 Depth (m) 280
0.6
Tim
e (s
)0.
9
Picked Moveout0.
6Ti
me
(s)
0.9
180 Depth (m) 280
Experimental Cross-well Data
180 Depth (m) 280
0.6
Tim
e (s
)0.
9
180 Depth (m) 280
0.6
Tim
e (s
)0.
9
Median Filter
Time Windowed
180 Depth (m)
0.6
Tim
e (s
)0.
9
280
Super-virtual Diffractions
Experimental Cross-well Data
0 Depth (m) 300
0.3
Tim
e (s
)1.
0
180 Depth (m) 280
0.6
Tim
e (s
)0.
9
Super-virtual Diffraction0.
6Ti
me
(s)
0.9
Median Filtered
180 Depth (m) 280
Diffraction Waveform Modeling
Born
Modeling
0 Distance (km) 3.8
0D
epth
(km
)1.
20
Dep
th (k
m)
1.2
0Ti
me
(s)
4.0
0 Distance (km) 3.8
Velocity
Reflectivity
Diffraction Waveform Inversion
0 Distance (km) 3.8
0D
epth
(km
)1.
20
Dep
th (k
m)
1.2
Initial Velocity
Estimated Reflectivity
0D
epth
(km
)1.
2
Inverted Velocity
0 Distance (km) 3.8
0D
epth
(km
)1.
2
True Velocity
Outline• Introduction
• Super-virtual stacking theory
• Synthetic data examples
• Field data examples
• Summary
Summary• Super-virtual diffraction algorithm can greatly improve
the SNR of diffracted waves..
Limitation• Dependence on median filtering when there are other coherent
events.• Wavelet is distorted (solution: deconvolution or match filter).
Acknowledgments
We thank the sponsors of CSIM consortium for their financial support.