migration deconvolution vs least squares migration jianhua yu, gerard t. schuster university of utah
Post on 21-Dec-2015
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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 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
• 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
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