@article{Guo2010, 
author = {William W Guo and Michael Li and Zhengxiang Li and Greg Whymark},
title = {Approximating Nonlinear Relations Between Susceptibility and Magnetic Contents in Rocks Using Neural Networks},
year = {2010},
journal = {Tsinghua Science and Technology},
volume = {15},
number = {3},
pages = {281-287},
keywords = {neural networks, nonlinear function approximation, rock magnetism, magnetic susceptibility, magnetic contents},
url = {https://www.sciopen.com/article/10.1016/S1007-0214(10)70062-6},
doi = {10.1016/S1007-0214(10)70062-6},
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.}
}