  We present an algorithm for focusing inversion of electrical resistivity
tomography (ERT) data. ERT is a typical example of ill-posed problem.
Regularization is the most common way to face this kind of problems; it
basically consists in using a priori information about targets to reduce the
ambiguity and the instability of the solution. By using the minimum gradient
support (MGS) stabilizing functional, we introduce the following geometrical
prior information in the reconstruction process: anomalies have sharp
boundaries. The presented work is embedded in a project (L.A.R.A.) which aims
at the estimation of hydrogeological properties from geophysical
investigations. L.A.R.A. facilities include a simulation tank (4 m x 8 m x 1.35
m); 160 electrodes are located all around the tank and used for 3-D ERT.
Because of the large number of electrodes and their dimensions, it is important
to model their effect in order to correctly evaluate the electrical system
response. The forward modelling in the presented algorithm is based on the
so-called complete electrode model that takes into account the presence of the
electrodes and their contact impedances. In this paper, we compare the results
obtained with different regularizing functionals applied on a synthetic model.
