Near-field full-waveform inversion of radar waves to monitor water front in limestone - Archive ouverte HAL
Article Dans Une Revue IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Année : 2017

Near-field full-waveform inversion of radar waves to monitor water front in limestone

Résumé

Limestone has been a widely used building material for many years. Its degradation process has been strongly linked to its water content, to which the radar waves are sensitive. In this paper, the closed-form, near-field full-wave radar model of Lambot et al. is utilized for monitoring water ingress in limestone. This model is based on planar layered media Green's functions combined with an intrinsic representation of the antenna through global reflection and transmission functions. The Jonscher's model is used to describe the frequency dependence of the limestone electromagnetic properties. Then, an inversion method based on a genetic algorithm is developed and optimized. The method is numerically validated by simulating electromagnetic wave propagation in medias, using a three-dimensional finite element software, namely high-frequency electromagnetic field simulation. Finally, the method is experimentally validated, with a stepped-frequency radar system and an ultra-wide band antenna, during an imbibition process of a limestone slab. Results are compared to gamma densimetry measurements, a nuclear and semi-destructive approach, performed as the reference. Analyses of the results show that the average relative difference between the estimated and the reference water front is 2.5%, thereby demonstrating the performance of the proposed nondestructive method.
Fichier non déposé

Dates et versions

hal-01599949 , version 1 (02-10-2017)

Identifiants

Citer

Borui Guan, Amine Ihamouten, Xavier Derobert, David Guilbert, Sébastien Lambot, et al.. Near-field full-waveform inversion of radar waves to monitor water front in limestone. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10 (10), 9p. ⟨10.1109/JSTARS.2017.2743215⟩. ⟨hal-01599949⟩
157 Consultations
0 Téléchargements

Altmetric

Partager

More