## 5 Field Data Test  
To validate the efficacy of the proposed method for analyzing field data, we performed a field trial with the SIR-4000 GPR system and surveyed a concrete pavement. With a central frequency of 400 MHz for both transmitting and receiving antennas, the acquisition employed a common-offset configuration with an offset of 0.02 m, a time window of 45 ns, and a sampling rate of 1024 per trace. As shown in Figure 22, several diffractions with relatively high energy are distinctly visible in the observed GPR profile. However, faint diffractions, as highlighted by white arrows, are severely obscured by strong reflections that may originate from layering interfaces, making these vital components challenging to discern clearly. A 5-level wavelet transform was performed on the raw GPR profile. The resulting low-frequency approximation and high-frequency detail coefficient profiles for each level are presented in Figure 23. As illustrated, MWT effectively decomposes the majority of the energy from reflection and diffraction into high-frequency detailed coefficients. However, the rate of decomposition is slightly slower than that observed in the synthetic data. Figure 23m-t shows that the energy of the reflection is mostly dispersed among the low-frequency approximation coefficients \(d_{LL4}\) and high-frequency horizontal detailed coefficients \(d_{LH4}\). In contrast, the high-frequency vertical detail coefficients \(d_{HL4}\) are where the diffraction energy is mainly concentrated, while the vertex energy of the diffraction is still located in the low-frequency approximation coefficients \(d_{LL4}\). After the fifth level wavelet transform, the energy at the vertex of the diffraction (previously retained in the fourth level low-frequency approximation coefficients) is further decomposed into the fifth-level high-frequency horizontal detail coefficients, as illustrated in Figure 23r. If the low-frequency approximation coefficients and high-frequency vertical detail coefficients  
Figure 21: Comparison of diffraction and reflection profiles separated using MWT-MSSA and PWD. (**a**) Noisy GPR profile; (**b**) diffraction profile and (**c**) reflection profile separated using the MWT-MSSA method; (**d**) dip field applied in PWD separation; (**e**) diffraction profile and (**f**) reflection profile obtained from PWD separation. Figure 22: The Observed GPR profile. from the fifth level wavelet transform are used to reconstruct the diffraction, a significant energy loss at the vertex of the diffraction may occur in the reconstructed result. To address this, the MWT-MSSA method is employed to reconstruct the diffraction from the coefficients of the first to fourth levels of the wavelet transform. The corresponding _k_-means cluster analysis parameters has been recorded and listed in Table 5.  
Figure 24a,b present the diffraction and reflection profiles derived using the MWT-MSSA. The results demonstrate that the reflection present in the observed GPR profile has effectively been eliminated, with only slight energy loss observed at the vertex of the diffraction. As indicated by the arrow in Figure 22, the diffraction is obscured by the reflection. However, in the profile shown in Figure 24a, the diffraction (marked by the dashed circle) is clearly visible. In contrast, the diffraction obtained from the dip field (Figure 25c) through PWD exhibits a higher energy loss at the vertex of the diffraction (indicated by arrow), resulting in less effective separation results, as shown in Figure 25b. After comparing the A-scan waveforms (as shown in Figure 26) along the white dashed lines in Figures 24a and 25a, it was found that the MWT-MSSA method performs better in conserving diffraction energy. Quantitative evaluation shows that MWT-MSSA achieves an energy-retention ratio of approximately 69.7%, which represents a significant improvement over the PWD method, which yields a ratio of 58.3%.