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Data regularization using Gaussian beams decomposition and sparse norms
-
Yanfei Wang
,
Peng Liu
Veröffentlicht/Copyright:
1. Februar 2013
Abstract.
We consider seismic data regularization problems in this paper. Gaussian beam decomposition model is proposed for seismic data representation. To solve the representation problem, an l0 quasi-norm minimization model with different smooth approximations is proposed. To solve the l0 quasi-norm minimization problem, a projected gradient method with nonmonotone choice of iterative steps is developed. Numerical simulations on one-dimensional and two-dimensional seismic imaging problems are performed to verify the feasibility of our methods.
Received: 2012-05-08
Published Online: 2013-02-01
Published in Print: 2013-02-01
© 2013 by Walter de Gruyter Berlin Boston
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Schlagwörter für diesen Artikel
Data regularization;
Gaussian beams;
l0 optimization;
sparse inversion
Artikel in diesem Heft
- Masthead
- Data regularization using Gaussian beams decomposition and sparse norms
- Material parameter estimation and hypothesis testing on a 1D viscoelastic stenosis model: Methodology
- The use of statistical tests to calibrate the normal SABR model
- Unique determination of potentials and semilinear terms of semilinear elliptic equations from partial Cauchy data
- Irregular nonlinear operator equations: Tikhonov's regularization and iterative approximation
- On a multidimensional integral equation with data supported by low-dimensional analytic manifolds
- Inverse problem for elliptic equation in a Banach space with Bitsadze–Samarsky boundary value conditions
- Certain problems of synchronization theory
