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