Least Squares Kirchhoff Depth Migration: potentials, challenges and its relation to interpolation

Least squares migration (LSM), like interpolation, has the potential to address sampling issues and generate images with better amplitudes than migration. Although both techniques share the same goal and often the same formulation, they differ on the nature of the model that is used to predict the data. For LSM the cost of the migration/modelling operator bring limitations. In this abstract I discuss some of these limitations, possible ways to overcome them, and analyse some similarities and differences with respect to interpolation. Also I present results using a LSM implementation for Kirchhoff Depth Migration for both PP and PS data.