Elastic full-waveform inversion (FWI) provides a better description of the subsurface information than those given by the acoustic assumption. However, it suffers from a more serious cycle-skipping problem compared with the latter. Reflection waveform inversion (RWI) is able to build a good background model, which can serve as an initial model for elastic FWI. Because, in RWI, we use the model perturbation to explicitly fit reflections, such perturbations should include density, which mainly affects the dynamics. We applied Born modeling to generate synthetic reflection data using optimized perturbations of the P- and S-wave velocities and density. The inversion for the perturbations of the P- and S-wave velocities and density is similar to elastic least-squares reverse time migration. An incorrect background model will lead to misfits mainly at the far offsets, which can be used to update the background P- and S-wave velocities along the reflection wavepath. We optimize the perturbations and background models in an alternate way. We use two synthetic examples and a field-data case to demonstrate our proposed elastic RWI algorithm. The results indicate that our elastic RWI with variable density is able to build reasonably good background models for elastic FWI with the absence of low frequencies, and it can deal with the variable density, which is required in real cases.
|Original language||English (US)|
|Number of pages||1|
|State||Published - Mar 20 2019|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We 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 views expressed in this paper are the views of the authors and do not necessarily reflect the views of Statoil ASA and the Volve field license partners. We would like to thank M. Houbiers from Statoil for the helpful comments and corrections. We thank J. Etgen, A. Abubakar, and two anonymous reviewers for their valuable comments. We also thank KAUST for its support and SWAG for the collaborative environment. This research is also sponsored by National Science and Technology major projects of China (grant no. 2017ZX05032-003-002) and Shandong Natural Science Fund (grant no. ZR2017MD014).