migration deconvolution vs. least squares migration
DESCRIPTION
Migration Deconvolution vs. Least Squares Migration. Jianhua Yu University of Utah. Outline. Motivation MD vs. LSM Numerical Tests Conclusions. Amplitude distortion. Footprint. Migration noise and artifacts. Migration Noise Problems. Limited Resolution. Migration Problems. Aliasing. - PowerPoint PPT PresentationTRANSCRIPT
Migration Deconvolution vs. Least Squares
Migration
Jianhua YuUniversity of Utah
OutlineOutline• MotivationMotivation
• MD vs. LSMMD vs. LSM
• Numerical TestsNumerical Tests
• ConclusionsConclusions
Migration Noise ProblemsMigration Noise Problems
Migration noise and artifacts
Footprint Amplitude distortion
Migration ProblemsMigration Problems
AliasingAliasing
Limited ResolutionLimited Resolution
MotivationMotivation
Investigate MD and LSM:
Improving resolution
Suppressing migration noiseComputational cost
Robustness
OutlineOutline• MotivationMotivation
• MD vs. LSMMD vs. LSM
• Numerical TestsNumerical Tests
• ConclusionsConclusions
m = (m = (L L L L )) L L ddTTTT -1
Least Squares Migration
Reflectivity
Modeling operator
Seismic data
Migration operator
TTmm = ( = (L LL L ) m’ ) m’
-1-1
ReflectivityReflectivity
MD deblurring operator
Migration SectionMigration Section
Migration Deconvolution
Solutions of MD vs. LSMSolutions of MD vs. LSM
m = (m = (L L L L )) L L ddTTTT -1LSM:
TTmm = ( = (L LL L ) ) mm’’
-1-1 MD:
Migrated image
Data
OutlineOutline• MotivationMotivation
• MD vs. LSMMD vs. LSM
• Numerical TestsNumerical Tests
• ConclusionsConclusions
Numerical TestsNumerical Tests
• Point Scatterer ModelPoint Scatterer Model
• 2-D SEG/EAGE overthrust model 2-D SEG/EAGE overthrust model poststack MD and LSMpoststack MD and LSM
Scatterer Model Kirchhoff MigrationD
epth
(k
m)
1.8
01.00 1.00
MD LSM Iter=15D
epth
(k
m)
1.8
01.00 1.00
• Point Scatterer ModelPoint Scatterer Model
• 2-D SEG/EAGE Overthrust Model 2-D SEG/EAGE Overthrust Model Poststack MD and LSMPoststack MD and LSM
Numerical TestsNumerical Tests
KM
Dep
th (
km
)
4.5
00 7.0
0 7.0
X (km)
X (km)
4.5
0
LSM 10
KM
Dep
th (
km
)
4.5
00 7.0
0 7.0
X (km)
X (km)
4.5
0
LSM 15
KM
Dep
th (
km
)
4.5
00 7.0
0 7.0
X (km)
X (km)
4.5
0
MD
Dep
th (
km
)
4.5
00 7.0
0 7.0
X (km)
X (km)
4.5
0
MD
LSM 15
LSM 15
MD
KM2
3.5
Dep
th (
km
)
LSM 192
3.5
Dep
th (
km
)Zoom View
Dep
th (
km
)
4.5
00 7.0
Why does MD perform better than LSM ?
4.5 MD
LSM 19
0
X (km)
OutlineOutline• MotivationMotivation
• MD vs. LSMMD vs. LSM
• Numerical TestsNumerical Tests
• ConclusionsConclusions
ConclusionsConclusions
Efficiency MD >> LSM
FunctionFunction PerformancPerformanceeResolutionResolution MD = LSMMD = LSM
.
Suppressing noise MD > LSM
Robustness MD < LSM
AcknowledgmentsAcknowledgments
• Thanks to 2001 UTAM sponsors Thanks to 2001 UTAM sponsors for their financial supportfor their financial support