Low-frequency signal content in seismic data as well as a realistic initial model are key ingredients for robust and efficient full-waveform inversions. However, acquiring low-frequency data is challenging in practice for active seismic surveys. Data-driven solutions show promise to extrapolate low-frequency data given a high-frequency counterpart. While being established for synthetic acoustic examples, the application of bandwidth extrapolation to field datasets remains non-trivial. Rather than aiming to reach superior accuracy in bandwidth extrapolation, we propose to jointly reconstruct low-frequency data and a smooth background subsurface model within a multi-task deep learning framework. We automatically balance data, model and trace-wise correlation loss terms in the objective functional and show that this approach improves the extrapolation capability of the network. We also design a pipeline for generating synthetic data suitable for field data applications. Finally, we apply the same trained network to synthetic and real marine streamer datasets and run an elastic full-waveform inversion from the extrapolated dataset.
|Original language||English (US)|
|Number of pages||1|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|State||Published - Jun 23 2022|
Bibliographical noteKAUST Repository Item: Exported on 2022-06-27
Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia. We thank the members of the Seismic Modeling and Inversion Group (SMI) and the Seismic Wave Analysis Group (SWAG) for the constructive discussions. The real data shown in this study are proprietary to and provided courtesy of CGG. The well-log information is provided by Geoscience Australia.
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)