Image-guided wavefield tomography for VTI media

Vladimir Li, Antoine Guitton, Ilya Tsvankin, Tariq Ali Alkhalifah

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

Abstract

Transversely isotropic (TI) models have become essential in generating accurate depth images from seismic data. Here, we develop image-domain tomography (IDT) for building acoustic VTI (TI with a vertical symmetry axis) models from P-wave reflection data. Based on a separable dispersion relation, the modeling operator extrapolates only P-wavefields without the shear-wave artifacts. The inversion algorithm includes least-squares reverse-time migration (LSRTM), which improves the quality of the extended images and accuracy of parameter estimation. Whereas the zero-dip NMO velocity (V) and anellipticity parameter η are updated by focusing energy in space-lag LSRTM gathers, the Thomsen parameter δ is constrained by image-guided interpolation between two or more boreholes. We also apply image-guided smoothing to the IDT gradients of V and η to steer the inversion towards geologically plausible models. To mitigate the trade-off between V and η, we adopt a multistage approach that gradually relaxes the constraints on the spatial η-variation. The robustness of the algorithm is demonstrated on the elastic VTI Marmousi-II model. We also present preliminary inversion results for a line from a 3D data set acquired in the Gulf of Mexico.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2018
PublisherSociety of Exploration Geophysicists
Pages5183-5187
Number of pages5
DOIs
StatePublished - Aug 27 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the Consortium Project on SeismicInverse Methods for Complex Structures at CWP and competitive research funding from King Abdullah University of Science and Technology (KAUST). We are grateful to Shell Explorationand Production Company for sharing the 3D Gulf of Mexicodata set with Colorado School of Mines, and their permissionto publish the results using this data set. We also thank DanielRocha (CWP) for help in preprocessing the data.

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