Recent developments in the field of compressed sensing enable us to take a new look on Cadzow / Singular Spectrum Analysis (SSA) filtering and its robust and interpolation derivatives. We formulate the problem of simultaneous random plus erratic noise attenuation and interpolation as a well-posed Joint Low-Rank and Sparse Inversion (JLRSI) convex optimization program. The JLRSI problem consists in a joint minimization of a nuclear norm term and a L1 norm term to recover the low-rank signal component from incomplete and noisy data. Numerical results on field data illustrate the effectiveness of our approach at recovering missing data and increasing the signal-to-noise ratio.


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