NDLTD Global ETD Search
  • Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 2
  • Tagged with
  • 2
  • 2
  • 2
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Search results

Showing 1 to 2 of 2 (0.105 seconds)

Spelling suggestions: "subject:"discrete cervelet transform""

1

A parallel windowed fast discrete curvelet transform applied to seismic processing

Thomson, Darren, Hennenfent, Gilles, Modzelewski, Henryk, Herrmann, Felix J. January 2006 (has links)
We propose using overlapping, tapered windows to process seismic data in parallel. This method consists of numerically tight linear operators and adjoints that are suitable for use in iterative algorithms. This method is also highly scalable and makes parallel processing of large seismic data sets feasible. We use this scheme to define the Parallel Windowed Fast Discrete Curvelet Transform (PWFDCT), which we apply to a seismic data interpolation algorithm. The successful performance of our parallel processing scheme and algorithm on a two-dimensional synthetic data is shown.
fast discrete curvelet transform
parallel processing
overlapping windows
parallelization
FDCT
fast fourier transform
2

Multiple prediction from incomplete data with the focused curvelet transform

Herrmann, Felix J., Wang, Deli, Hennenfent, Gilles January 2007 (has links)
Incomplete data represents a major challenge for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles this challenge in a two-step approach. During the first step, the recenly developed curvelet-based recovery by sparsity-promoting inversion (CRSI) is applied to the data, followed by a prediction of the primaries. During the second high-resolution step, the estimated primaries are used to improve the frequency content of the recovered data by combining the focal transform, defined in terms of the estimated primaries, with the curvelet transform. This focused curvelet transform leads to an improved recovery, which can subsequently be used as input for a second stage of multiple prediction and primary-multiple separation.
incomplete data
multiple removal
CRSI
discrete curvelet transform
data adaptive transform
curvelet regularized inversion
data recovery
sparse curvelet coefficient vector
sparsity
propagation
focal transform

Page generated in 0.1134 seconds