Rapid subsurface sensing via Bayesian-optimized FDTD modeling of ground penetrating radar

Ground penetrating radars (GPRs) are widely used for non-destructive sub-surface evaluations but require extensive data processing and expert-guided analysis. Modern tools, including machine learning and waveform-inversion, automate interpretation but require accurate GPR modeling. Past modeling approaches primarily focus on full-scale 3D modeling of GPR antennas, which is computationally expensive. To simulate realistic GPR scans, this study proposes a simplified and efficient modeling approach by calibrating GPR antennas in 1D and 2D finite-difference-time-domain (FDTD) models. Intrinsic radar parameters (e.g., center frequency, waveform type, and bistatic separation) in FDTD are calibrated by Bayesian optimization using a medium with known dielectric properties as the calibration reference. The calibrated model is then applied to materials with unknown extrinsic dielectric properties, simplifying the process by separating the calibration of intrinsic radar parameters from material dielectric properties. The method was validated for two real commercial GPRs of different frequencies, and forward simulations with the calibrated models produced A- and B-scans closely matching real GPR data. The calibrated models were also used to analyze the sensitivity of GPR signals to changes in subsurface material properties and to determine depth resolution and maximum penetration depth of a real GPR. Furthermore, Bayesian wave-inversion using the calibrated model could accurately estimate dielectric properties of building materials such as concrete and soil, demonstrating strong correlations (0.89–0.99) with in-situ sensor measurements. Estimation of dielectric properties with the proposed model was about nine times faster than with full-scale 3D models. The proposed method offers an efficient and promising approach for subsurface material characterization applications such as concrete nondestructive testing and soil moisture-sensing. • Simplified model calibration for accurate and realistic simulation of real GPRs. • Use of calibrated models to effectively determine the depth resolution of a GPR. • Accurate forward modeling (A- and B-scan generation) using calibrated models. • Accurate material property estimation using calibrated models and optimization. • Proposed method is significantly faster than full-scale 3D modeling.