Add to ORKGIn this abstract, we present a nonlinear curvelet-based sparsitypromoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions.Science, Faculty ofEarth and Ocean Sciences, Department ofUnreviewedGraduateFacult
A recent robust multiple-elimination technique, based on the underlying principle that relates prima...
In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic ...
In this abstract, we present a nonlinear curvelet-based sparsity promoting formulation of a seismic ...
In this abstract, we present a nonlinear curvelet-based sparsitypromoting formulation for the primar...
A non-linear primary-multiple separation method using curvelets frames is presented. The advantage o...
A non-linear primary-multiple separation method using curvelets frames is presented. The advantage o...
In this abstract, we present a novel primary-multiple separation scheme which makes use of the spars...
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 multi...
In many exploration areas, successful separation of primaries and multiples greatly determines the q...
The process of obtaining high quality seismic images is very challenging when exploring new areas th...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which mult...
Incomplete data represents a major challenge for a successful prediction and subsequent removal of m...
In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three proble...
A robust data interpolation method using curvelets frames is presented. The advantage of this method...
A recent robust multiple-elimination technique, based on the underlying principle that relates prima...
In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic ...
In this abstract, we present a nonlinear curvelet-based sparsity promoting formulation of a seismic ...
In this abstract, we present a nonlinear curvelet-based sparsitypromoting formulation for the primar...
A non-linear primary-multiple separation method using curvelets frames is presented. The advantage o...
A non-linear primary-multiple separation method using curvelets frames is presented. The advantage o...
In this abstract, we present a novel primary-multiple separation scheme which makes use of the spars...
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 multi...
In many exploration areas, successful separation of primaries and multiples greatly determines the q...
The process of obtaining high quality seismic images is very challenging when exploring new areas th...
Predictive multiple suppression methods consist of two main steps: a prediction step, in which mult...
Incomplete data represents a major challenge for a successful prediction and subsequent removal of m...
In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three proble...
A robust data interpolation method using curvelets frames is presented. The advantage of this method...
A recent robust multiple-elimination technique, based on the underlying principle that relates prima...
In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic ...
In this abstract, we present a nonlinear curvelet-based sparsity promoting formulation of a seismic ...