The problem with this approach is that our
regularization operator is multi-dimensional.
Inverting the operator
requires a little trick that % image processors
%have known for a long time but
has only been recently been used
in geophysics
\cite{helix}.  Given a multi-dimensional filter,
such as Figure \ref{fig:small-filter}, you first
map it onto the coordinate space of the data (panel {\it a} of 
Figure~\ref{fig:helix}). Then imagine wrapping the data around a cylinder with the end of column one
connected the beginning of column two.  Finally, unwind the data into a single
string of numbers and you have converted your multi-dimensional filter into
a 1-D filter!
If this new, one-dimensional
filter is causal and minimum phase, which steering filters
are, you can apply the inverse of the filter cheaply through
polynomial division \cite{fgdp}.
\plot{helix}{height=3.0in}{ Filtering on a helix.
        The same filter coefficients overlay the same data values
        if the 2-D coils are unwound into 1-D strips. This figure
was taken from \longcite{Fomel.sep.95.sergey2}.
}
