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dc.contributor.authorGuo, W.
dc.contributor.authorLi, M.
dc.contributor.authorWhymark, G.
dc.contributor.authorLi, Zheng-Xiang
dc.identifier.citationGuo, 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.

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.

dc.subjectNeural networks
dc.subjectMultilayer perceptron
dc.subjectCorrelation analysis
dc.subjectRock magnetism
dc.titleMutual complement between statistical and neural network approaches for rock magnetism data analysis
dc.typeJournal Article
dcterms.source.titleExpert Systems with Applications

The link to the journal’s home page is: Copyright © 2009 Elsevier B.V. All rights reserved

curtin.accessStatusFulltext not available
curtin.facultyDepartment of Applied Geology
curtin.facultyFaculty of Science and Engineering
curtin.facultyWA School of Mines

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