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    Leveraging domain adaptation for efficient seismic denoising

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    Type
    Conference Paper
    Authors
    Birnie, Claire Emma
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
    Physical Science and Engineering (PSE) Division
    Earth Science and Engineering Program
    Date
    2022-05-03
    Permanent link to this record
    http://hdl.handle.net/10754/676710
    
    Metadata
    Show full item record
    Abstract
    The selection of training data for deep learning procedures dictates both the neural network's performance and its applicability to further datasets. Particularly in seismic applications, the selection is non-trivial with the common approaches of manually labelling field data or generating synthetic data both exhibiting severe limitations. The former in its inability to outperform conventional approaches required for the label generation and the later in its inability to properly represent future application data. Domain adaptation, through input features and label transformations, offers the potential to leverage on the benefits of both these approaches while reducing their drawbacks. In this work we illustrate how vital information from field data can be incorporated into a training procedure on synthetic data with the trained network successfully applied on the field data afterwards, despite large differences between the training and inference, i.e., synthetic and field, datasets. Furthermore, we illustrate how an inverse correlation procedure can be incorporated into the training procedure in an attempt to maintain the original wavefield properties.
    Citation
    Birnie, C., & Alkhalifah, T. (2022). Leveraging domain adaptation for efficient seismic denoising. Energy in Data Conference, Austin, Texas, 20–23 February 2022. https://doi.org/10.7462/eid2022-04.1
    Sponsors
    The authors thank Prof. M. Ravasi, Dr O. Ovcharenko and Dr H. Wang for insightful discussions, as well as the wider KAUST Seismic Wave Analysis Group. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia
    Publisher
    Energy in Data
    Conference/Event name
    Energy in Data Conference
    DOI
    10.7462/eid2022-04.1
    Additional Links
    https://library.seg.org/doi/10.7462/eid2022-04.1
    ae974a485f413a2113503eed53cd6c53
    10.7462/eid2022-04.1
    Scopus Count
    Collections
    Conference Papers; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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