\def\figdir{./Figs}
To see the power of a non-stationary filter to characterize model covariance,
let   's return to the fault model missing
data problem  (Figure~\ref{fig:fault-model}).
For our interpolation we
will use the known model as the basis for our PEF, and have a micro-patch
size of one sample in $x$ and $z$.
The resulting interpolation, Figure~\ref{fig:fault.cont}, isn't
quite as high-frequency as our initial model and we have a minimal
amount of continuation of the layers over the fault, but we generally
do an excellent job recovering the model.  

\activesideplot{fault.cont}{width=3.0in,height=3.0in}{ER}{The result of finding
non-stationary PEF using the correct model and then applying it to
the missing data problem. The returned image is almost exact and does
a better job with bed discontinuities than in Figure~\ref{fig:fault-model}.}
