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![]() | Seismic data interpolation without iteration using ![]() ![]() ![]() | ![]() |
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We created a 3D prestack dataset (Figure 6a) from
a 2D slice out of the benchmark French model (French, 1974), and
the data was subsampled by a factor of two in both offset and shot
axes, which caused visible aliasing of dipping events. Furthermore, we
removed
of randomly selected traces from the decimated
data. The data interleaved with zero traces along the offset and shot
directions is shown in Figure 6b. The challenge
of this test was to account for nonstationarity, aliasing, both
decimated and irregular missing traces, and computational cost.
Figure 7a and
Figure 7b display the interpolated result
using 3D Fourier POCS and the conventional 3D
-
-
SPF,
respectively. Notably, the Fourier POCS method can only recover
randomly missing traces, and it fails in handling regularly missing
traces. For the proposed 3D
-
-
SPF, the choices of the filter
length were seven samples in time axis, nine samples in the offset
axis, and three samples in the shot axis. We designed the scale
parameters,
,
,
, and
, to deal with the variability of
events. The conventional 3D
-
-
SPF did not recover the decimated
data well. However, the proposed method succeeded in interpolating
irregular and regular missing traces simultaneously
(Figure 7c), which produced reasonable results
for curved events. The CPU times of the 3D Fourier POCS with 500
iterations and the 3D
-
-
SPF were 889.21 s and 33.72 s,
respectively.
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amiss,zero3
Figure 6. (a) 3D synthetic prestack data and (b) missing data interleaved with zero traces. |
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pocsqd,adds,add
Figure 7. Reconstructed data volumes using different methods. (a) The 3D Fourier POCS, (b) the conventional 3D ![]() ![]() ![]() ![]() ![]() ![]() |
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![]() | Seismic data interpolation without iteration using ![]() ![]() ![]() | ![]() |
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