首页|Sparse decomposition of seismic data and migration using Gaussian beams with nonzero initial curvature
Sparse decomposition of seismic data and migration using Gaussian beams with nonzero initial curvature
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NSTL
Elsevier
We study problems associated with seismic data decomposition and migration imaging. We first represent the seismic data utilizing Gaussian beam basis functions, which have nonzero curvature, and then consider the sparse decomposition technique. The sparse decomposition problem is an l(0)-norm constrained minimization problem. In solving the l(0)-norm minimization, a polynomial Radon transform is performed to achieve sparsity, and a fast gradient descent method is used to calculate the waveform functions. The waveform functions can subsequently be used for sparse Gaussian beam migration. Compared with traditional sparse Gaussian beam methods, the seismic data can be properly reconstructed employing fewer Gaussian beams with nonzero initial curvature. The migration approach described in this paper is more efficient than the traditional sparse Gaussian beam migration. (C) 2018 The Authors. Published by Elsevier B.V.
NSTL
Elsevier
