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Test results for leveled inverse interpolation

Figures [*] and [*] show the same example as in Figures [*] and [*]. What is new here is that the proper PEF is not given but is determined from the data. Figure [*] was made with a three-coefficient filter (1,a1,a2) and Figure [*] was made with a five-coefficient filter (1,a1,a2,a3,a4). The main difference in the figures is where the data is sparse. The data points in Figures [*], [*] and [*] are samples from a sinusoid.

 
subsine390
Figure 26
Interpolating with a three-term filter. The interpolated signal is fairly monofrequency.

subsine390
[*] view burn build edit restore

 
subsine590
Figure 27
Interpolating with a five term filter.

subsine590
[*] view burn build edit restore

Comparing Figures [*] and [*] to Figures [*] and [*] we conclude that by finding and imposing the prediction-error filter while finding the model space, we have interpolated beyond aliasing in data space.


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Next: Analysis for leveled inverse Up: LEVELED INVERSE INTERPOLATION Previous: LEVELED INVERSE INTERPOLATION
Stanford Exploration Project
4/27/2004