• Login
    View Item 
    •   Home
    • Theses and Dissertations
    • MS Theses
    • View Item
    •   Home
    • Theses and Dissertations
    • MS Theses
    • 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

    Eikonal Solution Using Physics-Informed Neural Networks for Global Seismic Travel Time Modelling

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Taufik _MSc_thesis.pdf
    Size:
    15.23Mb
    Format:
    PDF
    Download
    Type
    Thesis
    Authors
    Taufik, Mohammad Hasyim cc
    Advisors
    Alkhalifah, Tariq Ali cc
    Committee members
    Peter, Daniel cc
    Ravasi, Matteo cc
    Program
    Earth Science and Engineering
    KAUST Department
    Physical Science and Engineering (PSE) Division
    Date
    2021-12
    Permanent link to this record
    http://hdl.handle.net/10754/673952
    
    Metadata
    Show full item record
    Abstract
    Being able to determine how much time it takes for a seismic wave to travel from one point to another is essential in geophysics. One can achieve this goal under the asymptotic ray assumption and end up with the so-called Eikonal equation. The equation finds itself to be beneficial across science and engineering. In geophysics, especially the global seismology field, the solution of this equation is primarily used to perform travel time tomography and earthquake relocation application. In this research I propose a novel scheme to solve the Eikonal equation under two main objectives in mind: being able to compute more accurate first-arrival travel time using Three-dimensional (3-D) velocity model and also being as efficient as the standard procedure. The proposed method is using a physics-informed neural network (PINN). The forward problem is formulated such that the physical equation is the driving component of the minimization of the objective function. The velocity model used on this research is the second generation of the three-dimensional global adjoint tomographic model, GLAD-M25, to account for anelastic behaviour of the Earth. From the numerical tests, I observed one unique feature in using PINNs to solve the Eikonal equation. I demonstrate that I can use a velocity model which has incomplete velocity information in it and still able to model accurately in some regions the travel time. The results show that the proposed method achieves a significant improvement on the velocity validation and more importantly, is able to calculate the first-arrival travel time using a full three-dimensional global tomographic model (GLAD-M25). The validation process is done by comparing the input velocity data with the recovered velocity from the modelled travel time. The residuals for all depth is below -1 to 1 % error and the recovered velocity and input data are align with a cosine similarity value around 0.999. The main limitation pertaining to the first iteration model proposed on this research is its training cost. For each epoch, given the large number of batches, the training takes around 52.383 minutes. However, once the model is trained, the inference process is comparable to a standard Eikonal solver.
    Citation
    Taufik, M. H. (2021). Eikonal Solution Using Physics-Informed Neural Networks for Global Seismic Travel Time Modelling. KAUST Research Repository. https://doi.org/10.25781/KAUST-476A7
    DOI
    10.25781/KAUST-476A7
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-476A7
    Scopus Count
    Collections
    MS Theses; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

    entitlement

     
    DSpace software copyright © 2002-2023  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.