Full-Waveform Inversion with Convolutional Objective Function for Robust Subgrade Anomaly Detection in GPR Data

Detecting pavement subsidence and underground cavities is critical for infrastructure safety, and Ground Penetrating Radar (GPR) is a preferred method due to its high precision, efficiency, and real-time imaging capabilities. However, traditional full waveform inversion (FWI) methods often suffer from reduced accuracy and reliability when the transmitting source wavelet is inaccurately estimated. To address this issue, we propose a convolution-type objective function FWI algorithm. Using synthetic data models, we compare the inversion results of our proposed method with those of traditional FWI for road subsidence and underground cavity detection. The results demonstrate that the convolution-based objective function FWI achieves robust inversion performance even with an imprecise source wavelet estimation, confirming its effectiveness. Furthermore, we apply the algorithm to two different sets of real GPR field data, analyzing the inverted relative permittivity distributions of subsurface media. The findings validate the practicality of our convolutional objective function FWI for real-world data, providing a theoretical foundation for detecting subsurface anomalies beneath roadbeds.