Improved linear inversion of low induction-number electromagnetic data

We define a two-step procedure to obtain reliable inverse models of the distribution of electrical conductivity at depth from apparent conductivities estimated by electromagnetic instruments such as GEONICS EM38, EM31 or EM 34-3. The first step of our procedure consists in the correction of the apparent conductivities to make them consistent with a Low Induction Number condition, for which these data are very similar to the true conductivity. Then, we use a linear inversion approach to obtain a conductivity model. To improve the conductivity estimation at depth we introduced a depth-weighting function in our regularized weighted minimum length solution algorithm. We test the whole procedure on two synthetic datasets generated by the COMSOL Multiphysics for both the vertical magnetic dipole and horizontal magnetic dipole configurations of the loops. Our technique was also tested on a real dataset, and the inversion result has been compared with the one obtained using the dipole-dipole DC electrical resistivity (ER) method. Our model not only reproduces all shallow conductive areas similar to the ER model, but also succeeds in replicating its deeper conductivity structures. On the contrary, inversion of uncorrected data provides a biased model underestimating the true conductivity.