## 1 Introduction  
In recent decades, with the rapid development of seismic interferometry (SI), passive seismic exploration has garnered increasing attention from researchers due to its advantages such as low acquisition cost, high flexibility in data reconstruction, and rich information content ([PERSON] et al., 2021) [1]. Currently, the data acquisition in passive seismic exploration mainly relies on nodal seismic receivers. Compared with traditional cable seismic instruments, nodal seismic receivers are not only lightweight but also relatively flexible to deploy. Moreover, they can independently and continuously record data without the need to transmit it back to the main seismic instrument through acquisition cables ([PERSON] et al., 2021) [2]. [PERSON] et al. (2018) [3] used nodal seismic receivers to record ambient noise data and obtained the shallow crustal velocity structure through cross-correlation. Meanwhile, in passive seismic data acquisition, there may be requirements such as placing seismic geophones at specific locations, as well as detecting the hardware status of the instrumentsand the quality of the data. These functions can also be achieved through intelligent control of nodal seismic receivers. [PERSON] et al. (2024) [4] proposed a method using the Smart 4 Controller to assist operators in effectively evaluating the vibrations and impact loads at the bottom of the drill string during drilling operations in oil and gas development, which helps reduce emergencies such as damage to drill string components. SI reconstructs [PERSON]'s function between two receivers by cross-correlating the seismic records received by different geophones. [PERSON] (1968) [5] was the first to propose in his paper that, in an acoustic one-dimensional layered medium, the autocorrelation of seismic records received at a free surface is equivalent to the self-stimulating and self-receiving seismic records. This conclusion can be extended to the non-self-stimulating and non-self-receiving: if cross-correlation is performed between different seismic traces, a new seismic record can be obtained, with one seismic trace acting as a virtual source stimulating the seismic response received by the other seismic trace. [PERSON] also suggested that this conclusion applies to 3D anisotropic media. Later, [PERSON] et al. (1999) [6] extended this method to multi-dimensional models and named it Acoustic Daylight Imaging. [PERSON] (2008) [7] renamed this method seismic interferometry and elaborated on the basic principles of SI, demonstrating its broad range of applications, including data transformation, interpolation, passive signal extraction, and imaging. Based on the reciprocity theorem of the one-way wave equation and assuming no wavefield scattering, [PERSON] and [PERSON] (2006) [8] made rigorous theoretical inferences for noise sources in 3D inhomogeneous media and proved that SI holds under different non-attenuating media and source conditions. Subsequently, passive seismic interferometry has continued to advance. [PERSON] et al. (2008) [9] proposed a passive seismic interferometry method based on multi-dimensional deconvolution, which, compared to conventional cross-correlation methods, can still reconstruct high-quality virtual seismic records in cases of irregular underground source distributions. [PERSON] et al. (2012) [10] applied cross-correlation methods to reconstruct virtual seismic records from traffic noise, successfully extracting both body waves and surface waves. However, there are several challenges in using passive seismic data for exploration. Due to the relatively low signal-to-noise ratio of passive seismic data, the seismic records obtained after cross-correlation are difficult to process ([PERSON] et al., 2014) [11]. This is because body wave noise and surface wave noise in passive data frequently overlap in both spatial locations and frequency bands, making traditional filtering methods less effective ([PERSON] et al., 2003) [12].