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    Mapping Planetary Surface Ages at Ultimate Resolutions with Machine Learning: The Moon

    Fairweather JH 2023 Public.pdf (18.10Mb)
    Access Status
    Open access
    Authors
    Fairweather, John Hugh
    Date
    2023
    Supervisor
    Gretchen Benedix
    Anthony Lagain
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Earth and Planetary Sciences
    URI
    http://hdl.handle.net/20.500.11937/95165
    Collection
    • Curtin Theses
    Abstract

    The density of impact craters upon a terrestrial surface can give an accurate estimate of the surface's formation age. The Moon has hundreds of millions of impact craters scattered across its surface. Through the power of machine learning, we can automatically count those craters to date any surface on the Moon!

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