Two-dimensional permittivity and conductivity imaging by full waveform inversion of multioffset GPR data: a frequency-domain quasi-Newton approach

International audienceFull waveform inversion of ground-penetrating radar data is an emerging technique for the quantitative, high-resolution imaging of the near subsurface. Here, we present a 2-D frequency-domain full waveform inversion for the simultaneous reconstruction of the dielectric permittivity and of the electrical conductivity. The inverse problem is solved with a quasi-Newton optimization scheme, where the influence of the Hessian is approximated by the L-BFGS-B algorithm. This formulation can be considered to be fully multiparameter since it enables to update permittivity and conductivity values within the same descent step, provided we define scales of measurement through a reference permittivity, a reference conductivity, and an additional scaling factor. Numerical experiments on a benchmark from the literature demonstrate that the inversion is very sensitive to the parameter scaling, despite the consideration of the approximated Hessian that should correct for parameter dimensionalities. A proper scaling should respect the natural sensitivity of the misfit function and give priority to the parameter that has the most impact on the data (the permittivity, in our case). We also investigate the behaviour of the inversion with respect to frequency sampling, considering the selected frequencies either simultaneously or sequentially. As the relative imprint of permittivity and conductivity in the data varies with frequency, the simultaneous reconstruction of both parameters takes a significant benefit from broad frequency bandwidth data, so that simultaneous or cumulative strategies should be favoured. We illustrate our scaling approach with a realistic synthetic example for the imaging of a complex subsurface from on-ground multioffset data. Considering data acquired only from the ground surface increases the ill-posedness of the inverse problem and leads to a strong indetermination of the less-constrained conductivity parameters. A Tikhonov regularization can prevent the creation of high-wavenumber artifacts in the conductivity model that compensate for erroneous low-wavenumber structures, thus enabling to select model solutions. We propose a workflow for multiparameter imaging involving both parameter scaling and regularization. Optimal combinations of scaling factors and regularization weights can be identified by seeking regularization levels that exhibit a clear minimum of final data misfit with respect to parameter scaling. We confirm this workflow by inverting noise-contaminated synthetic data. In a surface-to-surface acquisition configuration, we have been able to reconstruct an accurate permittivity structure and a smooth version of the conductivity distribution, based entirely on the analysis of the data misfit with respect to parameter scaling, for different regularization levels