# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 4 Validation on Real GPR Data  
In this section, we use real GPR images collected from the Husa Tunnel project to verify the effectiveness of our model, as shown in Figure 10. The Husa Tunnel is located in the western region of Yunnan Province, China, at an altitude between 1404\(\sim\)2020 m. It is the longest tunnel on the Tengchong to Longchuan highway.  
Figure 10: GPR data collection process of Husa Tunnel.  
Figure 9: Comparison of suppression results under different defect depths. (**a**) The simulation models with different defect depths. (**b**) Corresponding GPR images. (**c**) Rebar-suppressed results.  
We were authorized to use the SIR3000 geological radar system, manufactured by Geophysical Survey Systems, Inc. (GSSI, Nashua, NH, USA), equipped with a 400 MHz central frequency antenna, to inspect the concrete lining structure of the tunnel segment YK81+310\(\sim\)YK82+840. The system operates within a frequency range of 16-2200 MHz, with a sampling rate of 0-8000 ns and a depth penetration capability of up to 50 m in concrete, as specified by the manufacturer. In this study, each A-scan consisted of 512 samples, ensuring sufficient resolution for analyzing subsurface reflections. Similar applications of this system have been reported in previous studies [35; 36], providing a basis for interpreting our results. When the vertical depth of the defect is less than 10 cm, the defect is classified as non-dense. When the vertical depth of the defect is greater than 10 cm, the defect is classified as a void [37]. Ultimately, five detection lines with void characteristics were identified. We randomly selected data from three of these lines for sliding-window-based data extraction while usingg data augmentation techniques such as geometric or color transformations to enhance the real GPR image dataset. This resulted in 100 real GPR images with rebar clutter, and we also selected 100 real GPR images with only void defects. The data from the remaining two lines were used for the final testing of the model.  
Due to factors such as the site environment, real data are often affected by background noise and exhibit significant differences from simulated data. To enhance the model's ability to adapt to different data distributions and environmental conditions in real-world scenarios, this study adopted a transfer learning and fine-tuning strategy. First, the model was pre-trained on simulated data at 800 MHz, which has higher resolution and clearer defect features. This allowed the model to better learn how to remove rebar clutter and retain defect information during the early stages of training. Then, the model was fine-tuned using real data collected at 400 MHz, enabling it to adapt to the GPR signal characteristics in actual engineering environments. This helped bridge the distribution gap between simulated and real data, further improving the model's applicability in real tunnel tasks.  
Figure 11a shows an GPR image of a void defect at the arch top of the right section entrance of the Husa Tunnel, within the range of YK81+546\(\sim\)YK81+549. Before inputting the GPR image into the trained model for rebar clutter removal, data preprocessing was first performed to suppress noise and highlight target echoes. This included time-zero correction, DC offset removal, and one-dimensional frequency filtering. Figure 11b shows the processed GPR image. The effect after rebar suppression by our model is shown in Figure 11c. It is evident from the figures that, after rebar suppression, the rebar reflection signals in the image were significantly weakened, making the outline of the void defect more distinct and easier to identify. The results showed that our rebar suppression method effectively improves the visibility of void defects signals, thereby enhancing the interpretability of GPR data for tunnel lining inspections.  
Figure 11: Rebar suppression result of real GPR image. (**a**) Original GPR image. (**b**) Processed GPR image. (**c**) Rebar suppression result.