## 4 Numerical Experiments
Comparing the two, it can be observed that, relative to Figure 8b, the virtual seismic record processed with the SWT method (Figure 8a) effectively removes surface waves in the red-boxed area, while the reflection section indicated by the yellow arrows is well preserved. We further conducted Kirchhoff pre-stack depth migration imaging on the reconstructed virtual seismic records (modified from the CREWES MATLAB Software Library (CMSL, Version 2122) developed by [PERSON]) on the reconstructed virtual seismic record, and the resulting imaging profile is shown in Figure 8c, where the reflection layers at 200 m and 600 m (marked by the yellow arrows) are accurately imaged. For the extraction of surface wave signals, a similar approach to body wave processing is used. Low-frequency wavelet coefficients are selected for thresholding, and cross-correlation is performed in the wavelet domain before returning to the time domain, resulting in a virtual shot gather predominantly composed of surface waves. Similarly, we compare this with the virtual shot gather processed using conventional bandpass filtering, where the bandpass filter aligns with the frequency band selected by SWT. The results are shown in Figure 9. Figure 9a displays the virtual shot gather obtained after applying low-pass filtering below 10 Hz and cross-correlation, while Figure 9b shows the reconstructed virtual shot gather after SWT processing. It can be observed that, compared to the conventional method, the reflection wave at the yellow arrow is effectively suppressed, and high signal-to-noise ratio surface wave records (within the red box) are obtained. Figure 7: Time–frequency spectrum of the reflected waves before and after thresholding: (**a**) before thresholding; (**b**) after thresholding. Figure 8: Comparison of cross-correlation results after extracting body wave noise: (**a**) virtual shot gather reconstructed after bandpass filtering; (**b**) reconstructed virtual shot gather after SWT thresholding; (**c**) migration imaging profile. Figure 9: Comparison of cross-correlation results after extracting surface wave noise: (**a**) reconstructed virtual shot gather after SWT thresholding; (**b**) virtual shot gather reconstructed after bandpass filtering. Additionally, we performed surface wave dispersion analysis on the obtained surface wave records and used surface wave inversion to derive the shallow subsurface structure. The MASW method, commonly used in geophysical exploration for analyzing Rayleigh waves, extracts surface wave dispersion curves by transforming the two-dimensional seismic signals from the time-space domain to the frequency-velocity domain. Figure 10 shows the dispersion imaging results obtained using the MASW method from the virtual surface wave seismic record in Figure 9b. Furthermore, using a constant velocity model as the initial velocity model (indicated by the blue solid line), a multi-mode surface wave nonlinear inversion method was applied to obtain the inversion results for the shallow subsurface structure, shown in Figure 10c. The red solid line represents the inversion results, while the green solid line represents the true velocity profile. Here, due to the weak high-frequency signal energy in the passive data, some high-frequency information is missing when extracting the dispersion curve, leading to insensitivity in the inversion of shear wave velocities for the shallow layers above 2 m. However, the inversion results for the subsurface layers below 2 m are well captured.