Introduction

Multiples are often the most significant impediment to the successful construction and interpretation of an image of the primaries, especially in regions with anomalously strong reflectors (e.g., ``hard'' water bottom or salt bodies). But, since they penetrate deeply enough into the earth and illuminate different angular ranges and reflection points, a primary and its multiples are more than simply redundant.

The problem of handling multiples becomes more challenging when we encounter shallow water-bottom with a large impedence contrast. In such cases a large amount of multiple energy is generated, most of which is surface related. Offshore Australia is a prime example of such a marine environment. An important class of multiple-suppression techniques create from the data a ``model'' of the multiples, which may then be adaptively subtracted from the data. Verschuur and Prein (1999) use Surface-Related Multiple Elimination (SRME) for multiple-suppression on an Australian data set infected with shallow water bottom multiples. The multiple-generating boundaries were more or less horizontal or gently dipping. Long et al. (2005) use SRME to revisit a poor-data-quality area in the northern Carnarvon Basin, offshore Western Australia, where both short- and long-period multiple energy prohibit imaging of the underlying geology. In some sense, shallow-water multiple suppression is easier. The multiples look a lot like primaries, so deconvolution with a gapped filter (gap size = water-bottom time) will solve the problem nicely. However, some of the more advanced multiple suppression methods, like high-resolution Radon and SRME, have a trouble with shallow water-bottom. The Radon transform relies on moveout differences between multiples and primaries, which are not present in shallow data, except near the surface, and SRME often requires many non-linear iterations in shallow-water environments.

Brown (2004) introduces LSJIMP (Least-Squares Joint Imaging of Multiples and Primaries) for using information embedded in primaries and multiples simultaneously to enhance the signal-to-noise ratio and fill illumination gaps by averaging the images constructed from primaries and multiples. He has effectively demonstrated the use of his method on a variety of deep water-bottom data sets.

In this paper, we discuss implementation of LSJIMP on two shallow water-bottom synthetic data sets, for which we get mixed results. Some slight modifications have been made and we point out some improvements that might make this method perform better.