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    Wavefield reconstruction inversion via machine learned functions

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    Wavefield reconstruction inversion via ML (1).pdf
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    1.180Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Song, Chao cc
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2020-09-30
    Permanent link to this record
    http://hdl.handle.net/10754/667568
    
    Metadata
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    Abstract
    Wavefield reconstruction inversion (WRI) is a PDE constrained optimization problem that aims to mitigate cycle skipping in full-waveform inversion (FWI) among other potential features. WRI is often implemented in the frequency domain, and thus, requires expensive matrix inversions to reconstruct the wavefield. A recently introduced machine learning (ML) framework, called physics-informed neural networks (NNs), is used to predict PDE solutions by setting the physical laws as loss functions. These NNs have shown their effectiveness in solving the Helmholtz equation specifically for the scattered wavefield. By including the recorded data at the sensors’ locations as a data constraint, the NNs can predict the wavefields which simultaneously fit the recorded data and satisfy the Helmholtz equation for a given initial velocity model. Using the predicted wavefields, we build another independent NN to predict the velocity that fits the wavefield. In this new NN, we use spatial coordinates as input to the network, and use the scattered Helmholtz wave to define the loss function. After we train this deep neural network, we are able to predict the velocity in the domain of interest. We demonstrate the potential of the proposed method using a square anomaly model and a simple layered model, and the initial results considering single frequency data show that the ML-based WRI is able to invert for reasonable velocity models.
    Citation
    Song, C., & Alkhalifah, T. (2020). Wavefield reconstruction inversion via machine learned functions. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3427351.1
    Sponsors
    We thank KAUST for its support and the SWAG group for thecollaborative environment. This work utilized the resources ofthe Supercomputing Laboratory at King Abdullah Universityof Science and Technology (K AUST) in Thuwal, Saudi Ara-bia, and we are grateful for that.
    Publisher
    Society of Exploration Geophysicists
    Conference/Event name
    SEG International Exposition and 90th Annual Meeting
    DOI
    10.1190/segam2020-3427351.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2020-3427351.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2020-3427351.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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