In this work, we investigate the application of the recently introduced signal decomposition method known as robust principal component analysis (RPCA) to the problem of wave separation in seismic data. The motivation of our research comes from the observation that the elements of the decomposition performed by RPCA can be associated with particular structures that often arise in seismic data. Results obtained considering two different situations, the separation of crossing events and the separation of diffracted waves from reflected ones, confirms that RPCA is a promising tool in seismic signal processing, outperforming the classical singular value decomposition (SVD) and the extension of the SVD based on independent component analysis in most cases. © 2012 EURASIP. 14941498Glangeaud, F., Mari, J.-L., (1994) Wave Separation, , Technip EditionsSheriff, R.E., Geldart, L.P., (1995) Exploratory Seismology, , Cambridge University PressYilmaz, O., (2001) Seismic Data Analysis: Processing, Inversion and Interpretation of Seismic Data, 1. , SEG, second editionFreire, S.L.M., Ulrych, T.J., Application of singular value decomposition to vertical seismic profiling (1988) Geophysics, 53, pp. 778-785Kirlin, R.L., Done, W.J., (1999) Covariance Analysis for Seismic Signal Processing, , Society of Exploration GeophysicistsYedlin, M., Jones, I.F., Narod, B.B., Application of the karhunen-love transform to diffraction separation (1987) IEEE Transactions on Acoustics, Speech and Signal Processing, 35 (1), pp. 2-8Porsani, M.J., Silva, M.G., Melo, P.E.M., Ursin, B., Svd filtering applied to ground-roll attenuation (2010) Journal of Geophysics and Engineering, 7, pp. 284-289Vrabie, V.D., Mars, J.I., Lacoume, J.-L., Modified singular value decomposition by means of independent component analysis (2004) Signal Processing, (84), pp. 645-652Bekara, M., Van Der Baan, M., Local svd/ica for signal enhancement of pre-stack seismic data (2006) 68th EAGE Conference & ExhibitionCandes, E.J., Li, X., Ma, Y., Wright, J., Robust principal component analysis? (2011) Journal of the ACM, 58, pp. 1-37Chandrasekaran, V., Sanghavi, S., Parrilo, P.A., Willsky, A.S., Sparse and low-rank matrix decompositions (2009) Proc. 47th Ann. Allerton Conf. Communication, Control, and Computing Allerton, pp. 962-967Huang, P.-S., Chen, S.D., Smaragdis, P., Hasegawa-Johnson, M., Singing-voice separation from monaural recordings using robust principal component analysis (2012) Proc. of the IEEE ICASSPZhou, T., Tao, D., Godec: Randomized low-rank & sparse matrix decomposition in noisy case (2011) Proceeding of the International Conference on Machine Learning (ICML)Ding, X., He, L., Carin, L., Bayesian robust principal component analysis (2011) IEEE Transactions on Image Processing, 20 (12), pp. 3419-3430