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.
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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.