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    Mutual complement between statistical and neural network approaches for rock magnetism data analysis

    Access Status
    Fulltext not available
    Authors
    Guo, W.
    Li, M.
    Whymark, G.
    Li, Zheng-Xiang
    Date
    2009
    Type
    Journal Article
    
    Metadata
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    Citation
    Guo, William and Li, Michael and Whymark, Gregory and Li, Zheng-Xiang. 2009. Mutual complement between statistical and neural network approaches for rock magnetism data analysis. Expert Systems with Applications. 36 (6): pp. 9678-9682.
    Source Title
    Expert Systems with Applications
    DOI
    10.1016/j.eswa.2008.11.045
    ISSN
    09574174
    Faculty
    Department of Applied Geology
    Faculty of Science and Engineering
    WA School of Mines
    Remarks

    The link to the journal’s home page is: http://www.elsevier.com/wps/find/journaldescription.cws_home/939/description#description. Copyright © 2009 Elsevier B.V. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/9733
    Collection
    • Curtin Research Publications
    Abstract

    Interpretation of magnetic phenomena in rock magnetism requires a good understanding in relationship between magnetic susceptibility and magnetic minerals, particularly magnetite, contained in rocks. Previous studies emphasized on describing such a correlation using a sole expression through statistical analysis. The resultant correlations are generally useful only in qualitative interpretation, but too coarse to simulate quantitative solutions. In this paper, we combine the correlation analysis with neural network techniques to not only identify the correlations between susceptibility and magnetite in rocks but also simulate accurate susceptibilities with respect to the magnetite contents provided. Our study has demonstrated that multilayer perceptron models are capable of producing accurate mappings between susceptibility and magnetite in rocks. However, correlation analysis provides qualitative interpretation for rock magnetism data in identifying the patterns of magnetic behaviours of the rocks. Inquantitative simulation, if the required accuracy is not restricted, a general MLP model with existenceof noises in training data is the first choice because it does not require statistical data pre-processingfor establishing the NN model. If the simulation is to provide solutions as accurate as possible, theMLP model must be trained by noise-filtered datasets. The noise filtering is based on the preliminary correlation analysis. Therefore, these two approaches are mutually complementary, rather than competitive to each other.

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