A Synergistic GPR Approach of Back Projection Algorithm and Full-Wave Inversion for Reconstruction in Unknown Multilayered Environments

Ground-penetrating radar (GPR) has become indispensable for subsurface reconstruction, especially in complex, multilayered infrastructure environments. Efficient and accurate estimation of permittivity and layer thickness, crucial for understanding these scenes, presents significant challenges. This article explores the integration and interaction between the back projection (BP) algorithm and full-wave inversion (FWI) to optimize reconstruction outcomes. We propose a ray-based refraction method and a high-order moment (HOM)-oriented local BP estimation, each aimed at improving efficiency from the critical perspectives of "delay" and "summation" in the BP algorithm, respectively, with a focus on the target layer. Crucially, the BP estimation results provide a reliable initial model for FWI, reducing the risk of local minima and decreasing the iteration count for more precise parameter estimation. FWI also compensates for the BP algorithm's limitations in estimating parameters in nontarget layers. Furthermore, full-wave modeling (FWM) mitigates antenna effects prior to parameter estimation, thereby enhancing accuracy. During reconstruction, the BP algorithm primarily generates target imaging, while FWI provides detailed information about layer interfaces. This synergistic approach leverages the complementary strengths of both algorithms: the BP algorithm captures spatial information depicted by the targets observed in B-scans, while FWI incorporates detailed electromagnetic wave propagation along layer interfaces presented in A-scans via radar equations. Experiments systematically analyze the accuracy and effectiveness of the proposed approach using ideal simulation models, sandbox laboratory data, and road data from the Belgian Road Research Centre (BRRC) facilities. This comprehensive evaluation underscores the approach's substantial potential in advanced geophysical surveys and environmental research.