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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/563 |
| Date | January 2007 |
| Creators | Herrmann, Felix J., Wang, Deli, Hennenfent, Gilles |
| Publisher | Society of Exploration Geophysicists |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | text |
| Rights | Herrmann, Felix J. |
Page generated in 0.0028 seconds