3D-electrical resistivity tomography monitoring of salt transport in homogeneous and layered soil samples

Monitoring transport of dissolved substances in soil deposits is particularly relevant where safety is concerned, as in the case of geo-environmental barriers. Geophysical methods are very appealing, since they cover a wide domain, localising possible preferential flow paths and providing reliable links between geophysical quantities and hydrological variables. This paper describes a 3D laboratory application of Electrical Resistivity Tomography (ERT) used to monitor solute transport processes. Dissolution and transport tests on both homogeneous and heterogeneous samples were conducted in an instrumented oedometer cell. ERT was used to create maps of electrical conductivity of the monitored domain at different time intervals and to estimate concentration variations within the interstitial fluid. Comparisons with finite element simulations of the transport processes were performed to check the consistency of the results. Tests confirmed that the technique can monitor salt transport, infer the hydro-chemical behaviour of heterogeneous geomaterials and evaluate the performances of clay barrier

Imaging heterogeneities with electrical impedance tomography: laboratory results

Electrical impedance tomography (EIT) is commonly used on site as a characterisation and monitoring tool. In the present work this technique has been applied at laboratory scale in order to investigate its capabilities in controlled conditions, with particular reference to the detection of anomalies in sandy samples. Various configurations have been studied, investigating heterogeneities due to variation of porosity, grain size distribution and clay content. The results show the great potential of EIT as an imaging tool in laboratory equipment to check sample homogeneity and to monitor processes during tests

A primal-dual interior-point framework for using the L1 or L2 norm on the data and regularization terms of inverse problems

Maximum a posteriori estimates in inverse problems are often based on quadratic formulations, corresponding to a least-squares fitting of the data and to the use of the L2 norm on the regularization term. While the implementation of this estimation is straightforward and usually based on the Gauss-Newton method, resulting estimates are sensitive to outliers and result in spatial distributions of the estimates that are smooth. As an alternative, the use of the L1 norm on the data term renders the estimation robust to outliers, and the use of the L1 norm on the regularization term allows the reconstruction of sharp spatial profiles. The ability therefore to use the L1 norm either on the data term, on the regularization term, or on both is desirable, though the use of this norm results in non-smooth objective functions which require more sophisticated implementations compared to quadratic algorithms. Methods for L1-norm minimization have been studied in a number of contexts, including in the recently popular total variation regularization. Different approaches have been used and methods based on primal-dual interior-point methods (PD-IPMs) have been shown to be particularly efficient. In this paper we derive a PD-IPM framework for using the L1 norm indifferently on the two terms of an inverse problem. We use electrical impedance tomography as an example inverse problem to demonstrate the implementation of the algorithms we derive, and the effect of choosing the L2 or the L1 norm on the two terms of the inverse problem. Pseudo-codes for the algorithms and a public domain implementation are provided

Generation of anisotropic-smoothness regularization filters for EIT

In the inverse conductivity problem, as in any ill-posed inverse problem, regularization techniques are necessary in order to stabilize inversion. A common way to implement regularization in electrical impedance tomography is to use Tikhonov regularization. The inverse problem is formulated as a minimization of two terms: the mismatch of the measurements against the model, and the regularization functional. Most commonly, differential operators are used as regularization functionals, leading to smooth solutions. Whenever the imaged region presents discontinuities in the conductivity distribution, such as interorgan boundaries, the smoothness prior is not consistent with the actual situation. In these cases, the reconstruction is enhanced by relaxing the smoothness constraints in the direction normal to the discontinuity. In this paper, we derive a method for generating Gaussian anisotropic regularization filters. The filters are generated on the basis of the prior structural information, allowing a better reconstruction of conductivity profiles matching these priors. When incorporating prior information into a reconstruction algorithm, the risk is of biasing the inverse solutions toward the assumed distributions. Simulations show that, with a careful selection of the regularization parameters, the reconstruction algorithm is still able to detect conductivities patterns that violate the prior information. A generalized singular-value decomposition analysis of the effects of the anisotropic filters on regularization is presented in the last sections of the paper