DL-fused elastic FWI: Application to marine streamer data

Abstract
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.

Citation
Plotnitskii, P., Ovcharenko, O., Kazei, V., Peter, D., & Alkhalifah, T. (2022). DL-fused elastic FWI: Application to marine streamer data. Second International Meeting for Applied Geoscience & Energy. https://doi.org/10.1190/image2022-3745846.1

Publisher
Society of Exploration Geophysicists and American Association of Petroleum Geologists

Conference/Event Name
Second International Meeting for Applied Geoscience & Energy

DOI
10.1190/image2022-3745846.1

Additional Links
https://library.seg.org/doi/10.1190/image2022-3745846.1

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