The hydraulic conductivity distribution in a natural porous media is characterized by a great heterogeneity that makes its spatial assessment problematic and expensive. At the same time, a detailed knowledge of the hydraulic properties, as porosity, storativity, transmissivity and hydraulic conductivity K, is fundamental for the prediction of groundwater flow and solute transport in natural formations. Among the hydraulic properties, being the subsurface transport phenomena in natural formations mainly controlled by the Darcy's law, the proper definition of the K spatial distribution at different scales plays a fundamental role to evaluate the evolution of a contaminant plume, to define the well-catchment areas or to monitor a landfill site. To estimate aquifer hydraulic properties, inverse models have long been studied and, beyond the traditional hydraulic conductivity and head measurements, tracer test analyses have been widely adopted in the past and their use have increased in the recent years thanks to a great improvement of geophysical techniques. Among others, the Electrical Resistivity Tomography (ERT) allows to monitor a tracer test injection, providing time-lapse informations about the plume evolution with limited cost.
Assuming that time-lapse spatially distributed data deduced from a tracer test are available, the present work investigates different approaches aimed to the estimation of the local K distribution. At this purpose, Kalman filter based data assimilation techniques are coupled with the Lagrangian transport model and applied in different synthetic context
