Abstract: The core of vertical seismic profile (VSP) data processing lies in accurately separating upgoing and downgoing waves, and the quality of this separation directly affects the reliability of imaging and inversion of formation parameters. Current mainstream methods include frequency-wavenumber (f-k) filtering, the Radon transform and its improved high-precision algorithms, median filtering, and sparse constraint-based optimization methods. However, these methods incur high trial-and-error costs because of parameter sensitivity and difficulty in balancing fidelity and efficiency in complex wavefields. This study conducted a detailed theoretical analysis and data testing on common methods from the aspects of separation accuracy, processing efficiency, and operation procedures. Considering label generation in deep learning, the study tested the adaptability of different methods for label production, thereby making suggestions for label optimization to improve the quality and production efficiency of training data. In addition, the study constructs a rich label library based on data from multiple work areas, trains a general wavefield separation model, and realizes efficient processing of unknown data, which ultimately improve the accuracy and efficiency of wavefield separation.