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Figure shows stacked sections from the
multiple-infested zone of the CGG subset before and after application of LSJIMP.
Figure is in the same format, but shows a zoom
of the multiple-infested region. CMP stacking strongly suppresses the
multiples, but from the difference panel, notice that LSJIMP has nonetheless
subtracted most of the remaining surface-related multiple energy, and has
preserved the stronger primaries to a great extent. The timeslice on the 3-D
cube transects the seabed pegleg from reflector R1; it shows up prominently on
the raw data stack, as well as on the difference panel, but has been largely
suppressed from the LSJIMP estimated primaries stack.
stackcomp3d.gc3d
Figure 4
Stacked subset (192 midpoints inline, 14 midpoints crossline) of CGG 3-D data
before and after LSJIMP. All panels windowed in time from 3.8 to 6.0 seconds
and gained with t2. Left: Raw data stack. Center: Stack of estimated
primary image, . Right: Stack of the subtracted multiples. Naming
convention for pure first-order multiples: (reflector)M, e.g., R1M.
Naming convention for first-order pegleg multiples:
(target)PL(multiple generator), e.g., R1PLWB.
stackcomp3d.zoom.gc3d
Figure 5
Zoom of stacked subset of CGG 3-D data before and after LSJIMP. All panels
windowed in time from 4.0 to 5.0 seconds. Left: Raw data stack. Center: Stack
of estimated primary image, . Right: Stack of the subtracted
multiples.
Figures and
illustrate LSJIMP's performance on two
individual CMP gathers extracted from different portions of the CGG 3-D data.
It is in this domain where the strength of the LSJIMP method shines most. The
raw data panels show strong surface-related multiples with an onset of around
4.3 seconds, and also fairly strong primary events under the curtain of
multiples. The LSJIMP estimated primaries in panel (b) are effectively free of
multiples, and moreover, since the data residual panel (f) barely contains any
noticeable flat primary energy, we have preserved the primary events. Also
notice that the data residual contains little structured energy. This implies
that the LSJIMP forward model accurately models the primaries and important
multiples in the data. Unfortunately, much of this ``unstructured'' energy
likely belongs to fairly weak pegleg multiples that simply appear incoherent
with the data's poor inline resolution. On Figure
, notice that cable feathering has caused
missing traces at far offsets. LSJIMP has used the data's multiplicity and
model constraints to reasonably extrapolate the missing traces.
compwind.lsrow.gc3d.100.4
Figure 6
LSJIMP results on individual midpoint location (CMPx=100,CMPy=4). All panels
decimated in offset by a factor of 6 and NMO'ed with stacking velocity, for
display purposes. Panel (a): Raw data. Panel (b): LSJIMP estimated
primaries. Panels (c) and (d): Estimated seabed and R1 pegleg multiples.
Panel (e): Modeled data (sum of panels (b), (c), and (d)). Panel (f): Data
residual (difference of panels (a) and (e)), with residual weight applied.
compwind.lsrow.gc3d.100.12
Figure 7
LSJIMP results on individual midpoint location (CMPx=100,CMPy=12). All panels
decimated in offset by a factor of 6 and NMO'ed with stacking velocity, for
display purposes. Panel (a): Raw data. Panel (b): LSJIMP estimated
primaries. Panels (c) and (d): Estimated seabed and R1 pegleg multiples.
Panel (e): Modeled data (sum of panels (b), (c), and (d)). Panel (f): Data
residual (difference of panels (a) and (e)), with residual weight applied.