Application of an Improved Seeds Local Averaging Algorithm in X-ray Spectrum

Abstract

As an element content analysis technology, X-ray fluorescence spectrometry can be used for quantitative or semiquantitative analysis of the element content in the sample, which is of great significance for mineral census and spent fuel reprocessing. Due to the limitation of the inherent energy resolution of the detector itself, the accuracy of X-ray fluorescence analysis is difficult to be greatly improved. In some applications, even if the semiconductor detector with the best energy resolution is used, the characteristic peaks of different elements cannot be completely separated. Therefore, greatly improving the energy resolution of the detection system is a hot issue in the existing research field. To solve these problems, this paper analyzes the advantages and disadvantages of the traditional MCA (multichannel analyzer) and SLA (seeds local averaging) algorithm and proposes an ISLA (improved seeds local averaging) algorithm based on mathematical statistics. In the section of theoretical derivation, the principle of ISLA algorithm is described, whose theoretical characteristics and spectral results with different parameters are derived and simulated. In the application effect evaluation, the spectrum obtained by each method is analyzed in detail. Simulation and experimental results show that the spectrum obtained by SLA algorithm has a smaller full width at half maximum than that obtained by MCA, but the seed average process in SLA algorithm also reduces its counting rate. The optimized ISLA algorithm can not only effectively reduce the full width at half maximum of the spectral line and sharpen the spectrum peak but also compensate for the loss of the count rate of SLA algorithm.