Migration Deconvolution versus Least Squares Migration

Both migration deconvolution (MD) and least squares migration (LSM) are capable of improving the resolution and suppress acquisition footprints in migrated images. In this report, I investigate the relative performance of these two methods in enhancing migration image quality, suppressing artifacts and computational efficiency. Both MD and LSM were implemented on synthetic data generated from point scatterer and the SEG/EAGE Overthrust models. The results indicate that both MD and LSM improves the energy focusing and illumination equality of the migration images. LSM sharpens the reflection events with increasing iterations at the cost of more than a 10-fold increase in CPU time. In comparison, MD is also able to improve the spatial resolution of migration images but at a low computation cost. In addition, MD performs better in attenuating migration noise compared with the LSM method.