In marine environments, three-dimensional reflection seismic data is normally acquired in a so-called ``wide tow'' streamer configuration, illustrated in Figure . Ironically, the crossline offset range of this data is less than with most land acquisition geometries, so geophysicists often call towed streamer data ``narrow azimuth'' data. Note that the crossline shot interval, , is chosen such that the outermost receiver line on one swath overlaps the innermost receiver line on the previous swath. Figure illustrates that such an acquisition geometry produces a regularly sampled crossline CMP axis, if cable feathering is absent. In one sense, this geometry boasts some degree of optimality, as it produces a well-sampled 3-D image at a minimum cost.
Unfortunately, geometry shown in Figure causes the 3-D extension of the SRME method of multiple prediction to fail spectacularly. SRME requires to be relatively small-in practice, roughly the same as commonly chosen crossline receiver line spacing parameters van Borstelen (2003). 3-D field datasets commonly have a crossline shot interval of up to ten times the crossline receiver line spacing. Workarounds for the 3-D sampling problem include: ignoring crossline structure and using a 2-D prediction, massive (270,000 CPU hours) shot interpolation Kleemeyer et al. (2003), sparse inversion of the crossline multiple contribution gathers Hokstad and Sollie (2003); van Dedem and Verschuur (2002), and novel acquisition geometries Paffenholz (2003). Currently, none of these methods combines proven accuracy with computational/cost efficiency.
The LSJIMP method has good potential to separate 3-D peglegs from wide tow marine data. In Section , I demonstrated how, in a fairly complex 2-D setting, the HEMNO equation can model some complex multiples as accurately as SRME. In this chapter, I outline a practical extension of my implementation of LSJIMP to work on 3-D wide tow marine data.