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Least Squares Migration

Least Squares Migration. d=Lm. Forward Model:. m mig =L T d. Standard Migration:. m =[L T L] -1 L T d. Migration Decon:. Least Sq. Migration :. m =[L T L] -1 m. mig. 5D input. 3D input. Motivation: Poor Acquisition Geomtery. Motivation: Poor Illumination. *. g. SALT.

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Least Squares Migration

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  1. Least Squares Migration

  2. d=Lm Forward Model: mmig=LTd Standard Migration: m =[LTL]-1LTd Migration Decon: Least Sq. Migration : m =[LTL]-1m mig 5D input 3D input

  3. Motivation: Poor Acquisition Geomtery

  4. Motivation: Poor Illumination * g SALT Uneven Illumination under Salt

  5. Wave Equation Migration Before MD 0 X (km) 20 3 Depth (km) 10

  6. Wave Equation Migration after MD X (km) 0 20 3 Depth (km) 10

  7. Motivation: Better Resolution Kirchhoff Mig Beylkin Kirchhoff MD Gaussian Beam MD FFD MD

  8. 0 0 0 Y (km) Y (km) Y (km) 3 3 3 Kirchhoff MD Motivation: Better Resolution 3 0 X (km) Meandering Stream Kirchhoff Mig Kirchhoff MD

  9. Kirchhoff MD Iterative Least Squares Migration Step 1: Step 2: Step 3: Step 4:

  10. Kirchhoff MD

  11. Kirchhoff MD

  12. Kirchhoff MD

  13. Kirchhoff MD

  14. Kirchhoff MD

  15. Summary 1. LSM resolution twice better than KM 2. LSM >20 times more expensive than KM 3. LSM sensitive to accurate v(x,z) 4. Multisource LSM costs same as KM

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