Support Vector Regression method applied to thin pavement thickness estimation by GPR

摘要

In the field of civil engineering, sounding the layers is classically performed using standard ground-penetrating radar (GPR), whose vertical resolution is bandwidth dependent. The layer thicknesses are deduced from both the time delays of backscattered echoes and the dielectric constants of the layers. In contrast with the conventional spectral analysis approaches, we propose in this paper to use one of the most powerful machine learning algorithm, namely the Support Vector Machine(SVM), to perform Time Delay Estimation (TDE) of backscattered radar signals. In particular, this paper demonstrates the super time resolution capability of such technique in the context of overlapping and totally correlated echoes when thin pavement layers survey is under scope.