Application of regularized deconvolution technique for predicting pavement thin layer thicknesses from ground penetrating radar data

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In this paper, regularized deconvolution is utilized to analyze GPR signal collected from thin asphalt pavement overlays of various mixtures and thicknesses on a test site. By applying regularized deconvolution and the L-curve method, the overlapped interface was identified in the signal. The thickness of the thin layer was predicted with maximum error of 4.2%, which is less than 1.5mm, a value well below the layer tolerance during construction. The study shows that the algorithm based on regularized deconvolution is a simple and effective approach for processing GPR data collected from thin pavement layers to predict their thickness.