## 2 The Tunnel GPR Dataset and Transfer Learning Between Optical and Radar Images  
The tunnel GPR dataset used in this study was collected from the Longhai tunnel of the Zhangjiuhe Water Diversion Project using a SIR-3000 ground-penetrating radar system with a central frequency of 900 MHz. During field testing, calibration experiments were conducted by laying GPR survey lines over tunnel lining sections with known thicknesses. The relative permittivity of the cast-in-place reinforced concrete used in the project was derived by measuring the electromagnetic wave travel time and correlating it with the actual lining thickness. The calibrated value was determined to be 6.3. Based on the reflection characteristics of electromagnetic waves at material interfaces, variations in the signal peaks in the GPR data were analyzed. A peak-amplitude criterion was applied to identify the lining interface by evaluating changes in amplitude magnitude and polarity. All dataset images were manually annotated using the LabelImg tool to generate labels in the Pascal VOC format [13]. Each annotation was verified by geological experts with relevant domain knowledge to ensure labeling accuracy and consistency. A total of 341 GPR images were used in this study, including 133 field-acquired images and 208 synthetic images generated via the finite-difference time-domain (FDTD) method. All images were resized to 416 x 416 pixels. The dataset was split into training and validation sets in an 8:2 ratio to ensure adequate data coverage during training and the reliability of the validation results. Synthetic data were incorporated into the dataset to address the limited availability of real GPR images of underground tunnel structures. A finite-difference time-domain (FDTD) method was employed to perform forward numerical simulations of secondary tunnel lining structures, generating representative synthetic GPR images to enhance sample diversity in model training. The simulation process was implemented using the open-source electromagnetic wave modeling platform gprMax. The simulation model incorporated the interface structure between primary and secondary lining layers and simulated GPR responses under various thickness conditions, enhancing the model's adaptability to different structural variations. To increase the realism of the simulated data and better approximate actual field acquisition conditions, boundary perturbations and geometric uncertainties were introduced during model construction. These included superimposed interface perturbations based on sinusoidal and piecewise terrain functions, along with Gaussian geometric noise (mean = 0, standard deviation = 0.05-0.1 m) to simulate surface roughness. During signal simulation, additional Gaussian white noise (mean = 0, standard deviation = 8% of maximum amplitude) was added to emulate the environmental and system noise typically encountered in real GPR acquisition. To ensure that the time-frequency characteristics of the simulated data closely match real GPR signals, measured field parameters were referenced throughout the simulation process. The relative permittivity of the secondary lining concrete was set to a mean of 6.3 with a standard deviation of approximately 0.75, while that of the primary lining was set to a mean of 9.7 with a similar deviation. Both materials were assigned a conductivity of 0.001 S/m. The simulation domain was set to 10 m in width and 1.4 m in depth, with a grid resolution of 0.005 m. A Ricker wavelet with a center frequency of 900 MHz was used as the source signal, with the receiving antenna placed 0.1 m away and a time window of 25 ns. The antenna was moved with a step size of 0.01 m, generating a total of 980 B-scan image traces. Perfectly matched layers (PMLs) were applied around the model to suppress boundary reflections and improve simulation accuracy. The introduction of simulated data not only improved the model's ability to recognize secondary lining structures but also enhanced its generalization across different tunnel scenarios.