Migration Deconvolution vs. Least Squares Migration

Jianhua Yu University of Utah

Outline

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Motivation

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MD vs. LSM

•

Numerical Tests

•

Conclusions

Migration Noise Problems

Footprint Amplitude distortion Migration noise and artifacts

Migration Problems

Aliasing Limited Resolution

Motivation

Investigate MD and LSM:

Improving resolution Suppressing migration noise Computational cost Robustness

Outline

•

Motivation

•

MD vs. LSM

•

Numerical Tests

•

Conclusions

Least Squares Migration

T m = ( L L )

-1

T L d

Reflectivity Modeling operator Seismic data Migration operator

Migration Deconvolution

m T = ( L L Reflectivity Migration Section

MD deblurring operator

Solutions of MD vs. LSM

LSM: MD:

T m = ( L L )

-1

T L d m T = ( L L -1 ) m ’

Data Migrated image

Outline

•

Motivation

•

MD vs. LSM

•

Numerical Tests

•

Conclusions

Numerical Tests

•

Point Scatterer Model

•

2-D SEG/EAGE overthrust model poststack MD and LSM

1.8

0 0 Scatterer Model 1.0

0 Kirchhoff Migration 1.0

1.8

0 0 MD 1.0

0 LSM Iter=15 1.0

Numerical Tests

•

Point Scatterer Model

•

2-D SEG/EAGE Overthrust Model Poststack MD and LSM

0 0 4.5

0 0 4.5

X (km) 7.0

X (km) KM 7.0

LSM 10

0 0 4.5

0 0 4.5

X (km) 7.0

X (km) KM 7.0

LSM 15

0 0 4.5

0 0 4.5

X (km) 7.0

X (km) KM 7.0

MD

0 0 4.5

0 0 4.5

X (km) 7.0

X (km) LSM 15 7.0

MD

2 KM Zoom View LSM 15 3.5

2 LSM 19 3.5

MD

Why does MD perform better than LSM ?

0 0 X (km) 7.0

LSM 19 4.5

0 4.5

MD

Outline

•

Motivation

•

MD vs. LSM

•

Numerical Tests

•

Conclusions

Conclusions

Function Resolution Performanc e

.

MD = LSM Efficiency MD >> LSM Suppressing noise MD > LSM Robustness MD < LSM

Acknowledgments

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Thanks to 2001 UTAM sponsors for their financial support