Migration Deconvolution vs Least Squares Migration
Jianhua Yu, Gerard T. Schuster
University of Utah
OutlineOutline• MotivationMotivation
• MD vs. LSMMD vs. LSM
• Numerical TestsNumerical Tests
• ConclusionsConclusions
Migration Noise ProblemsMigration Noise Problems
Footprint
Migration noise and artifacts
Tim
e
Migration ProblemsMigration Problems
Recording footprintsRecording footprints
AliasingAliasing
Limited resolutionLimited resolution
Amplitude distortionAmplitude distortion
MotivationMotivation
Investigate MD and LSM:
Improve resolution
Suppress 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
m = (m = (L L L L )) L L ddTTTT -1
Migration Deconvolution
Reflectivity
Modeling operator
Migrated data
m’m’
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
I/O of 3-D MD Vs. LSMI/O of 3-D MD Vs. LSM
Huge volumeHuge volume LSM:
Relative samll cubeRelative samll cube MD:
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 Krichhoff MigrationD
epth
(k
m)
1.8
01.00 1.00
MD LSM Iter=10D
epth
(k
m)
1.8
01.00 1.00
Dep
th (
km
)
1.8
01.00
LSM Iter=151.00
LSM Iter=20
• 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 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 < LSM (?)MD < LSM (?)
Suppressing noise MD = LSM (?)
Robustness MD < LSM
AcknowledgmentsAcknowledgments
• Thanks UTAM (Thanks UTAM (http://utam.gg.utah.edu) sponsors for the financial support
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