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- W2899059684 abstract "Multidimensional seismic data reconstruction has emerged as a primary topic of research in the field of seismic data processing. Although there exists a large number of algorithms for multidimensional seismic data reconstruction, they often adopt the [Formula: see text] norm to measure the discrepancy between observed and reconstructed data. Strictly speaking, these algorithms assume well-behaved noise that ideally follows a Gaussian distribution. When erratic noise contaminates the seismic traces, a 5D reconstruction must adopt a robust criterion to measure the difference between observed and reconstructed data. We develop a new formulation to the parallel matrix factorization tensor completion method and adapt it for coping with erratic noise. We use synthetic and field-data examples to examine our robust reconstruction technique." @default.
- W2899059684 created "2018-11-09" @default.
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- W2899059684 date "2019-03-01" @default.
- W2899059684 modified "2023-10-17" @default.
- W2899059684 title "Robust tensor-completion algorithm for 5D seismic-data reconstruction" @default.
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- W2899059684 doi "https://doi.org/10.1190/geo2018-0109.1" @default.
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