• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Transfer learning for low frequency extrapolation from shot gathers for FWI applications

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    transfer_ovcharenko_et_al_2019.pdf
    Size:
    1.091Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Conference Paper
    Authors
    Ovcharenko, Oleg cc
    Kazei, Vladimir cc
    Peter, Daniel cc
    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
    2019-08-26
    Permanent link to this record
    http://hdl.handle.net/10754/661870
    
    Metadata
    Show full item record
    Abstract
    Low-frequency data proved to be crucial for robust full-waveform inversion (FWI) applications. However, acquiring those data in the field is a challenging and costly task. Deep neural networks can be trained to extrapolate missing low frequencies, but no optimal network configuration exists. Therefore, the search for an acceptable network architecture is a tedious empirical task whose outcome heavily affects the performance of the application. Here, we propose and utilize transfer learning to reduce the computational efforts otherwise spent on an optimal architecture search and an initial network training. We re-train the light-weight MobileNet convolutional network to infer low-frequency data from a frequency-domain representation of the individual shot-gathers, which leads to an efficient, yet accurate inference of low frequencies according to wavenumber theory. In particular, we show that the extrapolated 0.25 - 1 Hz from 2-4.5 Hz data are accurate enough for acoustic FWI on part of the original BP 2004 model and the Marmousi II model of double scale. We bridge the gap between the 1 Hz predicted and the 2 Hz modeled data by the application of a Sobolev space norm regularization.
    Citation
    Ovcharenko, O., Kazei, V., Peter, D., & Alkhalifah, T. (2019). Transfer Learning For Low Frequency Extrapolation From Shot Gathers For FWI Applications. 81st EAGE Conference and Exhibition 2019. doi:10.3997/2214-4609.201901617
    Publisher
    EAGE Publications BV
    Conference/Event name
    81st EAGE Conference and Exhibition 2019
    DOI
    10.3997/2214-4609.201901617
    Additional Links
    http://www.earthdoc.org/publication/publicationdetails/?publication=97373
    ae974a485f413a2113503eed53cd6c53
    10.3997/2214-4609.201901617
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Extreme Computing Research Center; Earth Science and Engineering Program

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.