• 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

    Seismic inversion by multi-dimensional Newtonian machine learning

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    segam2020-3425975.1.pdf
    Size:
    616.0Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Conference Paper
    Authors
    Chen, Yuqing
    Saygin, Erdinc
    Schuster, Gerard T. cc
    KAUST Department
    Center for Subsurface Imaging and Fluid Modeling
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    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/10754/665468
    
    Metadata
    Show full item record
    Abstract
    Newtonian machine learning (NML) inversion has been shown to accurately recover the low-to-intermediate wavenumber information of subsurface velocity models. This method uses the wave-equation inversion kernel to invert the skeletonized data that is automatically learned by an autoencoder. The skeletonised data is a one-dimensional latent-space representation of the seismic trace. However, for a complicated dataset, the decoded waveform could lose some details if the latent space dimension is set to one, which leads to a low-resolution NML tomogram. To mitigate this problem, an autoencoder with a higher dimensional latent space is needed to encode and decode the seismic data. In this paper, we present a wave equation inversion that inverts the multi-dimensional latent variables of an autoencoder for the subsurface velocity model. The multi-variable implicit function theorem is used to determine the perturbation of the multi-dimensional skeletonised data with respect to the velocity perturbations. In this case, each dimension of the latent variable is characterized one gradient and the velocity model is updated by the weighted sum of all these gradients. Numerical results suggest that the multidimensional NML inverted result can achieve a higher resolution in the tomogram compared to the conventional single dimensional NML inversion.
    Citation
    Chen, Y., Saygin, E., & Schuster, G. T. (2020). Seismic inversion by multi-dimensional Newtonian machine learning. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3425975.1
    Sponsors
    We thanks to the Deep Earth Imaging Future Science Platformof CSIRO for funding and computing resources of CSIRO.
    Publisher
    Society of Exploration Geophysicists
    DOI
    10.1190/segam2020-3425975.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2020-3425975.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2020-3425975.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; 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.