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    Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock

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    Author
    Gerke, KM; Karsanina, MV; Mallants, D
    Date
    2015-11-02
    Source Title
    Scientific Reports
    Publisher
    NATURE PUBLISHING GROUP
    University of Melbourne Author/s
    GERKE, KIRILL
    Affiliation
    Infrastructure Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Gerke, K. M., Karsanina, M. V. & Mallants, D. (2015). Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock. SCIENTIFIC REPORTS, 5 (1), https://doi.org/10.1038/srep15880.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256531
    DOI
    10.1038/srep15880
    Abstract
    Spatial data captured with sensors of different resolution would provide a maximum degree of information if the data were to be merged into a single image representing all scales. We develop a general solution for merging multiscale categorical spatial data into a single dataset using stochastic reconstructions with rescaled correlation functions. The versatility of the method is demonstrated by merging three images of shale rock representing macro, micro and nanoscale spatial information on mineral, organic matter and porosity distribution. Merging multiscale images of shale rock is pivotal to quantify more reliably petrophysical properties needed for production optimization and environmental impacts minimization. Images obtained by X-ray microtomography and scanning electron microscopy were fused into a single image with predefined resolution. The methodology is sufficiently generic for implementation of other stochastic reconstruction techniques, any number of scales, any number of material phases, and any number of images for a given scale. The methodology can be further used to assess effective properties of fused porous media images or to compress voluminous spatial datasets for efficient data storage. Practical applications are not limited to petroleum engineering or more broadly geosciences, but will also find their way in material sciences, climatology, and remote sensing.

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