Vertical seismic profiling (VSP) helps to derive high-resolution images around the instrumented borehole and is a cost-effective technique for CO2storage monitoring. In routine VSP data processing, P- and S-wave separation is a crucial step to extract independent single-mode waves for accurate imaging and interpretation. Conventional wave mode separation involves tedious, subjective, and non-reproducible manual interventions, especially when dealing with complex geology. To better automate the process, we propose a data-driven deep learning-based P- and S-wave separation method. Our method adapts a fully convolutional neural network that simultaneously extracts P- and S-potential data from multicomponent VSP measurements. To reduce the enormous computational cost in wave simulation while constructing training datasets with sufficient kinematic and dynamic variations, we introduce virtual wellbores where synthetic VSP data sampling wide variations in seismic kinematics and dynamics are recorded using only a dozen elastic wave simulations on a single velocity model. We qualify the separation results both directly in data space and in image space after reverse time migration (RTM). Generalization tests on various synthetic models and their corresponding RTM images demonstrate that the proposed strategy provides sufficient sampling of the high-dimensional data space and essentially ensures successful applications of the trained neural network to similar yet different geological scenarios.