Least-squares joint imaging of multiples and primaries applied to 3-D field data

Morgan Brown

morgan@sep.stanford.edu

ABSTRACT

In this paper I outline the extension to 3-D of the Least-squares Joint Imaging of Multiples and Primaries (LSJIMP) method for simultaneously separating multiples and primaries and combining their information. I apply LSJIMP to a 3-D field dataset and demonstrate that the method cleanly removes surface-related multiples from the data while preserving the prestack amplitude signature of the primaries. LSJIMP compares favorably to least-squares Radon demultiple, both in terms of computational performance and result quality.