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Figure a shows the estimated primaries when the norm
is used to compute the shaping filters. Figure b displays
the estimated internal multiples.
As expected, because of the local amplitude
differences between the signal (primaries) and the noise (multiples), the
adaptive subtraction fails and we retrieve the behavior explained in the preceding
section with the 1-D example. Now, in Figure , we see the
beneficial effects of the norm. Figure a shows
the estimated primaries and Figure b the estimated
multiples. The noise subtracted matches very well the
internal multiple model in Figure b, as anticipated.
Note that with this dataset, had to be changed to
.
**comp-diffL1L2
**

Figure 9 (a) The difference between the
exact multiples (Figure b) and the subtracted
multiples with the norm. (b) The difference between the
exact multiples (Figure b) and the subtracted
multiples with the norm.

As a final comparison, Figure displays the
difference between the internal-multiple model (Figure
b) and the subtracted multiples with the two norms. The
norm (Figure b) matches the multiple
model much better than the norm (Figure
a).

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** Up:** 2-D data example: attenuation
** Previous:** Adaptive filtering with non-stationary
Stanford Exploration Project

5/5/2005