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    Seismic Inversion with Deep Neural Networks: a Feasibility Analysis

    Access Status
    Fulltext not available
    Authors
    Puzyrev, Vladimir
    Egorov, Anton
    Pirogova, Anastasia
    Elders, Christopher
    Otto, Claus
    Date
    2019
    Type
    Conference Paper
    
    Metadata
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    Citation
    Puzyrev, V. and Egorov, A. and Pirogova, A. and Elders, C. and Otto, C. 2019. Seismic Inversion with Deep Neural Networks: a Feasibility Analysis. In: 81st EAGE Conference and Exhibition 2019, 3rd Jun 2019, London, UK.
    Source Conference
    81st EAGE Conference and Exhibition 2019
    DOI
    10.3997/2214-4609.201900765
    Faculty
    Faculty of Science and Engineering
    School
    School of Earth and Planetary Sciences (EPS)
    URI
    http://hdl.handle.net/20.500.11937/76413
    Collection
    • Curtin Research Publications
    Abstract

    We investigate deep learning approaches to inversion of a 1D model of the subsurface using synthetic surface seismic and VSP data. Several deep neural networks based on three different architectures are developed and tested. The matrix propagator technique is used to generate the synthetic data for network training. The pre-trained deep neural networks can instantly predict velocity models from new data in a single step. The synthetic datasets used in training can be extended by adding random noise to the existing data, thus making the method closer to real-world conditions.

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