The weakness of the method presented
in this thesis is what it tries to overcome,
the non-linearity of tomography.  When we are far from the correct solution
and/or we have inappropriate view of the model covariance matrix, the
method is susceptible to non-convergence, or convergence to an
unreasonable model.
\par
The ray based tomography used in this thesis is limited
by the ray tracing high frequency approximation.
The high frequency approximation requires that the model
varies smoothly over a wavelength.  For the datasets presented in this
thesis that assumption is 
valid except at the salt edge.
In more complex environments  much more expensive
wave equation tomography 
methods \cite{Woodward.sepphd.62,Biondi.sep.100.biondo2} 
have the potential to do better under these adverse conditions.
\par
For the steering filters of Chapter~\ref{chap:reg}  to be effective,
they must adequately
describe the model covariance.  Often velocity does
not follow dip, and the velocity gradient for the region would need to be
used instead of the early migrated reflector position.
In addition, a steering filter might not describe a complex media's covariance.
For the frequencies used in reflection seismology practice
shows that velocity can be approximated
as a smoothly varying function, so steering filters are a valid representation
of the model's covariance.
\par
This thesis assumes that no anisotropy exists in the data. 
In theory, it is not hard to incorporate anisotropy into the velocity
estimation process presented in this thesis, in fact the method presented
has great potential for anisotropic estimation.  The reflector stability
of  the tau domain method would lead to much better ability to constrain
our $\eta$  \cite{GEO60-05-15501566} or equivalent anisotropic parameter.
In addition,
steering filters, with their ability to spread information along layers,
are ideal for defining and determining anisotropic layers.
