Abstract
Conventional full-waveform inversion (FWI) often fails to retrieve the unknown model parameters from noisy seismic data. A successful FWI implementation usually requires to follow a multistage recovery approach, starting from the retrieval of the lower model wavenumbers (tomography) to those with the higher resolutions (migration). Here, we propose a new method based on the flux-corrected transport (FCT) technique often used in computational fluid dynamics for the removal of instabilities in a shock profile. FCT involves three finite-difference steps: a transport, a diffusion and an antidiffusion process. This third step involves non-linear 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 without any strong ripples like shock waves, we exclude the non-linear step from FCT, which allows us to evaluate the novel FWI gradient efficiently. As a result, we achieve a converging FWI model by gradually reducing the diffusive flux-correction. We demonstrate the versatility of FCT-based FWI on a noisy synthetic data set from the Marmousi II model and a marine field data set from offshore Australia.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2147-2164 |
| Number of pages | 18 |
| Journal | Geophysical Journal International |
| Volume | 217 |
| Issue number | 3 |
| DOIs | |
| State | Published - Feb 25 2019 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). We would like to thank KAUST for its support, as well as, all members of the Seismic Wave Analysis Group (SWAG) for the fruitful discussions. We also thank Dr. David Ketcheson from Applied Mathematics and Computational Science at KAUST for his valuable suggestions. For computer time, this research used the resources of the Supercomputing Laboratory and IT Research Computing at KAUST. In addition, this research used the resources of the Core Labs of KAUST. We acknowledge Thomas Theussl from Visualization Core Laboratory at KAUST, for his assistance in displaying some of the results reported in this work. The real data shown in this study are proprietary and provided by courtesy of CGG.
