A B S T R A C TRecently, new on-shore acquisition designs have been presented with multicomponent sensors deployed in the shallow sub-surface (20 m-60 m). Virtual source redatuming has been proposed for these data to compensate for surface statics and to enhance survey repeatability. In this paper, we investigate the feasibility of replacing the correlation-based formalism that undergirds virtual source redatuming with multi-dimensional deconvolution, offering various advantages such as the elimination of free-surface multiples and the potential to improve virtual source repeatability. To allow for data-driven calibration of the sensors and to improve robustness in cases with poor sensor spacing in the shallow sub-surface (resulting in a relatively high wavenumber content), we propose a new workflow for this configuration. We assume a dense source sampling and target signals that arrive at near-vertical propagation angles. First, the data are preconditioned by applying synthetic-aperture-source filters in the common receiver domain. Virtual source redatuming is carried out for the multi-component recordings individually, followed by an intermediate deconvolution step. After this specific pre-processing, we show that the downgoing and upgoing constituents of the wavefields can be separated without knowledge of the medium parameters, the source wavelet, or sensor characteristics. As a final step, free-surface multiples can be eliminated by multi-dimensional deconvolution of the upgoing fields with the downgoing fields.