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Joint Low-rank and Sparse Inversion for Multidimensional Simultaneous Random/Erratic Noise Attenuation and Interpolation
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 77th EAGE Conference and Exhibition 2015, Jun 2015, Volume 2015, p.1 - 5
Abstract
Summary
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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