Simultaneous-source (or blended) seismic data acquisition allows reducing acquisition time, which is beneficial in harsh meteorological environment or when strict environmental regulations are applied. The only draw-back of blended acquisition is the interference (or cross-talk) between signals originating from different seismic sources firing at the same time. Recent advances in processing and imaging allow acceptable handling of the cross-talk, however, specific processing methods adapted for blended data still need to be improved. Whether the deblending step (or separation of signals originating from different sources) is necessary remains an open question, but it is still included in the beginning of most of the simultaneous-source processing sequences. In this paper, we propose a deblending method based on the decomposition of the blended signal into a set of locally coherent features, or seismic events. The information on the source contained in each seismic event is further used for separation. The decomposition is performed using the Orthogonal Matching Pursuit signal decomposition algorithm with a specific parametric dictionary adapted for seismic events and allowing sparse representation of the data. The method shows promising results on synthetic sets of 2D seismic data and is scalable to large datasets of industrial size.