3D land CSEM with a single transmitter position

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

3D land CSEM inversion in noisy environment with a single transmiter: inversion approach and application for geothermal water prospection

International audienceAnthropogenic noise, cost and logistical constrains generaly limit to the use of land CSEM to a few transmiter positions for the deep imaging of the electrical conductivity. The 3D inversion of CSEM data in the near field using a single transmiter position suffers from critical sensitivity singularities. We proposed a robust inversion framework adapted to this ill-conditioned inversion problem. The framework relies specificaly on a robust Gauss-Newton solver, model parameter transformations to compensate the heterogeneous sensitivies, and on the reformulation of the near field CSEM data under the form of a pseudo-MT tensor. We describe the approach used for modeling and inversion implemented in our code POLYEM3D and show the advantages of pseudo-MT tensor formulation. The strategy have been tested on a pathologic synthetic case inspired from grayver et al (2013), and then was successfully applied to a real CSEM dataset acquired in the context of thermal water prospection in a noisy environnement

Variational Bayesian inversion of synthetic 3D controlled-source electromagnetic geophysical data

International audienceInversion of controlled-source electromagnetic data is dealt with for a geophysical application. The goal is to retrieve a map of conductivity of an unknown body embedded in a layered underground from measurements of the scattered electric field that results from its interaction with a known interrogating wave. This constitutes an inverse scattering problem whose associated forward problem is described by means of electric field domain integral equations. The inverse problem is solved in a Bayesian framework in which prior information is introduced via a Gauss-Markov-Potts model. This model describes the body as being composed of a finite number of different materials distributed into compact homogeneous regions. The posterior distribution of the unknowns is approached by means of the variational Bayesian approximation as a separable distribution that minimizes the Kullback-Leibler divergence with respect to the posterior law. Thus, we get a parametric model for the distributions of the induced currents, the conductivity contrast, and the various parameters of the prior model that are obtained following a semisupervised iterative approach. This method is applied to multifrequency synthetic data corresponding to a 3D crosswell configuration in which the sought body is made of two separated anomalies, a conductive heterogeneity and a resistive one, and its results are compared with that given by the classic contrast source inversion (CSI). The method succeeds in retrieving compact homogeneous regions that correspond to the two anomalies whose shape and conductivities are obtained with a good precision compared with that obtained with CSI.Read More: https://library.seg.org/doi/10.1190/geo2016-0682.