Application of Least-Squares Joint Imaging of Multiples and Primaries on Shallow Water-Bottom Data Sets

Madhav Vyas and Morgan Brown

mvyas@stanford.edu

ABSTRACT

Data contaminated with strong shallow water-bottom multiples is rife with challenges. Application of Least-Squares Joint Imaging of Multiples and Primaries (LSJIMP) on such data sets yields mixed results. LSJIMP solves both the separation and integration simultaneously, as a global least-squares inverse problem. We point out some limitations of LSJIMP by testing it on synthetic data sets that emulate shallow water-bottom marine environments. Some slight modifications have been made, and we suggest some strategies that might make LSJIMP an effective algorithm.