Integrating U-nets into a Multi-scale Full-waveform Inversion for Salt Body Building

Research output: Contribution to journalArticlepeer-review


In salt provinces, full-waveform inversion (FWI) is most likely to fail when starting with a poor initial model that lacks the salt information. Conventionally, salt bodies are included in the FWI starting model by interpreting the salt boundaries from seismic images, which is time-consuming and prone to error. Studies show that FWI can improve the interpreted salt provided that the data have long offsets, and low frequencies, which is not always the case. Thus, we develop an approach to invert for the salt body starting from a poor initial model, limited data offsets, and the absence of low frequencies. We leverage deep learning to apply multi-stage flooding and unflooding of the velocity model. Specifically, we apply a multi-scale FWI using three frequency bandwidths.We apply a network after each frequency scale. After the first two bandwidths, the networks are trained to flood the salt, while the network after the last frequency bandwidth is trained to unflood it. We follow the unflooding step, with a final FWI. We verify the method on the synthetic BP 2004 salt model benchmark. We only use the synthetic data of short offsets up to 6 km and remove frequencies below 3 Hz. We also apply the method to real vintage data acquired in the Gulf of Mexico region. The real data lack frequencies below 6 Hz and the streamer length is only 4.8 km. With these limitations, we manage to recover the salt body and verify the result by using them to image the data and analyze the resulting angle gathers.
Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
StatePublished - Aug 31 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-09-18
Acknowledgements: The authors would like to thank the sponsors of the DeepWave consortium for their support. They also thank the members of seismic wave analysis group (SWAG) for their valuable insights. They extend their appreciation to the Supercomputing Laboratory at the King Abdullah University of Science and Technology (KAUST) for providing the computational resources.


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