Migration Deconvolution vs Least Squares Migration Jianhua Yu, Gerard T. Schuster University of Utah

Outline MotivationMotivation MD vs. LSMMD vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

Migration Noise Problems Footprint Migration noise and artifacts Time

Migration Problems Recording footprints Aliasing Limited resolution Amplitude distortion

Motivation Investigate MD and LSM: Improve resolution Suppress migration noise Computational cost Robustness

Outline MotivationMotivation MD vs. LSMMD vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

m = (L L ) L d TT Least Squares Migration Reflectivity Modeling operator Seismic data Migration operator

m = (L L ) L d TT Migration Deconvolution Reflectivity Modeling operator Migrated datam’

Solutions of MD Vs. LSM m = (L L ) L d TT LSM: T m = (L L ) m’ MD: Migrated image Data

I/O of 3-D MD Vs. LSM Huge volume LSM: Relative samll cube MD:

Outline MotivationMotivation MD Vs. LSMMD Vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

Numerical Tests Point Scatterer ModelPoint Scatterer Model 2-D SEG/EAGE overthrust model poststack MD and LSM2-D SEG/EAGE overthrust model poststack MD and LSM

Scatterer Model Krichhoff Migration Depth (km)

MDLSM Iter=10 Depth (km)

Depth (km) LSM Iter= LSM Iter=20

Point Scatterer ModelPoint Scatterer Model 2-D SEG/EAGE Overthrust Model Poststack MD and LSM2-D SEG/EAGE Overthrust Model Poststack MD and LSM Numerical Tests

KM Depth (km) X (km) LSM 15

KM Depth (km) X (km) MD

Depth (km) X (km) MD LSM 15

MD KM Depth (km) LSM Depth (km) Zoom View

Depth (km) Why does MD perform better than LSM ? 4.5 MD LSM 19 0 X (km)

Outline MotivationMotivation MD Vs. LSMMD Vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

Conclusions Efficiency MD >> LSMFunction Performanc e Resolution MD < LSM (?) Suppressing noise MD = LSM (?) Robustness MD < LSM

Acknowledgments Thanks UTAM ( sponsors for the financial supportThanks UTAM ( sponsors for the financial supporthttp://utam.gg.utah.edu