arXiv:2610.19265v1 Announce Type: new Abstract: The scarcity of real-world ground penetrating radar (RealGPR) data is a major barrier to the development of deep learning (DL) methods for GPR data processing. Although simulated GPR (SimGPR) datasets can be used to train DL models, differences in hardware configuration and system parameters between SimGPR and RealGPR create a sim-to-real domain gap that leads to poor generalization of DL models to RealGPR measurements. To address this gap, we present a physics-based far-field GPR domain transfer (FFDT) method to transfer far-field SimGPR data to the corresponding RealGPR-domain data in the same scenario. In FFDT, SimGPR and RealGPR are modeled using angle-dependent transfer functions that capture three-dimensional antenna radiation characteristics. These transfer functions are then combined with ray tracing to enable the sim-to-real data transfer. Extensive studies across diverse objects and soil conditions demonstrate that FFDT-transferred data closely match RealGPR ground truth. To further demonstrate its value for RealGPR-domain dataset generation, FFDT is implemented to build a RealGPR-domain dataset for training a DL model for clutter removal. The model achieves robust performance on RealGPR measurements. With one-time SimGPR and RealGPR system calibration, FFDT enables RealGPR-domain dataset generation without requiring task-specific RealGPR field measurements for DL model training or fine-tuning, thereby addressing the RealGPR data scarcity bottleneck and supporting reliable development and deployment of DL methods for real-world GPR applications.