Kai Lu King Abdullah University of Science and Technology Outline Introduction and Motivation Theory conventional SVI with stationary phase integration S ynthetic data example Field data example ID: 384189
Download Presentation The PPT/PDF document "3D Super-virtual Refraction Interferomet..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
3D Super-virtual Refraction Interferometry
Kai Lu
King Abdullah University of Science and TechnologySlide2
Outline
Introduction and Motivation
Theory: conventional SVI with stationary phase integration
S
ynthetic data example
Field data example
Conclusion
AcknowledgementSlide3
Outline
Introduction and Motivation
Theory: conventional SVI with stationary phase integration
S
ynthetic data example
Field data example
Conclusion
AcknowledgementSlide4
B C
A3
dt
A2
A1
dt
dt
1.Stacked
Refractions:
+ Stacking
dt
B C
A
B C
Common Pair Gather
(Dong et al., 2006)
Benefit: SNR = N
d d
AB
AC
~ d
BC
A
virtual
~
2D Super-virtual Interferometry Slide5
2.
De
datum
Virtual Refraction to Known Surface Point
B C
B C
A
B C
A
=
*
=
*
+
d d
AB
AC
~ d
BC
A
src
virtual
real
super-virtual
d d
AB
BC
~ d
AC
B
rec
supervirtual
*
virtual
Raw trace
Virtual trace
(
Calvert+Bakulin
, 2004)
Super-virtual trace
B
rec
A
src
Datuming
De
datuming
2D Super-virtual Interferometry Slide6
2D Super-virtual Interferometry
Theory
and workflow:
Are first arrivals at far-offsets pickable ?
Window around first arrivals and mute near offset
Correlate and stack to generate virtual refractions
Input DataOutput Data
Convolve and stack to generate Super-virtual refractions
N
Ʃ
Ʃ
Raw Data
Super-virtual refraction Data
Windowed Data
Iterative SV
ISlide7
Difficulties from 2D to 3D
Difficulty to find locations of stationary sources and receivers
S
A
1
Unknown Path
Few sources and receiver available
A
2
Limited number of sources and receiversSlide8
Solution
2D: all traces are stationary
3D: stationary phase integration
A
B
Virtual Trace
Virtual Trace
S
1
S
2
S
3
S
n
• • •
S
*Slide9
Outline
Introduction and Motivation
Theory: conventional SVI with stationary phase integration
S
ynthetic data example
Field data example
ConclusionAcknowledgementSlide10
Stationary Phase Integration
Stationary phase analysis (
Bleistein
, 1984) applied to the line integral:
Applied to SVI:
Virtual trace AB
A
B
S
1
S
2
S
3
S
n
• • •
S
*Slide11
Cross-correlation Type
A
B
CRG A
CRG B
Cross-correlation Results
Ʃ
Correlation of S*A and S*B
Virtual trace AB
Source
1
180
Time (s)
0
4
Source
1
180
Source
1
180
Amplitude
-1
1
S
1
S
1
S
n
SnS*S*Slide12
Virtual Trace Stacking over Source Lines
A
B
S
1
S
2
S
3
S
n
lineN
line1
line2
2D: Stacking over sources:
3
D: Stacking over source lines:
C
A1
B
C
A1
B
C
B
=
A2
A2
A3
A3Slide13
Super Virtual Trace – Convolution Type
A
S
B
1
B
2
B
3Bn
lineNline1line2
2D: Stacking over receivers:
3
D: Stacking over receiver lines:
C
A
B1
C
A
B
C
B1
*
=
B2
B2
B3
B3Slide14
Workflow of 3D SVI
Window around
the targeted refraction
Generate virtual trace AB:
Input
Band-pass filtered Data
Output Data
Generate super-virtual trace SA:
Stack
generated from different sources
Stack
generated from different receiver lines
Iterative SV
ISlide15
Outline
Introduction and Motivation
Theory: conventional SVI with stationary phase integration
S
ynthetic data example
Field data example
ConclusionAcknowledgementSlide16
S
ynthetic Test – Undulating Layer Model
•
•
•
V
1
=1500m/s
V
2
=3000m/s
151 receivers, 76 sources on every line
11 survey linesSlide17
Line1
Line11
S
ynthetic Result
•
•
•
Original data
Data with random noise
Super-virtual refraction
Iterative Super-virtual Refraction
T
race
Time (s)
0
3
1151TraceTime (s)031
151
Trace
Time (s)031151TraceTime (s)
031151Slide18
Outline
Motivation: from 2D to 3D
Theory: conventional SVI with stationary phase integration
S
ynthetic data example
Field data example
ConclusionAcknowledgementSlide19
x [km]
y [km]
2
14
-2
18
3D OBS Survey Geometry
19
400 m
50 m
50 m
5 m
Sihil
3D OBS data
234 OBS stations
129 source-lines
Irregular geometry.
Map viewSlide20
Field Results 1
Raw data
Band-pass filtered data
Super-virtual result
T
race
Time (s)
0
4
1
361
T
race
Time (s)
0
4
1361TraceTime (s)041
361Slide21
Zoom View Comparison
Zoom view of band-pass filtered
d
ata
Zoom view of super-virtual
d
ata
TraceTime (s)131
361TraceTime (s)1
3
1361
Zoom view of super-virtual data
U
npickableSlide22
Field Results 2
Raw data
Super-virtual result
Iterative Super-virtual
result
T
race
Time (s)041
361TraceTime (s)
04
1361
Trace
Time (s)
0
41361Slide23
Zoom View Comparison
Raw data
Super-virtual result
Iterative Super-virtual
result
T
race
Time (s)041361TraceTime (s)
041
361Trace
Time (s)
04
1
361
U
npickable
UnpickableSlide24
Outline
Introduction and Motivation
Theory: conventional SVI with stationary phase integration
S
ynthetic data example
Field data example
ConclusionAcknowledgementSlide25
Conclusion
We apply
stationary phase integration method
to achieve super-virtual refraction with enhanced SNR in 3D cases.
Iterative method is an option to further improve SNR when super-virtual refraction is still noisy.
A
rtifacts can be produced because of the limited aperture for integration as well as a coarse spacing of sources or receivers.Slide26
Thank you !