Spelling suggestions: "subject:"discrete cervelet transform""
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A parallel windowed fast discrete curvelet transform applied to seismic processingThomson, 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.
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Multiple prediction from incomplete data with the focused curvelet transformHerrmann, 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.
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