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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.
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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
Introduction
Super-virtual stacking theory
Synthetic data examples
Field data examples
Summary
Diffracted energy contains valuable information about the subsurface structure.
- Goal: extract diffractions from seismic data and enhance its SNR.
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
Introduction
Super-virtual stacking theory
Synthetic data examples
Field data examples
Summary
dt
dt
=
dt
dt
w2
w1
w3
y z
y z
y z
y’
y’
Benefit: SNR = N
Convolution to restore diffractions
x
y z
y z
x
y z
=
*
y’
x
y z
y z
x
y z
=
*
y’
x
Stacking Over Geophone LocationDesired shot/
receiver combination
Common raypaths
Benefit: SNR = N
1. Crosscorrelate and stack to generate virtual diffractions
w z
w z
w z
=
Virtual src
excited at -tzz’
z’
2. Convolve and stack to generate super-virtual diffractions
w z
w z
*
=
z
Benefit: SNR = N
Raw data
dt
Select a master trace
dt
Cross-correlate to generate virtual diffractions
=
Repeat for all the shots and stack the result to give virtual diffractions
dt
Convolve the virtual diffractions with the master trace
=
*
Stack to generate Super-virtual Diffractions
Introduction
Super-virtual stacking theory
Synthetic data examples
Field data examples
Summary
Synthetic Shot Gather: Fault Model
Windowed Data
0.5
0
Z (km)
Time (s)
3
0
X (km)
6
1.5
Our Method
Median Filter
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Time (s)
Time (s)
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1.5
Offset (km)
0
Offset (km)
2
0
2
0.5
Picked Traveltimes
Predicted Traveltimes
Time (s)
Estimate statics
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Offset (km)
2
0
Introduction
Super-virtual stacking theory
Synthetic data examples
Field data examples
Summary
0.6
0.3
Time (s)
0.9
180
280
Depth (m)
Time (s)
Picked Moveout
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Time (s)
0.9
1.0
180
280
Depth (m)
0
300
Depth (m)
Time Windowed
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Time (s)
Median Filter
0.9
Depth (m)
180
280
Super-virtual Diffractions
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0.6
Time (s)
Time (s)
0.9
0.9
Depth (m)
Depth (m)
180
180
280
280
Median Filtered
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0.3
Time (s)
0.9
180
280
Depth (m)
Time (s)
Super-virtual Diffraction
0.6
Time (s)
0.9
1.0
180
280
Depth (m)
0
300
Depth (m)
0
Time (s)
Born
Modeling
4.0
Distance (km)
0
3.8
Velocity
0
Depth (km)
1.2
Reflectivity
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Depth (km)
1.2
0
Distance (km)
3.8
True Velocity
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Depth (km)
1.2
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Distance (km)
3.8
Initial Velocity
Inverted Velocity
0
0
Depth (km)
Depth (km)
1.2
1.2
Estimated Reflectivity
0
Depth (km)
1.2
0
Distance (km)
3.8
Introduction
Super-virtual stacking theory
Synthetic data examples
Field data examples
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).
We thank the sponsors of CSIM consortium for their financial support.
