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Quantitative phase analysis of clay minerals by X-ray powder diffraction using artificial neural networks. I. Feasibility study with calculated powder patterns

Published online by Cambridge University Press:  09 July 2018

D. T. Griffen
D. T. Griffen*
Affiliation:
Department of Geology, Brigham Young University, Provo, Utah 84602, USA
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Abstract

An artificial neural network with back-propagation architecture has been applied to the problem of the quantitative analysis of simulated clay mixtures from synthetic X-ray diffraction (XRD) data. A ‘clay characterization function’ (CCF) has been devised that combines information from two clay XRD peaks into a single peak that simplifies the problem which the neural network must solve. In addition, it eliminates peaks from non-layered minerals. A neural network with 17 neurons in the hidden layer and log-sigmoid transfer functions in each layer is sufficiently successful at predicting the compositions of binary and ternary mixtures of the three model clays to demonstrate the potential of the method. Moreover, training is accomplished in relatively short times using a Levenberg-Marquardt algorithm.

Although the problem ‘solved’ by this neural network is rather simple, the approach has the potential for the much more complex problem of the quantitative phase analysis of mixtures of real clay minerals. In this study, only the CCF peak heights have been used, but the positions and peak widths also contain important information. It may be practical to include as variables not only the concentrations of clay phases, but also compositional information to which XRD is sensitive.

Type
Research Article
Information
Clay Minerals , Volume 34 , Issue 1 , March 1999 , pp. 117 - 126
Copyright
Copyright © The Mineralogical Society of Great Britain and Ireland 1999

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References

Bischof, H., Schneider, W. & Pinz, A.J. (1992) Multispectral Classification of Landsat Images Using Neural Networks. IEEE Trans. Geosc. Rem. Sens. 30, 482–490.Google Scholar
Bish, D.L. (1993) Studies of clays and clay minerals using X-ray powder diffraction and the Rietveld method. Pp. 79-121 in: Computer Applications to X-ray Powder Diffraction Analysis of Clay Minerals, (Walker, J.R. & Reynolds, R.C. Jr., editors) CMS Workshop Lectures, 5, The Clay Minerals Society, Boulder, CO.Google Scholar
Carling, A. (1992) Introducing Neural Networks. Sigma Press, Cheshire, England.Google Scholar
Carr, J.R. & Hibbard, M.J. (1991) Open-ended mineralogical/ textural rock classification. Comp Geosc. 17, 1409–1463.Google Scholar
Chester, M. (1993) Neural Networks, A Tutorial. Prentice-Hall, Englewood Cliffs, New Jersey.Google Scholar
Demuth, H. & Beale, M. (1992) Neural Network Toolbox for use with MATLAB. The MathWorks, Natick, MA.Google Scholar
De Wilde, P. (1997) Neural Network Models. Second Edition. Springer-Verlag, London.Google Scholar
Griffen, D.T., Griffen, B.T. & Secrest CD. (1995) Toward an artificial neural network for the modal analysis of rocks from X-ray diffraction data. Geol. Soc. Am. Ann. Meet, Abstracts with Prog. 27, A-195.Google Scholar
Huang, Y., Wong, P.M. & Gedeon, T.D. (1996) An Improved Fuzzy Neural Network for Permeability Estimation from Wireline Logs in a Petroleum Reservoir. Proc. IEEE Region Ten Conf. (TENCON) on Digital Signal Proc. Appl., 2, 912–917.Google Scholar
Jones, R.C. (1989) A computer technique for X-ray diffraction curve fitting/peak decomposition. Pp. 52-101 in: Quantitative Mineral Analysis of Clays, (Pevear, P.R. & Mumpton, F.A., editors), CMS Workshop Lectures, 1, The Clay Minerals Society, Boulder, CO.Google Scholar
Pevear, D.R. & Schuette, J.F. (1993) Inverting the NEWMOD© X-ray diffraction forward model for clay minerals using genetic algorithms. Pp. 19-41 in: Computer Applications to X-ray Powder Diffraction Analysis of Clay Minerals, (Walker, J.R. & Reynolds, R.C. Jr., editors) CMS Workshop Lectures, 5, The Clay Minerals Society, Boulder, CO.Google Scholar
Prince, E. (1994) Mathematical Techniques in Crystallography and Materials Science. Springer-Verlag, Berlin.Google Scholar
Reynolds, R.C. (1985) NEWMOD®, a Computer Program for the Calculation of Basal X-ray Diffraction Intensities of Mixed-Layered Clays. 8 Brook Road, Hanover, NH 03755, USA.Google Scholar
Reynolds, R.C. Jr. & Reynolds, R.C. III (1987) Description of program NEWMOD2 for the calculation of the one-dimensional X-ray diffraction patterns of mixed-layered clays. 8 Brook Road, Hanover, NH 03755, USA.Google Scholar
Walker, J.R. (1993) An introduction to computer modeling of X-ray diffraction patterns of clay minerals: A guided tour of NEWMOD®. Pp. 1-17 in: Computer Applications to X-ray Powder Diffraction Analysis of Clay Minerals, (Walker, J.R. & Reynolds, R.C. Jr., editors) CMS Workshop Lectures, 5, The Clay Minerals Society, Boulder, CO.Google Scholar