Sparse Thresholding for Regularization, Interpolation, and Dealiasing
SEG 2021, Expanded Abstract
Aaron Stanton
https://library.seg.org/doi/10.1190/segam2021-3584024.1
Sparse thresholding algorithms are useful for interpolating data acquired with random spatial sampling patterns because the sampling creates incoherent aliasing artefacts that are lower in amplitude than the underlying signal.
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