Full-waveform inversion (FWI) iteratively recovers the unknown model parameters from seismic data. In practice, a successful FWI implementation often follows a multistage recovery approach: starting from the retrieval of the lower model wavenumbers (tomography) followed by the higher resolution ones (imaging). On that account, we propose a new method based on the flux-corrected transport (FCT) technique, used often in computational fluid dynamics owing to the removal of instabilities in a shock profile. FCT involves three finite-difference steps: a transport, a diffusion, and followed by an anti-diffusion. The third step, however, involves nonlinear operators such as maximum and minimum, which are non-differentiable in a classic sense. However, since the seismic source wavelet and the corresponding wavefield are relatively smooth and continuous in nature, and does not yield any strong ripples like shock waves, we unsubscribe to the non-linear step from FCT, which allows us to evaluate the FWI gradient. As a result, it accentuates no trouble in achieving a converging FWI model by gradually reducing the diffusive flux-correction amount. Those features are demonstrated on a dataset from the Marmousi II model with no frequency content less that 5 Hz. We initiate the inversion process for the remaining full-bandwidth of the dataset with a linear v(z) model. In addition, we show the versatility of the FCT based FWI on a marine field dataset from offshore Australia.
|Original language||English (US)|
|Title of host publication||SEG Technical Program Expanded Abstracts 2018|
|Publisher||Society of Exploration Geophysicists|
|Number of pages||5|
|State||Published - Aug 27 2018|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We would like to thank KAUST for its support, all members of Seismic Wave Analysis Group in KAUST and Dr. David Ketcheson for the fruitful discussions. For computer time, this research used the resources of the Supercomputing Laboratory and IT Research Computing at KAUST. We also thank CGG for providing the marine dataset example.