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PRESTACK TARGET-ORIENTED LEAST SQUARES MIGRATION. Zhiyong Jiang and Min Zhou. Geology and Geophysics Department University of Utah. Outline. Least Squares Migration Prestack Target-Oriented LSM Prestack Numerical Examples Conclusions. Standard Migration. LSM. d=Lm. d=Lm.

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slide1

PRESTACK TARGET-ORIENTED LEAST SQUARES MIGRATION

Zhiyong Jiang and Min Zhou

Geology and Geophysics Department

University of Utah

slide2

Outline

  • Least Squares Migration
  • Prestack Target-Oriented LSM
  • Prestack Numerical Examples
  • Conclusions
slide3

Standard Migration

LSM

d=Lm

d=Lm

mmig=LTd

mmig=[LTL]-1LTd

m : reflectivity model

mmig: migration image

d : observed data

L : forward operator

slide4

A Simple Model

Distance (km)

1

0

0

Depth (Km)

1.2

True Reflectivity Model

slide5

Common Shot Gathers

Distance (km)

Distance (km)

1

1

0

0

0

Time (s)

1

CSG 51

CSG 101

slide6

Distance (km)

1

0

0

Depth (Km)

1.2

Standard LSM-35

Standard Migration vs. LSM

Distance (km)

1

0

0

Depth (Km)

1.2

Standard Migration

slide7

Limitation of LSM

Requires 10 or more iterations,where each iteration requires several forward modeling and migration steps

Impractical to implement for current 3-D prestack imaging of large data sets

slide8

Outline

  • Least Squares Migration
  • Prestack Target-Oriented LSM
  • Prestack Numerical Examples
  • Conclusions
slide9

Target-Oriented Prestack LSM

Prestack LSM

Data

Model

slide10

Answer:

Smaller Model Fast Convergence

Why faster convergence &

better resolution?

Simple Model Revisited

Distance (km)

Distance (km)

1

1

0

0

0

Depth (Km)

1.2

Target Oriented LSM-20

Standard LSM-35

unocal poststack data 2003
UNOCAL POSTSTACK DATA, 2003

Distance (km)

0

14.4

a

0.8

D (Km)

1.5

0.8

b

D (Km)

1.5

(a) Standard Migration (b) TO LSM-5

unocal poststack data 20031
UNOCAL POSTSTACK DATA, 2003

Distance (km)

0

14.4

a

0.8

D (Km)

1.5

0.8

b

D (Km)

1.5

(a) Standard Migration (b) TO LSM-10

slide13

Outline

  • Least Squares Migration
  • Prestack Target-Oriented LSM
  • Prestack Numerical Results
  • Conclusions
slide14

MOBIL MARINE DATA

X (m)

10,356

22,856

0

V (m/s)

3643

Depth (m)

1500

3500

Velocity Model

slide15

X (m)

X (m)

10,356

22,856

10,356

22,856

500

Depth (m)

2500

Kirchhoff Migration

Standard LSM-6

slide16

X (m)

10,356

22,856

1000

Depth (m)

Kirchhoff

1800

1000

Standard LSM-6

Depth (m)

1800

slide17

Trace NO.

Trace NO.

0

120

0

120

0

Time (s)

Field Data CSG 600

Modeled Data CSG 600

6

slide18

X (m)

10,356

22,856

1000

Depth (m)

Kirchhoff

1800

1000

Target Oriented LSM-3

Depth (m)

1800

slide19

X (m)

10,356

22,856

1000

Standard LSM-6

Depth (m)

1800

1000

Target Oriented LSM-3

Depth (m)

1800

slide20

Outline

  • Least Squares Migration
  • Prestack Target-Oriented LSM
  • Prestack Numerical Results
  • Conclusions
conclusion

LSM is effective in suppressing the migration artifacts, but expensive

Prestack Target-oriented LSM improves the image quality in a few iterations

Prestack Target-oriented LSM greatly reduces CPU time

Conclusion
future work
Future Work

Eliminate the noise

Implement preconditioning and regularization for LSM

Test the poststack/prestack target-oriented LSM on 3-D data

slide23

Acknowledgements

We thank Utah Tomography and

Modeling/Migration Consortium

sponsors for their financial support

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