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

Authors:
William W. Guo;Michael M. Li;Gregory Whymark;Zheng-Xiang Li
Affiliations:
Faculty of Business and Informatics, Central Queensland University, Rockhampton, QLD 4702, Australia;Faculty of Business and Informatics, Central Queensland University, Rockhampton, QLD 4702, Australia;Faculty of Business and Informatics, Central Queensland University, Rockhampton, QLD 4702, Australia;Department of Applied Geology, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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. 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.