International audienceAnthropogenic noise, cost and logistical constrains generally limit to the use of land CSEM to a singletransmitter position for the deep imaging of the electrical conductivity. As the inversion of CSEM data inthe near field using a single transmitter position suffers from critical sensitivity singularities, we proposeda robust inversion framework adapted to this ill-conditioned inversion problem. The framework reliesspecifically on a robust Gauss-Newton solver, several model parameter transformations to compensate forthe heterogeneous sensitivities, and on the reformulation of the CSEM data under the form of a pseudo-MT tensor. We describe here the approach used for modelling and inversion implemented in our codePOLYEM3D and the new pseudo-MT formulation. We illustrate its application on a pathological syntheticcase inspired from Grayver et al. (2013) and then show the application of the process to a real CSEMdataset acquired in the context of thermal water prospection
