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Seismic-while-drilling (SWD) provides a cost-effective solution to subsurface imaging by utilizing the drill-bit noise as a source of seismic energy. However, retrieving an accurate virtual reflection data from SWD waveforms is challenging due to the erratic and unknown nature of the source signature. We propose a novel approach for multi-dimensional deconvolution (MDD) of SWD data that generates data free of surface-related multiples, corresponding to virtual sources located on the Earth’s surface. A key component of our approach is the direct arrival estimation and removal process based on the particle swarm optimization algorithm, which optimizes an initial traveltime curve by maximizing the energy of a flattened and stacked seismic recording. Moreover, to keep the computational cost of MDD to a reasonable level, the continuous SWD data is divided into smaller segments along the time axis, correlated, and stacked; in other words, we propose to form and solve the normal equations of the MDD problem. Validation on a synthetic dataset demonstrates that the proposed method can produce accurate virtual data and images of the subsurface. The proposed method is finally successfully applied to field dataset acquired in KAUST during a recent drilling campaign.