Least Squares MigrationLeast Squares Migration

Standard Migration:Standard Migration: mmmigmig=L=LTTdd

d=LmForward Model:Forward Model:

m =[Lm =[LTTL]L]-1-1LLTTddLeast Sq. Migration :Least Sq. Migration :

m =[Lm =[LTTL]L]-1-1m m migmig

Migration Decon:Migration Decon: 3D input3D input

5D input5D input

Motivation: Poor Acquisition GeomteryMotivation: Poor Acquisition Geomtery

**

Motivation: Poor IlluminationMotivation: Poor Illumination

gg

SALTSALT

Uneven Illumination under SaltUneven Illumination under Salt

X (km)

0 203

10

Dep

th (

km)

Wave Equation Migration Before MD

X (km)

0 203

10

Dep

th (

km)

Wave Equation Migration after MD

Motivation: Better ResolutionMotivation: Better Resolution

Beylkin

FFD MD

Gaussian Beam MD Kirchhoff MD

Kirchhoff Mig

Kirchhoff MDX (km)0

3

Y (km)

0

3

3

Y (km)

0

3

Y (km)

0 Kirchhoff MD

Kirchhoff Mig

Meandering Stream

Motivation: Better ResolutionMotivation: Better Resolution

Kirchhoff MD

Step 1:Step 1:

Step 2:Step 2:

Step 3:Step 3:

Step 4:Step 4:

Iterative Least Squares MigrationIterative Least Squares Migration

Kirchhoff MD

Kirchhoff MD

Kirchhoff MD

Kirchhoff MD

Kirchhoff MD

1. LSM resolution twice better than KM 1. LSM resolution twice better than KM

Summary

2. LSM >20 times more expensive than KM2. LSM >20 times more expensive than KM

3. LSM sensitive to accurate v(x,z)3. LSM sensitive to accurate v(x,z)

4. Multisource LSM costs same as KM4. Multisource LSM costs same as KM