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Curvelet imaging and processing : adaptive multiple elimination

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Title: Curvelet imaging and processing : adaptive multiple elimination
Author: Herrmann, Felix J.; Verschuur, Eric
Subject Keywords adaptive subtraction;curvelets;denoising;curvelet transform;3D;4D
Issue Date: 2004
Publicly Available in cIRcle 2008-03-26
Publisher Canadian Society of Exploration Geophysicists
Citation: Herrmann, Felix J., Verschuur, Eric. 2004. Curvelet imaging and processing: adaptive multiple elimination. CSEG National Convention.
Abstract: Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatial-temporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression.
Affiliation: Science, Faculty ofEarth and Ocean Sciences, Department of
URI: http://hdl.handle.net/2429/612
Peer Review Status: Unreviewed
Scholarly Level: Faculty

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