Development of an Efficient and Accurate Forward Model for GPR Imaging of Buried Objects

Imaging buried objects using ground penetrating radar (GPR) has been a key research focus over the years. In GPR imaging, popular migration methods, such as Kirchhoff’s migration and frequency-wavenumber migration are used for homogeneous media. They cannot adapt to scenarios like layered media, where electromagnetic (EM) wave velocity changes across layers. Model-based imaging methods are better suited for layered media. Numerical models, such as finite-difference time domain (FDTD) method and analytical models based on dyadic Green’s function, have been used for subsurface object imaging. However, they are computationally expensive. In this work, a new model based on the full wave model (FWM) of transmitted EM wave component has been developed to represent layered media. The proposed model has been validated with well-known FDTD-based FWM available in literature. Then, time reversal imaging method has been implemented on experimental data to perform imaging of buried objects. Peak signal-to-ratio values are evaluated for a qualitative study. The results demonstrate that the proposed model provides the same quality in imaging with enormous computational efficiency compared to the FWM.