Approximating nonlinear relations between susceptibility and magnetic contents in rocks using neural networks
journal contribution
posted on 2017-12-06, 00:00authored byWanwu Guo, Minmei LiMinmei 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);