Approximating Nonlinear Relations Between Susceptibility and Magnetic Contents in Rocks Using Neural Networks |
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Authors: | William W Guo Michael Li Zhengxiang Li Greg Whymark |
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Institution: | 1. Faculty of Arts, Business, Informatics and Education, Central Queensland University, Rockhampton QLD 4701, Australia;2. The Institute for Geoscience Research (TIGeR), Curtin University of Technology, Perth WA 6845, Australia |
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Abstract: | 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 estimation on 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. |
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