The 3D SRME method performs well when sampling and data-conditioning requirements are met (Dragoset et al., 2010). However, 3D SRME faces several challenges in shallow-water surveys. The two main challenges specific to such surveys are the reconstruction of seismic traces at offsets smaller than the nearest offset and the adaptive subtraction of the predicted multiples. For the purposes of our discussion, we will consider as “shallow water” the areas in a survey where water depth is less than about 200 m. For typical acquisition geometries, such water depths correlate broadly with the transition from successful to marginal results when processing data with standard 3D SRME.
In shallow-water surveys, methods based on wavefield extrapolation are often successful (Lokshtanov, 2001; Verschuur, 2012; Wang et al., 2014) in removing a class of high-amplitude multiples that has bounces (e.g., reflections, refractions, diffractions) on the water bottom. Figure 1 provides decompositions of free-surface-related and water-layer-related multiples into subsets, including the subset of multiples modeled by wavefield-extrapolation methods (water-layer multiples with bounces in the water layer on the shot or receiver side, i.e., WLM-SL-LS, with the notation introduced in Figure 1).
Moore and Bisley (2006) propose to follow the attenuation of WLM-SL-LS multiples with modeling of free-surface multiples that are not related to the water layer (FSM-LL in Figure 1a). The approach of Moore and Bisley (2006), known as deterministic water-layer demultiple (DWD), extends significantly the class of free-surface multiples being attenuated, but it leaves in the data multiples of the WLM-LSL type (van Groenestijn et al., 2012).