160 likes | 414 Views
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.
E N D
d=Lm Forward Model: mmig=LTd Standard Migration: m =[LTL]-1LTd Migration Decon: Least Sq. Migration : m =[LTL]-1m mig 5D input 3D input
Motivation: Poor Illumination * g SALT Uneven Illumination under Salt
Wave Equation Migration Before MD 0 X (km) 20 3 Depth (km) 10
Wave Equation Migration after MD X (km) 0 20 3 Depth (km) 10
Motivation: Better Resolution Kirchhoff Mig Beylkin Kirchhoff MD Gaussian Beam MD FFD MD
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
Kirchhoff MD Iterative Least Squares Migration Step 1: Step 2: Step 3: Step 4:
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