• 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 LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    ML-descent: An optimization algorithm for FWI using machine learning

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Sun, Bingbing
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2019-08-10
    Permanent link to this record
    http://hdl.handle.net/10754/661903
    
    Metadata
    Show full item record
    Abstract
    Full-Waveform Inversion is a nonlinear inversion problem, and a typical optimization algorithm such as nonlinear conjugate-gradient or LBFGS would iteratively update the model along gradient-descent direction of the misfit function or a slight modification of it. Rather than using a hand-designed optimization algorithm, we trained a machine to learn an optimization algorithm which we refer to as”ML-descent” and applied it in FWI. Using recurrent neural network (RNN), we use the gradient of the misfit function as input for training and the hidden states in the RNN uses the history information of the gradient similar to an BFGS algorithm. However, unlike the fixed BFGS algorithm, the ML version evolves as the gradient directs it to evolve.The loss function for training is formulated by summarization of the FWI misfit function by the L2-norm of the data residual. Any well-defined nonlinear inverse problem can be locally approximated by a linear convex problem, and thus, in order to accelerate the training speed, we train the neural network using the solution of randomly generated quadratic functions instead of the time-consuming FWI gradient. We use the Marmousi example to demonstrate that the ML-descent method outperform the steepest descent method, and the energy in the deeper part of the model can be compensable well by the ML-descent when the pseudo-inverse of the Hessian is not incorporated in the gradient of FWI.
    Citation
    Sun, B., & Alkhalifah, T. (2019). ML-descent: An optimization algorithm for FWI using machine learning. SEG Technical Program Expanded Abstracts 2019. doi:10.1190/segam2019-3215304.1
    Sponsors
    We thank KAUST for the funding of this research and the members of SWAG group for useful discussions.
    Publisher
    Society of Exploration Geophysicists
    Conference/Event name
    Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019
    DOI
    10.1190/segam2019-3215304.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2019-3215304.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2019-3215304.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

    entitlement

     
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    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.