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Wave equation multiple modelling: acquisition independent 3D SRME

Weisser, T. / Pica, A. / Herrmann, P. / Taylor, R.

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Wave equation multiple modelling: acquisition independent 3D SRME

Weisser, T. / Pica, A. / Herrmann, P. / Taylor, R.

Wave equation multiple modelling: acquisition independent 3D SRME

Weisser, T. / Pica, A. / Herrmann, P. / Taylor, R.

Access

Access via TIB

Check availability in my library

Order at Subito €

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Document information

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Document information

Title:

Wave equation multiple modelling: acquisition independent 3D SRME

Contributors:

Weisser, T. (author) / Pica, A. (author) / Herrmann, P. (author) / Taylor, R. (author)

Published in:

FIRST BREAK ; 75-80

Publication date:

2006-01-01

Size:

6 pages

ISSN:

0263-5046

Type of media:

Article (Journal)

Type of material:

Language:

English

Classification:

DDC: 550

Aaron, P. / Barnes, S. / van Borselen, R. et al. | British Library Online Contents | 2011

General surface multiple prediction: a flexible 3D SRME algorithm Ideal 3D SRME (surface-related multiple elimination) has geometric requirements for its input data that are not met by marine streamer surveys. Ian Moore and Bill Dragoset offer a solution

British Library Online Contents | 2008

Special Topic: Data Processing - General surface multiple prediction: A flexible 3D SRME algorithm - Ideal 3D SRME (surface-related multiple elimination) has geometric requirements for its input data that are not met by marine streamer surveys. Ian Moore and Bill Dragoset offer a solution.

Moore, Ian | Online Contents | 2008

Special Topic: Data Processing - Wave equation multiple modelling: Acquisition independent 3D SRME - T. Weisser, A. Pica, P. Herrmann, and R. Taylor of CGG discuss the case for an alternative approach to 3D surface-related multiple elimination which does not require dense acquisition and has a number of applications.

Weisser, T. | Online Contents | 2006

Technology Feature Enhanced data reconstruction for true-azimuth 3D SRME Surface-related multiple elimination (SRME) has become the industry-preferred method for complex 3D multiple removal, mainly for its independence of any geological information. However, a dense set of recorded data is needed to predict the multiples accurately, and success is dependent on the way missing data is reconstructed. Peter Aaron, Simon Barnes, Roald van Borselen and Rob Hegge present an enhanced data reconstruction method to resolve this issue.

Online Contents | 2011