Interpolation of Low-Sampling Interval Ground Penetrating Radar Images by F-K Domain Convolutional Neural Networks

Interpolation and high-resolution of missing traces of ground penetrating radar (GPR) images is a critical area of study. We propose a frequency-wavenumber (f-k) domain convolutional neural network specifically designed for the high-resolution task of GPR data acquired at large sampling intervals. The inputs and outputs of this network are the f-k domain spectra of GPR images. The architecture of the network is underpinned by aliasing theory, and it strategically addresses the relationship between the input and output spectra in the frequency domain. The proposed network was trained using 2000 simulation data generated via the FDTD method, and successfully reconstructed the f-k spectrum of data with fine 1 cm intervals from the spectra of coarsely sampled images taken at 7 cm intervals. In comparative performance evaluations, our network demonstrated a comparable proficiency to existing time-space domain networks for GPR data of earthwork sections. However, it exhibited superior accuracy in reconstructing images of bridge sections, where periodicity arises due to rebar responses. This indicates its enhanced capability in handling scenarios with periodic structural elements.