Physics-guided multi-channel attention learning for GPR imaging of hidden anomalies in historic masonry

Ground-penetrating radar (GPR) is a non-destructive electromagnetic method for high-resolution imaging of hidden structures and anomalies. However, its application to historic masonry remains challenging because heterogeneous brick–mortar materials, irregular interfaces, multiple scattering, signal attenuation, and environmental noise can obscure the responses of small or weak internal anomalies. This study proposes a physics-guided deep learning framework for imaging voids, delamination, cracks, and moisture-affected zones in heterogeneous historic masonry. Here, “physics-guided” refers to incorporating physically interpretable GPR attributes into the learning process to provide complementary waveform, reflection-energy, and structural-boundary information. A synthetic GPR dataset was first generated through time-domain electromagnetic forward modeling of brick–mortar structures containing anomalies with variable geometries, dielectric properties, positions, and noise levels. Each B-scan was represented using three complementary channels: the B-scan, the amplitude envelope obtained through the Hilbert transform, and the gradient magnitude. These channels preserve the original waveform characteristics, emphasize reflected energy, and highlight local structural boundaries, respectively. A physics-guided attention U-Net, termed PG-AttUNet, was then developed to integrate these representations. Attention gates were introduced into the skip connections to suppress clutter-dominated features while retaining responses associated with potential internal anomalies. On the synthetic test dataset, the proposed method achieved a mean squared error of 0.0053, a peak signal-to-noise ratio of 22.76 dB, and a structural similarity index of 0.927, outperforming conventional convolutional neural network, U-Net, and multi-input baseline models. Application to field GPR data acquired from a historic masonry structure produced more spatially continuous anomaly maps with fewer scattered false detections. The detected anomalies were spatially consistent with potential crack- or seepage-related pathways inferred from the radar attributes. Because direct field labels were unavailable, the field predictions are interpreted as probabilistic indications rather than deterministic structural reconstructions. These results demonstrate that integrating physically meaningful electromagnetic attributes with attention-based learning can improve the robustness and interpretability of GPR anomaly imaging in heterogeneous cultural-heritage materials.