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    Extrapolating low-frequency prestack land data with deep learning

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    seg_2020_low.pdf
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    Type
    Conference Paper
    Authors
    Ovcharenko, Oleg cc
    Kazei, Vladimir cc
    Plotnitskiy, Pavel
    Peter, Daniel cc
    Silvestrov, Ilya
    Bakulin, Andrey
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Science and Engineering Program
    Extreme Computing Research Center
    King Abdullah University of Science and Technology
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2020-09-30
    Online Publication Date
    2020-09-30
    Print Publication Date
    2020-09-30
    Permanent link to this record
    http://hdl.handle.net/10754/665480
    
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    Abstract
    Missing low-frequency content in seismic data is a common challenge for seismic inversion. Long wavelengths are necessary to reveal large structures in the subsurface and to build an acceptable starting point for later iterations of full-waveform inversion (FWI). High-frequency land seismic data are particularly challenging due to the elastic nature of the Earth contrasting with acoustic air at the typically rugged free surface, which makes the use of low frequencies even more vital to the inversion. We propose a supervised deep learning framework for bandwidth extrapolation of prestack elastic data in the time domain. We utilize a Convolutional Neural Network (CNN) with a UNet-inspired architecture to convert portions of band-limited shot gathers from 5-15 Hz to 0-5 Hz band. In the synthetic experiment, we train the network on 192x192 patches of wavefields simulated for different cross-sections of the elastic SEAM Arid model with free-surface. Then, we test the network on unseen shot gathers from the same model to demonstrate the viability of the approach. The results show promise for future field data applications.
    Citation
    Ovcharenko, O., Kazei, V., Plotnitskiy, P., Peter, D., Silvestrov, I., Bakulin, A., & Alkhalifah, T. (2020). Extrapolating low-frequency prestack land data with deep learning. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3427522.1
    Sponsors
    We thank Anatoly Baumstein from ExxonMobil for interestingdiscussion on low-frequency extrapolation at the EAGE An-nual Meeting. The research reported in this publication wassupported by funding from Saudi Aramco and King AbdullahUniversity of Science and Technology (KAUST).
    Publisher
    Society of Exploration Geophysicists
    DOI
    10.1190/segam2020-3427522.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2020-3427522.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2020-3427522.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Extreme Computing Research Center; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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