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    Modelling Elastic Properties of Clastic Rocks from Microtomographic Images Using Multi-Mineral Segmentation and Machine Learning

    Liang J 2022.pdf (13.18Mb)
    Access Status
    Open access
    Authors
    Liang, Jiabin
    Date
    2022
    Supervisor
    Boris Gurevich
    Maxim Lebedev
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    WASM: Minerals, Energy and Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/88666
    Collection
    • Curtin Theses
    Abstract

    Modelling elastic properties from micro-CT images of rocks is essential for geophysical characterisation of the subsurface. This is achieved through an advanced physics-based multi-mineral image segmentation workflow, which is then automated using machine learning. The effects of intergranular contacts that are below the micro-CT resolution are modelled by a workflow that extracts their elastic properties from rock microstructure and ultrasonic measurements. I also developed a workflow that successfully detects pressure-induced deformation in micro-CT images.

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