Passive seismic interferometry is a technique that reconstructs virtual seismic records using ambient noise, such as random noise or microseisms. The ambient noise in passive seismic data contains rich information, with surface waves being useful for the inversion of shallow subsurface structures, while body waves are employed for deep-layer inversion. However, due to the low signal-to-noise ratio in actual passive seismic data, different types of seismic waves mix together, making them difficult to distinguish. This issue not only affects the dispersion measurements of surface waves but also interferes with the imaging accuracy of reflected waves. Therefore, it is crucial to extract the target waves from passive source data. In practical passive seismic data, body wave noise and surface wave noise often overlap in frequency bands, making it challenging to separate them effectively using conventional methods. The synchrosqueezed continuous wavelet transform, as a high-resolution time–frequency analysis method, can effectively capture the variations in frequency of passive seismic data. This study performs time–frequency analysis of passive seismic data using synchrosqueezed continuous wavelet transform. It combines wavelet thresholding and Gaussian filtering to separate body wave noise from surface wave noise. Furthermore, wavelet cross-correlation is applied to separately obtain high-quality virtual seismic records for both surface waves and body waves.