Download
Super-virtual Interferometric Diffractions as Guide Stars - PowerPoint PPT Presentation
- 59 Views
- Uploaded on
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 Slideshow about ' Super-virtual Interferometric Diffractions as Guide Stars' - rosine
An Image/Link below is provided (as is) to download presentationDownload Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.
While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
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
0.5
0.5
Time (s)
Time (s)
1.5
1.5
Offset (km)
0
Offset (km)
2
0
2
0.5
Picked Traveltimes
Predicted Traveltimes
Time (s)
Estimate statics
1.0
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
0.6
Time (s)
0.9
1.0
180
280
Depth (m)
0
300
Depth (m)
Time Windowed
0.6
Time (s)
Median Filter
0.9
Depth (m)
180
280
Super-virtual Diffractions
0.6
0.6
Time (s)
Time (s)
0.9
0.9
Depth (m)
Depth (m)
180
180
280
280
Median Filtered
0.6
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
0
Depth (km)
1.2
0
Distance (km)
3.8
True Velocity
0
Depth (km)
1.2
0
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.
