# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 3 Validation on Synthetic GPR Data
### Suppression Under Different Arrangement Conditions  
To further evaluate the adaptability of the proposed model under different rebar arrangement conditions, additional simulated datasets with varying rebar spacings and defect depths were constructed based on the core dataset (rebar spacing of 0.2 m). Keeping other parameters unchanged, the same model training strategy was employed to conduct suppression experiments.  
Figure 8 presents the suppression results under different rebar spacings (0.15 m, 0.2 m, 0.25 m and 0.3 m). It can be observed that rebar spacing significantly affects the distribution of multiple reflection signals. When the rebar spacing is 0.15 m, the rebar clutter is highly dense, leading to overlapping multiple reflection signals that obscure the defect region, making it difficult to identify clearly. As the spacing increases to 0.2 m and 0.25 m, the multiple reflection interference is reduced to some extent, and the visibility of defect contours improves. Notably, at a spacing of 0.25 m, compared to 0.15 m, the signal structure becomes more distinct, and the defect region appears clearer. When the spacing is further increased to 0.3 m, the intervals between rebar clutters expand, significantly reducing multiple reflection signals. Consequently, the defect boundaries become the most distinct, and the signal structure appears the most well defined. After rebar suppression processing, the proposed model demonstrates strong performance across different rebar spacing conditions, effectively mitigating multiple reflection interference while preserving defect information integrity. Particularly when the rebar spacing is small (0.15 m), although the background interference remains strong, the suppression results show a significant reduction in clutter, allowing defect contours to gradually emerge.  
Figure 9 presents the suppression results for different defect depths. As seen in the figure, the proposed model continues to perform effectively in suppressing multiple reflection interference as defect depth increases. For shallow defects (rebar-defect distance within 0.3 m), the defect region exhibits strong reflection signals but is easily affected by background clutter, leading to blurred defect signals in the original images. After suppression processing, despite the shallowness of the defects, the model effectively attenuates multiple reflection interference, preserves the defect region's distinct contours, and enhances signal clarity. For deeper defects (rebar-defect distance beyond 0.6 m), the defect reflection signals gradually weaken, while the influence of background clutter becomes relatively minor. Under these conditions, the model remains capable of accurately suppressing reinforcement clutter and multiple reflection signals, resulting in clearer defect boundaries and more defined signal structures.  
Figure 7: Features analysis of the CSA module. (**a**) Original GPR image. (**b**) Rebar suppression result. (**c**) The heatmap of the model’s features without the CSA module. (**d**) The heatmap of the model’s features with the CSA module.  
Overall, the experimental results demonstrate that the proposed model consistently delivers stable and high-quality interference suppression under varying rebar spacing and defect depth conditions. This significantly enhances defect detectability, indicating the model's strong generalization ability and robustness in complex scenarios.  
Figure 8: Comparison of suppression results under different rebar spacings. (**a**) The simulation models with different rebar spacings. (**b**) Corresponding GPR images. (**c**) Rebar-suppressed results.