In this paper an overview is given on the application of directional basis functions, known under the name Curvelets/Contourlets, to various aspects of seismic processing and imaging, which involve adaptive subtraction. Key concepts in the approach are the use of (i) directional basis functions that localize in both domains (e.g. space and angle); (ii) non-linear estimation, which corresponds to localized muting on the coefficients, possibly supplemented by constrained optimization. We will discuss applications that include multiple, ground-roll removal and migration denoising.Science, Faculty ofEarth and Ocean Sciences, Department ofUnreviewedFacultyOthe
A non-linear singularity-preserving solution to the least-squares seismic imaging problem with spars...
Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the c...
A non-linear edge-preserving solution to the least-squares migration problem with sparseness & illum...
In this paper an overview is given on the application of directional basis functions, known under th...
In this paper an overview is given on the application of directional basis functions, known under th...
In this paper we present examples of ground roll attenuation for synthetic and real data gathers by ...
In this dissertation we address the problem of adapting frequency domain tiling using the curvelet t...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which multi...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which mult...
In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic ...
Running head: Curvelet-based processing In this letter, the solutions to three seismic processing pr...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which multi...
AbstractA curvelet is a new and effective spectral transform, that allows sparse representations of ...
In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three proble...
In this abstract, we present a nonlinear curvelet-based sparsity promoting formulation of a seismic ...
A non-linear singularity-preserving solution to the least-squares seismic imaging problem with spars...
Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the c...
A non-linear edge-preserving solution to the least-squares migration problem with sparseness & illum...
In this paper an overview is given on the application of directional basis functions, known under th...
In this paper an overview is given on the application of directional basis functions, known under th...
In this paper we present examples of ground roll attenuation for synthetic and real data gathers by ...
In this dissertation we address the problem of adapting frequency domain tiling using the curvelet t...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which multi...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which mult...
In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic ...
Running head: Curvelet-based processing In this letter, the solutions to three seismic processing pr...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which multi...
AbstractA curvelet is a new and effective spectral transform, that allows sparse representations of ...
In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three proble...
In this abstract, we present a nonlinear curvelet-based sparsity promoting formulation of a seismic ...
A non-linear singularity-preserving solution to the least-squares seismic imaging problem with spars...
Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the c...
A non-linear edge-preserving solution to the least-squares migration problem with sparseness & illum...