Transcript Multiple attenuation in the image space

Multiple attenuation
in the image space
Paul Sava & Antoine Guitton
Stanford University
SEP
paul@sep.stanford.edu
Goal
• Method feasible in 3-D
• Less expensive
• Dense data requirement
• Exploit the data/imaging mismatch
• Data:
two-way propagation
• Migration: one-way extrapolation
paul@sep.stanford.edu
Key technology
• Migration by wavefield extrapolation (WEM)
• Angle-domain common-image gathers
• High resolution Radon Transforms
paul@sep.stanford.edu
The big picture
Image
Image
RT & Mute
S/N separation
WE migration & ADCIG
RT & Mute
S/N separation
NMO
WE prediction
Data
Data
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Multiple attenuation by RTs
– Moveout analysis
• NMO
– Moveout analysis
• WE migration
– S/N separation
– S/N separation
• RT + Mute
• RT + Mute
paul@sep.stanford.edu
3-D depth imaging
• WE migration
• Kirchhoff migration
– Multi-arrival
– Single-arrival
• Angle-gathers
• Offset-gathers
– Single-valued
– Multi-valued
Biondi et al. (2003)
Stolk & Symes (2002)
y
x
g
g
z
paul@sep.stanford.edu
Synthetic example: data vs. image
CMP
CIG
paul@sep.stanford.edu
Which Radon transform?
zq, g   q  g g 
g
Parabolic
g g   g
Tangent
g g   tan g 
q
g(g)
Generic
Radon Transform
2
2
Biondi & Symes (2003)
z
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Synthetic example: RTs
Parabolic
g g   g
2
Tangent
g g   tan g 
2
paul@sep.stanford.edu
Synthetic example: S/N separation
primaries &
multiples
ART
ART + mute
multiples
primaries
paul@sep.stanford.edu
BP synthetic example
paul@sep.stanford.edu
BP synthetic example
primaries &
multiples
ART
multiples
primaries
paul@sep.stanford.edu
BP synthetic example: stacks
primaries &
multiples
multiples
primaries
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GOM example
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GOM example: CIG 1
primaries &
multiples
ART
ART + mute
multiples
primaries
paul@sep.stanford.edu
GOM example
paul@sep.stanford.edu
GOM example: CIG 2
primaries &
multiples
ART
ART + mute
multiples
primaries
paul@sep.stanford.edu
GOM example
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GOM example: zoom 1
primaries & multiples
paul@sep.stanford.edu
GOM example: zoom 1
primaries
paul@sep.stanford.edu
GOM example: zoom 1
primaries & multiples
paul@sep.stanford.edu
GOM example: zoom 1
multiples
paul@sep.stanford.edu
GOM example
paul@sep.stanford.edu
GOM example: zoom 2
primaries & multiples
paul@sep.stanford.edu
GOM example: zoom 2
primaries
paul@sep.stanford.edu
GOM example: zoom 2
primaries & multiples
paul@sep.stanford.edu
GOM example: zoom 2
multiples
paul@sep.stanford.edu
RT comparison
Image space RT
Data space RT
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Discussion
• PROs
– Cheap & robust
– 3-D
– Simple primaries
– Migration artifacts
• CONs
– Velocity model?
– Moveout function?
– Interactive mute
– Inner angles
– RT artifacts
paul@sep.stanford.edu
Summary
Image
Image
RT & Mute
S/N separation
WE migration & ADCIG
RT & Mute
S/N separation
NMO
WE prediction
Data
Data
paul@sep.stanford.edu
Summary
• Multiple attenuation after migration
• WE migration
• Angle gathers
• Cost/accuracy
• Complex propagation
• Cheap separation
• RT limitations
• filtering approach
paul@sep.stanford.edu