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A spatial prediction filter can take the form of a dip filter.
Using linear least squares we can estimate the coefficients
(*a*,*b*,*c*,*d*,*e*) in the 2D filter
`
a
b
1 c
d
e
`

Fitting the filter to two neighboring traces
that are identical but for a time shift,
the filter (*a*,*b*,*c*,*d*,*e*) should turn out to be
the negative of an interpolation filter.
Ideally you might see
(-1,0,0,0,0) or
(0,0,-.5,-.5, 0).
But if the two channels are not fully coherent you expect to see
something like
(-.9,0,0,0,0) or
(0,0,-.4,-.4,0).

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Stanford Exploration Project

1/13/1998