###### Abstract
However, complex geological conditions frequently result in overlapping wavefields contaminated with noise, thereby complicating conventional separation methods [6]. Traditional wavefield-separation approaches can be classified into two categories. The first comprises time-depth separation methods, including Singular Value Decomposition (SVD) [7] and median filtering [8]. SVD leverages differences in seismic wave lateral coherency; however, due to its non-orthogonal nature, SVD performs poorly with VSP wavefields. Median filtering utilizes distinct dip angles in the time-depth domain but requires precise first-break picking and may introduce frequency distortion in high-frequency signals. The second category encompasses transform-domain filtering methods, primarily f-k filtering [9] and Radon transformation [10; 11]. These methods separate wavefields by converting time-depth domain data into specific parameter domains. However, both approaches suffer from truncation effects and spatial aliasing, which potentially compromise separation accuracy through manual window selection. These challenges stem partly from the interference of wavefields within VSP records and the potentially time-varying spectral characteristics of the signal itself. Accurately characterizing and simulating such complex signal properties is itself a research focus; for instance, [PERSON] and [PERSON] utilized wavelet transforms to generate random seismic ground motion records with non-stationary characteristics, aiming to more realistically reflect the complex nature of seismic signals [12]. Recently, there has been increasing interest in deep learning for complex non-linear optimization problems [13; 14; 15]. Deep learning has successfully addressed the challenges of seismic inversion [16] and denoising [17] in seismic data processing. Compared to the limitations of traditional methods, neural networks possess powerful non-linear fitting capabilities. They are able to learn from data and model the complex and highly non-linear mapping relationship between the mixed wavefield and the separated up/downgoing wavefields. This gives them a theoretical advantage in handling complex wavefield superposition problems that are difficult for traditional linear methods to solve. Data-driven Convolutional Neural Networks (CNNs) [5] and Generative Adversarial Networks (GANs) [18], including variants like CGANs with specialized convolution blocks, effectively capture weak wavefield characteristics and mitigate the effects of amplitude disparity. However, the performance of these approaches depends heavily on the quality of the training data and their generalization capabilities. Although [PERSON] [6] and [PERSON] et al. [19] addressed this limitation using synthetic dataset generation, challenges persist in practical applications due to limited real-world samples. [PERSON] [20] proposed an unsupervised deep learning method for denoising receiver function data. This approach is based on an encoder-decoder framework, with its core idea being that the network learns the inherent structure of the signal by reconstructing the noisy input itself rather than a clean target during training, thereby achieving denoising and effectively eliminating the need for large-scale labeled data. [PERSON] [21] employed a dual-convolutional autoencoder architecture to realize unsupervised wavefield separation in VSP data. The pursuit of robust unsupervised and physics-guided methods continues to be an active area of research, with investigations into hybrid model-data-driven strategies [22] and the application of physics-informed principles to further constrain network learning. This study proposes a knowledge-guided unsupervised deep learning method (KGCAE, Knowledge-Guided Convolutional Autoencoder) whose core innovation lies in constructing and integrating multi-dimensional knowledge representations to guide the training process.