## 1 Introduction
By identifying and separating body wave-dominated noise and surface wave-dominated noise from the original data prior to cross-correlation and then performing cross-correlation and stacking on each noise type separately, it is possible to obtain high-quality virtual seismic records for both body waves and surface waves. However, the passive seismic data collected in practice are quite complex, with multiple signals overlapping and energy distribution being uneven. This makes it difficult to distinguish surface wave noise and body wave noise in the original passive data ([PERSON] et al., 2015) [13]. Due to the characteristics of surface waves, such as long propagation distances, slow attenuation, and high energy, and because most noise sources in actual data collection originate from the surface, the energy of surface waves is relatively strong in passive seismic data ([PERSON] et al., 2023) [14]. Therefore, extracting surface waves using SI is relatively straightforward. However, due to the complexity of passive seismic signals, the extraction of surface wave dispersion curves is still prone to interference. As a result, many researchers have conducted studies and practical applications on this topic. [PERSON] (1989) [15] proposed a Horizontal-to-Vertical Spectral Ratio (HVSR) method, which inverts passive surface waves using the vertical and horizontal components from a single station. [PERSON] et al. (2017) [16] introduced the Multi-Channel Analysis of PassiveSurface Waves (MAPS) method, replacing the active source in Multi-Channel Analysis of Surface Waves (MASW) with passive sources, allowing accurate imaging of surface wave dispersion energy using short-duration passive seismic records. [PERSON] et al. (2019) [17] proposed a frequency-Bessel transform method (F-J), which can clearly image multimodal dispersion curves from ambient seismic noise data recorded by observation arrays. This method was applied to USArray data recorded in the central United States. [PERSON] et al. (2022) [18] used convolutional neural networks (CNNs) to determine the Rayleigh wave phase velocity and validated with real data that the CNN can be used for near-surface subsurface imaging. [PERSON] et al. (2023) [19] successfully extracted surface waves from train vibrations using seismic interferometry, while [PERSON] et al. (2023) [20] applied surface waves measured from ambient noise for 3D tomography. Body wave signals are relatively weak in passive seismic records and are difficult to extract in practice. However, due to their higher frequency, they can enhance the resolution of subsurface imaging. As a result, considerable research has been conducted on the extraction of body waves. In terms of data acquisition, [PERSON] et al. (2018) [21] proposed a novel acquisition scheme using Distributed Fiber Acoustic Sensing (DAS) technology. By adopting buried vertical arrays, the surface wave noise can be significantly reduced. Alternatively, dampers or vibration isolation layers can be used when placing sensors. [PERSON] et al. (2025) [22] proposed a new shell damper and predicted its application in borehole shock absorbers in the oil and geothermal industries. [PERSON] et al. (2014) [11] used the method of inclined stacking illumination analysis to distinguish low-velocity surface waves and high-velocity body waves along a 2D profile and validated the proposed method using passive data recorded in Annerveen, northern Netherlands. [PERSON] et al. (2017) [23] applied beamforming to analyze real data from a CO\({}_{2}\) storage monitoring project, separating body waves and surface waves from the original passive data based on source azimuth and velocity distribution. [PERSON] et al. (2022) [24] extracted body waves by calculating the signal-to-noise ratio of the data before cross-correlation and applied this method to real data from Inner Mongolia, China. [PERSON] et al. (2024) [25] proposed a method using F-k spectral analysis to identify body wave noise and surface wave noise from raw passive noise data.