%In recent reports I introduced the concept of a steering filter
%\cite{Clapp.sep.95.bob1}.  
%Since then it has been used to regularize a wide variety of
%geophysical problems including interpolation \cite{Crawley.sep.104} , 
%multiple attenuation \cite{Clapp.sep.103.bob2,Clapp.sep.102.bob2}
%missing data problems \cite{Clapp.sep.95.bob1} ,
%migration \cite{Prucha.sep.103.marie1} , and tomography \cite{Clapp.sep.97.bob2,Clapp.sep.100.bob1,Clapp.sep.103.bob1}.
%\par
%Each of these problems used a 2-D steering filter. In  \longcite{Fomel.sep.102.sergey3}
%a method to construct a 3-D steering filter was introduced.  Fomel \shortcite{Fomel.sep.102.sergey3}
%formed a 3-D steering filter operator by first
%convolving two 2-D operators.
%To obtain a minimum-phase filter
%he performed expensive spectral factorization for
%each dip component ($p_x$ , $p_y$) pair in the data.  
%In order for the resulting filter to spread information over
%significant distances a large number of filter coefficients must be used
%increasing the cost of each iteration.
%\par
In chapter~\ref{chap:reg} I showed how to build a 2-D steering filter.
In this appendix 
 show how
a 3-D steering filter can be produced by cascading two 2-D steering
filters. The cascaded approach does not provide as accurate
a dip discrimination as the  approach taken
in \longcite{Fomel.sep.102.sergey3} but it doesn't require  expensive
spectral  factorization and the resulting filters are much smaller and
less expensive to apply.
I show how this method can accurately characterize a
large range of dips and that is accurate enough
for a wide class of applications.  In addition I apply it to a simple synthetic
missing data problem with very promising results.
