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    Approximating Nonlinear Relations Between Susceptibility andMagnetic Contents in Rocks Using Neural Networks

    Access Status
    Fulltext not available
    Authors
    Guo, W.
    Li, M.
    Li, Zhengxiang
    Whymark, G.
    Date
    2010
    Type
    Journal Article
    
    Metadata
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    Citation
    Guo, William W. and Li, Michael and Li, Zhengxiang and Whymark, Greg. 2010. Approximating Nonlinear Relations Between Susceptibility and Magnetic Contents in Rocks Using Neural Networks. Tsinghua Science and Technology. 15 (3): pp. 281-287.
    Source Title
    Tsinghua Science and Technology
    DOI
    10.1016/S1007-0214(10)70062-6
    ISSN
    10070214
    School
    Department of Applied Geology
    URI
    http://hdl.handle.net/20.500.11937/46086
    Collection
    • Curtin Research Publications
    Abstract

    Correlations between magnetic susceptibility and contents of magnetic minerals in rocks are importantin interpreting magnetic anomalies in geophysical exploration and understanding magnetic behaviorsof rocks in rock magnetism studies. Previous studies were focused on describing such correlations using a sole expression or a set of expressions through statistical analysis. In this paper, we use neural network techniques to approximate the nonlinear relations between susceptibility and magnetite and/or hematite contents in rocks. This is the first time that neural networks are used for such study in rock magnetism and magnetic petrophysics. Three multilayer perceptrons are trained for producing the best possible estimation on susceptibility based on magnetic contents. These trained models are capable of producing accurate mappings between susceptibility and magnetite and/or hematite contents in rocks. This approach opens a new way of quantitative simulation using neural networks in rock magnetism and petrophysical research and applications.

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