Automatic Wave Equation Migration Velocity Analysis by Focusing Subsurface Virtual Sources

Bingbing Sun, Tariq Ali Alkhalifah

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Macro velocity model building is important for subsequent pre-stack depth migration and full waveform inversion. Wave equation migration velocity analysis (WEMVA) utilizes the band-limited waveform to invert for the velocity. Normally, inversion would be implemented by focusing the subsurface offset common image gathers (SOCIGs). We re-examine this concept with a different perspective: In subsurface offset domain, using extended Born modeling, the recorded data can be considered as invariant with respect to the perturbation of the position of the virtual sources and velocity at the same time. A linear system connecting the perturbation of the position of those virtual sources and velocity is derived and solved subsequently by Conjugate Gradient method. In theory, the perturbation of the position of the virtual sources is given by the Rytov approximation. Thus, compared to the Born approximation, it relaxes the dependency on amplitude and makes the proposed method more applicable for real data. We demonstrate the effectiveness of the approach by applying the proposed method on both isotropic and anisotropic VTI synthetic data. A real dataset example verifies the robustness of the proposed method.
Original languageEnglish (US)
Pages (from-to)U1-U8
Number of pages1
JournalGEOPHYSICS
Volume83
Issue number2
DOIs
StatePublished - Dec 22 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The real dataset from Australia shown in this study is proprietary to and provided courtesy of CGG company. The authors would like to thank Statoil ASA and the Volve license partners ExxonMobil Exploration and Production Norway AS and Bayerngas Norge AS for the release of the Volve data. We thank the associate editor J. Schleicher and the reviewers K. Wapenaar, and F. Perrone for their constructive comments that helped to improve the paper.The authors also thank for the resources of the supercomputing Laboratory at KAUST.

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