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para,npara,tpefpatch,fxpatch,npre,npar
Figure 4. (a) Synthetic shot gather. (b) Noisy gather. (c) Result of ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
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fxdiff,mpapatch,ndiff
Figure 5. Difference sections of ![]() ![]() ![]() ![]() ![]() ![]() |
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Figure 4(a) shows a synthetic shot gather with four hyperbolic events,
501 traces. Some random noise is added to this gather. We do not use windows in time
for this example. For -
RNA, the length of filter is
and the smoothing radiuses in
space and frequency axes are respectively 20 and 3,
,
. The
-
domain prediction
is implemented over a sliding window of 20 traces width with 50% overlap and the filter length is 6,
and
the
-
domain prediction is implemented over the same sliding window and the filter length in space and
time are 6 and 5 respectively. The estimated nonstationary coefficients by the proposed
-
RNA are shown
in Figure 4(f). Note that the middle coefficient is bigger than the sideward, which is because the dip of
the middle is smaller than the sideward. The results of three methods are shown in Figures 4(d)- 6(d),
respectively. The
-
RNA achieves a similar result to
-
domain and
-
domain prediction methods.
However, we use equation 11 to compute the SNRs of the results of three methods. The SNRs of three
methods are 0.98 dB, 1.25 dB, 1.67 dB, respectively. The
-
RNA can improve SNR more greatly. The
-
RNA solves the nonstationary case by allowing the coefficients smoothly varying, while
-
domain
or
-
domain prediction method uses windowing strategies. From the difference sections (Figure 7(a)- 5(c)), we
find that
-
domain and
-
domain prediction methods damage more signals than
-
RNA. If we use
windows in time for this example, we can obtain better results. This example shows that
-
RNA can
be used for random noise attenuation in shot gather.
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