Focusing inversion techniques applied to electrical resistance tomography in an experimental tank

摘要

We present an algorithm for focusing inversion of electrical resistivitytomography (ERT) data. ERT is a typical example of ill-posed problem. Regularization is themost common way to face this kind of problems; it basically consists in using a prioriinformation about targets to reduce the ambiguity and the instability of the solution. By usingthe minimum gradient support (MGS) stabilizing functional, we introduce the followinggeometrical 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 ofhydrogeological properties from geophysical investigations. L.A.R.A. facilities include asimulation tank (4 m x 8 m x 1.35 m); 160 electrodes are located all around the tank and usedfor 3-D ERT. Because of the large number of electrodes and their dimensions, it is importantto model their effect in order to correctly evaluate the electrical system response. The forwardmodelling in the presented algorithm is based on the so-called complete electrode model thattakes into account the presence of the electrodes and their contact impedances.In this paper, we compare the results obtained with different regularizing functionals appliedon a synthetic model.