Pavements Layered Media Characterizations using deep learning-based GPR full-wave inversion

The possibility to estimate accurately the subsurface electric properties of the pavements from ground-penetrating radar (GPR) signals using inverse modeling is obstructed by the appropriateness of the forward model describing the GPR subsurface system. In this presentation, we improved the recently developed approach of Lambot et al. whose success relies on a stepped-frequency continuous-wave (SFCW) radar combined with an off-ground monostatic transverse electromagnetic horn antenna. The deep-learning based method were adopted to train an intelligent model including the waveform of the Green’s functions. The method was applied and validated in laboratory conditions on a tank filled with a two-layered sand subject to different water contents. Results showed agreement between the predictions of measured Green’s functions deep-learning model and the measured ones. Model inversions for the dielectric permittivity and heights of antenna further demonstrated for a comparison of presented method.