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Robust tensor-completion algorithm for 5D seismic-data reconstruction
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DOI：10.1190/GEO2018-0109.1.png)
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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 l(2 )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.
Keyword:
LOW-RANK
FOURIER RECONSTRUCTION
TRACE INTERPOLATION
MATRIX
APPROXIMATION
MINIMIZATION
TRANSFORM
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3.2
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8.4K
被引数:
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