A nonlinear approach of elastic reflection waveform inversion

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Elastic full waveform inversion (EFWI) embodies the original intention of waveform inversion at its inception as it is a better representation of the mostly solid Earth. However, compared with the acoustic P-wave assumption, EFWI for P- and S-wave velocities using multi-component data admitted mixed results. Full waveform inversion (FWI) is a highly nonlinear problem and this nonlinearity only increases under the elastic assumption. Reflection waveform inversion (RWI) can mitigate the nonlinearity by relying on transmissions from reflections focused on inverting low wavenumber components of the model. In our elastic endeavor, we split the P- and S-wave velocities into low wavenumber and perturbation components and propose a nonlinear approach to invert for both of them. The new optimization problem is built on an objective function that depends on both background and perturbation models. We utilize an equivalent stress source based on the model perturbation to generate reflection instead of demigrating from an image, which is applied in conventional RWI. Application on a slice of an ocean-bottom data shows that our method can efficiently update the low wavenumber parts of the model, but more so, obtain perturbations that can be added to the low wavenumbers for a high resolution output.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2016
PublisherSociety of Exploration Geophysicists
Pages1421-1425
Number of pages5
DOIs
StatePublished - Sep 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors would like to thank Statoil ASA and the Volve license partners ExxonMobil E&P Norway AS and Bayerngas Norge AS, for the release of the Volve data. The authors would like to thank Marianne Houbiers from Statoil, who gave some very helpful suggestions and corrections. We also thank KAUST for its support and we thank the SWAG group for collaborative environment.

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