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

journal contribution
posted on 06.12.2017, 00:00 authored by Wanwu GuoWanwu Guo, Minmei LiMinmei Li, Gregory WhymarkGregory Whymark, ZX Li
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. In quantitative simulation, if the required accuracy is not restricted, a general MLP model with existence of noises in training data is the first choice because it does not require statistical data pre-processing for establishing the NN model. If the simulation is to provide solutions as accurate as possible, the MLP 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.

History

Volume

36

Issue

6

Start Page

9678

End Page

9682

Number of Pages

5

ISSN

0957-4174

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Curtin University of Technology; Faculty of Business and Informatics; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Journal

Expert systems with applications.