  Inverse Problem techniques offer powerful tools which deal naturally with
marginal data and asymmetric or strongly smoothing kernels, in cases where
parameter-fitting methods may be used only with some caution. Although they are
typically subject to some bias, they can invert data without requiring one to
assume a particular model for the source. The Backus-Gilbert method in
particular concentrates on the tradeoff between resolution and stability, and
allows one to select an optimal compromise between them. We use these tools to
analyse the problem of reconstructing features of the source star in a
microlensing event, show that it should be possible to obtain useful
information about the star with reasonably obtainable data, and note that the
quality of the reconstruction is more sensitive to the number of data points
than to the quality of individual ones.
