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    Dual-band generative learning for low-frequency extrapolation in seismic land data

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    Type
    Conference Paper
    Authors
    Ovcharenko, Oleg cc
    Kazei, Vladimir
    Peter, Daniel cc
    Silvestrov, Ilya
    Bakulin, Andrey
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Earth Science and Engineering Program
    Extreme Computing Research Center
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2021-09-01
    Online Publication Date
    2021-09-01
    Print Publication Date
    2021-09-01
    Permanent link to this record
    http://hdl.handle.net/10754/670950
    
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    Abstract
    The presence of low-frequency energy in seismic data can help mitigate cycle-skipping problems in full-waveform inversion. Unfortunately, the generation and recording of low-frequency signals in seismic exploration remains a non-trivial task. Extrapolation of missing low-frequency content in field data might be addressed in a data-driven framework. In particular, deep learning models trained on synthetic data could be used for inference on the field data. Such an implementation of switching application domains remains challenging. We, therefore, propose the concept of generative dual-band learning to facilitate the knowledge transfer between synthetic and field seismic data applications of low-frequency data extrapolation. We first explain the two-step procedure for training a generative adversarial network (GAN) that extrapolates low frequencies. Then, we describe the workflow for synthetic dataset generation. Finally, we explore the feasibility of the dual-band learning concept on real near-surface land data acquired in Saudi Arabia. The presence of low-frequency energy in seismic data can help mitigate cycle-skipping problems in full-waveform inversion. Unfortunately, the generation and recording of low-frequency signals in seismic exploration remains a non-trivial task. Extrapolation of missing low-frequency content in field data might be addressed in a data-driven framework. In particular, deep learning models trained on synthetic data could be used for inference on the field data. Such an implementation of switching application domains remains challenging. We, therefore, propose the concept of generative dual-band learning to facilitate the knowledge transfer between synthetic and field seismic data applications of low-frequency data extrapolation. We first explain the two-step procedure for training a generative adversarial network (GAN) that extrapolates low frequencies. Then, we describe the workflow for synthetic dataset generation. Finally, we explore the feasibility of the dual-band learning concept on real near-surface land data acquired in Saudi Arabia.
    Citation
    Ovcharenko, O., Kazei, V., Peter, D., Silvestrov, I., Bakulin, A., & Alkhalifah, T. (2021). Dual-band generative learning for low-frequency extrapolation in seismic land data. First International Meeting for Applied Geoscience & Energy Expanded Abstracts. doi:10.1190/segam2021-3579442.1
    Sponsors
    The research reported in this publication was supported by funding from Saudi Aramco and King Abdullah University of Science and Technology (KAUST).
    Publisher
    Society of Exploration Geophysicists
    DOI
    10.1190/segam2021-3579442.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2021-3579442.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2021-3579442.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Extreme Computing Research Center; Earth Science and Engineering Program

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