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Approximating nonlinear relations between susceptibility and magnetic contents in rocks using neural networks

journal contribution
posted on 06.12.2017, 00:00 by Wanwu Guo, Minmei Li, Gregory Whymark, ZX Li
Correlations between magnetic susceptibility and contents of magnetic minerals in rocks are important in interpreting magnetic anomalies in geophysical exploration and understanding magnetic behaviors of 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 estimationon 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.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

15

Issue

3

Start Page

281

End Page

287

Number of Pages

7

ISSN

1007-0214

Location

Amsterdam, Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

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

Era Eligible

Yes

Journal

Tsinghua science and technology.

Exports