Test case for PEF estimation with sparse data II |

Given a pure plane wave section, i.e., a wavefield where all events have linear
moveout, designing a discrete multichannel filter to annihilate events with a given
dip seems a trivial task. In fact, it is quite a tricky task; an exercise in
interpolation. For many applications, accuracy considerations make the choice of
interpolation algorithm critical. *Accuracy* here means localization of the
filter's dip spectrum -- ideally the filter should annihilate only the desired
dip, or a narrow range of dips.

steering
Steering filter schematic. Given a plane wave
with dip , choose the to best annihilate the plane wave.
Figure 3. | |
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The problem is illustrated in Figure 3. Given a plane wave with dip , we must set the filter coefficients to best annihilate the plane wave. Achieving good dip spectrum localization implies a filter with many coefficients, by the uncertainly principle (Bracewell, 1986). If computational cost was not an issue, the best choice would be a sinc function with as many coefficients as time samples. Realistically, however, a compromise must be found between pure sinc and simple linear interpolation. The reader is referred to (Fomel, 2000) paper, which discusses these issues much more thoroughly. The model of Figure 1 was computed using an 8-point tapered sinc function. Figure 4 compares the result of using, for the same task, dip filters computed via four different interpolation schemes: 8-point tapered sinc, 6-point local Lagrange, 4-point cubic convolution, and simple 2-point linear interpolation. As expected, we see that the more accurate interpolation schemes lead to increased spatial coherency in the model panel. Clapp (2000) has been successful in using as few as 3 coefficients in steering filters for regularizating tomography problems, so we see that the needed amount of steering filter accuracy is a problem-dependent parameter.

interp-comp
Interpolation schemes compared.
Deconvolution of random image with labeled steering filters.
Figure 4. |
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Test case for PEF estimation with sparse data II |

2016-03-17