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Application of the Inverse Monte Carlo Method to Energy-Dispersive X-Ray Fluorescence

Published online by Cambridge University Press:  06 March 2019

A.M. Yacout  and
W.L. Dunn
A.M. Yacout
Affiliation:
Applied Research Associates, Inc. 6404 Falls of Neuse Road, Suite 200 Raleigh, NC 27615
W.L. Dunn
Affiliation:
Applied Research Associates, Inc. 6404 Falls of Neuse Road, Suite 200 Raleigh, NC 27615
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Abstract

The feasibility of utilizing the Inverse Monte Carlo (IMC) method to determine elemental amounts in homogeneous samples from energy-dispersive X-ray fluorescence (EDXRF) measurements has been investigated. IMC is a novel application of standard Monte Carlo that, in principle, allows a rather large class of inverse problems to be solved noniteratively, in the sense that the simulation is not repeated. IMC is implemented by hypothesizing values for the unknown parameters, executing a direct Monte Carlo simulation using these values and scoring the results with factors that contain the unknown parameters. By equating the resulting Monte Carlo estimators to the known measured responses, a system of algebraic equations is formed that may be soluble by standard numerical techniques. Advantages of IMC include the facts that it can be applied to complex (e.g., multidimensional) problems and that it is relatively efficient since the simulation is performed only once. In the EDXRF case, simplified assumptions have been used to construct an approximate IMC solution. The model provides a means to determine the elemental amounts in an unknown sample by treating the unknown composition as a perturbation around the composition of a fixed "reference" sample that contains the same elements as the unknown sample. Primary and secondary X-rays are included in the model. The resulting system of algebraic equations is nonlinear and solutions are obtained via an approximation. Since the model is based on correlated sampling techniques, several unknown samples can be modeled simultaneously provided that they contain the same elements and their compositions are sufficiently close to that of the assumed reference sample. Direct Monte Carlo was used to generate the relative intensities for eighteen different samples in a ternary (Ni-Fe-Cr) system; these results were then used in the IMC model to recover the sample weight fractions. Excellent results were obtained, demonstrating the feasibility of the approach.

Type
III. XRF Fundamental Parameters and Data Analysis
Information
Advances in X-Ray Analysis , Volume 30: Thirty-Fifth Annual Conference on Applications of X-ray Analysis, August 4-8, 1986 , 1986 , pp. 113 - 120
Copyright
Copyright © International Centre for Diffraction Data 1986

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References

1. Dunn, W.L., Inverse Monte Carlo Analysis, J. Comput. Phys. 41:154, 1981.Google Scholar
2. Dunn, W.L., Inverse Monte Carlo Solutions for Radiative Transfer in Inhomogeneous Media, 0. Quant. Spectrosc. Radi at. Transfer 29(1):19, 1983.Google Scholar
3. Dunn, W.L., Boffi, V.C. and O'Foghludha, F., Applications of the Inverse Monte Carlo Method in Photon Beam Physics, Nuc. Instr. Meth. Phys. Res. (in press).Google Scholar
4. Floyd, C.E., Jaszczak, R.J. and Coleman, R.E., Inverse Monte Carlo: A Unified Reconstruction Algo rithm for SPECT. IEEE. rans. Nuc. Sci. NS-32(1):779, 1985.Google Scholar
5. Sherman, O., The Theoretical Derivation of Fluorescent X-ray Intensities from Mixtures, Spectrochim. Acta. 7:283, 1955.Google Scholar
6. Gardner, R.P. and Hawthorne, A.R., Monte Carlo Simulation of X-Ray Fluorescence from Homogeneous Samples Including Tertiary Interelement Tffects, X-Ray Spectrometry 4:138, 1975.Google Scholar
7. Hawthorne, A.R. and Gardner, R.P., Monte Carlo Simulation of X-Ray Fluorescence from Homogeneous Multielement Samples Excited by Continuous and Discrete Energy Photons from X-Ray Tubes, Anal. Chem. 47:2220, 1975.Google Scholar
8. Doster, J.M. and Gardner, R.P., The Complete Spectral Response for EDXRF. Systems - Calculation by Monte, Carlo and Analysis Applications: 1. Homogeneous Samples, X-Ray Spectrometry 11(4):173, 1982.Google Scholar
9. Yacout, A.M., Gardner, R.P. and Verghese, K., Monte Carlo Simulation of the X-Ray Fluorescence Spectra from Multielement Homogeneous and Heterogeneous Samples, Abstract Book, 35th Annual Denver Conference on Applications of X-Ray Analysis, Denver, CO. August 4-8, 1986.Google Scholar