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It is appealing to think of interpolating land data, because land data
can be so expensive to acquire. Land data is more difficult to interpolate
than marine data, however, because it tends to be much more noisy.
We can guess that the PEFs will find the predictable part of the data,
and that it will be the signal.
However, even assuming we do estimate the correct PEF (one that
captures the dips of the signal rather than the noise), energy from
the noise will be carried along those dips to nearby interpolated traces.
As an alternative, we can attempt to separate the signal from the noise
while interpolating the missing traces. Taking the theory from
Claerbout 1997,
data can be decomposed into
known plus missing parts,
.With known and unknown data selectors and
,
write the data as
| |
(6) |
where
is zero-padded known data and
all the components of are freely adjustable.
The goal is to fill in the appropriate parts of .
The data
can also be decomposed into
signal plus noise,
.Thus
| |
(7) |
If we write fitting goals for signal and noise and then get rid of the noise
with equation (7) we have
| |
(8) |
| (9) |
Solving (8) and (9) gives estimates for the
signal component of both the
known and missing data, as well as an estimate of the noisy missing
traces, .
This requires a signal predictor and a noise predictor.
For the signal predictor I use the PEFs estimated from the data.
In the example to follow I will throw out alternating midpoint
gathers and attempt to interpolate them back, so I define
noise as whatever is incoherent across midpoints and
choose the noise predictor to be an average in a small window
along the midpoint axis.
Next: Example
Up: Crawley : Nonstationary filters
Previous: 2-D or 3-D
Stanford Exploration Project
4/20/1999