## 1 Introduction
This method was applied to real data from Songke-2, Northeast China, successfully extracting high-quality surface waves and body waves. Time-frequency analysis, as a widely used signal processing technique in seismic exploration, is highly effective for the processing of non-stationary signals. Traditional frequency-domain or time-domain analysis methods often fail to provide a comprehensive understanding of the signal characteristics. Time-frequency transformation techniques, by simultaneously analyzing the variations in the signal in both time and frequency domains, can effectively reveal the features of different seismic waves, especially demonstrating significant advantages in the processing of non-stationary signals ([PERSON] and [PERSON], 2013; [PERSON] et al., 2017) [26; 27]. Among commonly used time-frequency transformations, Fourier transform performs poorly for non-stationary signals. Although the short-time Fourier transform processes non-stationary signals by windowing, the window width is difficult to optimize: if the window is too wide, the time resolution is insufficient; if the window is too narrow, the frequency resolution is inadequate. On the other hand, continuous wavelet transform (CWT) replaces the infinite-length triangular basis function with a finite-length decaying wavelet basis, solving the scaling issue of the window. However, it is highly dependent on the choice of the mother wavelet and suffers from relatively low resolution ([PERSON] and [PERSON], 2017) [28]. Due to the limitations of conventional time-frequency analysis methods in meeting the analytical requirements, the synchrosqueezed continuous wavelet transform (SWT) combines CWT and the reallocation method ([PERSON] et al., 2011) [29]. Based on wavelet transform, SWT performs squeezing and reshuffling in the scale direction according to the magnitude of the modulus of the wavelet coefficients in the spectrum. Finally, by mapping, it generates a time-frequency spectrum with more concentrated energy in the time-frequency domain. While [PERSON] et al. (2011) [29] initially developed SWT based on the concept of Empirical Mode Decomposition (EMD), SWT offers a more rigorous mathematical derivation and supports inverse transformation, in contrast to EMD. Subsequently, [PERSON] and [PERSON] (2012) [30] introduced generalized synchrosqueezed wavelet transform, which addresses the limitation of the original SWT by incorporating additional factors beyond just the scale factor. [PERSON] et al. (2013) [31] demonstrated that SWT is applicable to both noise and non-uniformly sampled data in engineering and natural environments. Since then, SWT has been widely used in seismic data analysis and processing, as well as in oil and gas exploration and reservoir prediction ([PERSON] et al., 2017 [32]; [PERSON] et al., 2017 [33]). [PERSON] et al. (2016) [34] utilized SWT for denoising microseismic signals and detecting the onset of microseismic events. In this study, based on the differences in the distribution of passive surface waves and body waves across different frequency bands, we use time-frequency analysis, specifically SWT, to identify body wave noise and surface wave noise in passive seismic data. By combining this with denoising techniques, we aim to minimize the mutual interference between them. Furthermore, high-quality body and surface waves are extracted through cross-correlation in the wavelet domain.