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Seismic wave separation by means of robust principal component analysis

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.