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![]() | Seismic data interpolation beyond aliasing using regularized nonstationary autoregression | ![]() |
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Irregular gaps occur in the recorded data for many different reasons,
and prediction-error filters are known to be a powerful method for
interpolating missing data. Missing data interpolation is a particular
case of data regularization, where the input data are already given on
a regular grid, and one needs to reconstruct only the missing values
in empty bins (Fomel, 2001). One can use existing traces to
directly estimate adaptive PEF coefficients instead of scaling the
filter as in regular trace interpolation problem. However, finding the
adaptive PEF needs to avoid using any regression equations that
involve boundaries or missing data. This can be achieved by creating
selection mask operator
, a diagonal matrix with ones at the
known data locations and zeros elsewhere, for both causal translations
and input data (Claerbout, 2010).
Analogously to the stationary prediction-error filter
(1), adaptive PEF coefficients
use the
unscaled format and appear as
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![]() | Seismic data interpolation beyond aliasing using regularized nonstationary autoregression | ![]() |
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