DL-fused elastic FWI: Application to marine streamer data

Pavel Plotnitskii, Oleg Ovcharenko, Vladimir Kazei, Daniel Peter, Tariq Ali Alkhalifah

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Low-frequency data is crucial for successful retrieval of low-wavenumber model component in seismic full-waveform inversion (FWI), yet it is often limited by hardware. Deep learning (DL) can fuse early high-wavenumber updates of elastic FWI and map them into desired low-wavenumber updates that would be available from low-frequency data. FusionNET-based convolutional neural network (CNN) trained on a synthetic dataset produces meaningful low-wavenumber models taking initial FWI iterations on field data as inputs. Elastic FWI initiated from ”DL-fused” model updates shows improved convergence on synthetic data generated in unrelated to training dataset models and on real-world marine streamer data.
Original languageEnglish (US)
Title of host publicationSecond International Meeting for Applied Geoscience & Energy
PublisherSociety of Exploration Geophysicists and American Association of Petroleum Geologists
StatePublished - Aug 15 2022


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