光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2015年
8期
2320-2323
,共4页
黄洪全%丁卫撑%龚迪琛%方方
黃洪全%丁衛撐%龔迪琛%方方
황홍전%정위탱%공적침%방방
GMSM模型%统计遗传%重叠峰分解
GMSM模型%統計遺傳%重疊峰分解
GMSM모형%통계유전%중첩봉분해
GMSM model%Statistical and Genetic%Decomposition of overlapping peaks
针对X射线荧光分析中相邻谱峰之间的重叠问题,结合光谱形成过程的随机物理特性,提出了一种基于高斯混合统计模型(Gaussian mixture statistics model ,GMSM )和遗传算法的重叠峰分解方法。首先,提出了重叠峰的GMSM描述方法,并分析了期望最大化法(expectation maximization ,EM )的局部收敛问题;接着,将GMSM 参数看作个体基因,以重叠峰随机数据序列的对数似然函数作为适应度函数,并给出了目标函数值的快速算法;然后,采用遗传算法的群体搜索技术找出全局最优解,实现重叠峰分解。该方法将所有测量的随机数据都当作“有用”来处理,其“有用”程度由其概率大小来体现,实现了原谱数据的“零损失”,搜索到的GMSM是全局最大概率意义下的“最佳匹配”模型,符合放射性测量过程的随机性。通过对四个严重重叠峰分解的实验表明,分解后的峰位、峰面积及标准偏差具有较高精度,最大误差分别为0.7道,2.3%,2.17%,特别适合于严重重叠的情况,并可广泛用于其他能谱重叠峰的分解。
針對X射線熒光分析中相鄰譜峰之間的重疊問題,結閤光譜形成過程的隨機物理特性,提齣瞭一種基于高斯混閤統計模型(Gaussian mixture statistics model ,GMSM )和遺傳算法的重疊峰分解方法。首先,提齣瞭重疊峰的GMSM描述方法,併分析瞭期望最大化法(expectation maximization ,EM )的跼部收斂問題;接著,將GMSM 參數看作箇體基因,以重疊峰隨機數據序列的對數似然函數作為適應度函數,併給齣瞭目標函數值的快速算法;然後,採用遺傳算法的群體搜索技術找齣全跼最優解,實現重疊峰分解。該方法將所有測量的隨機數據都噹作“有用”來處理,其“有用”程度由其概率大小來體現,實現瞭原譜數據的“零損失”,搜索到的GMSM是全跼最大概率意義下的“最佳匹配”模型,符閤放射性測量過程的隨機性。通過對四箇嚴重重疊峰分解的實驗錶明,分解後的峰位、峰麵積及標準偏差具有較高精度,最大誤差分彆為0.7道,2.3%,2.17%,特彆適閤于嚴重重疊的情況,併可廣汎用于其他能譜重疊峰的分解。
침대X사선형광분석중상린보봉지간적중첩문제,결합광보형성과정적수궤물리특성,제출료일충기우고사혼합통계모형(Gaussian mixture statistics model ,GMSM )화유전산법적중첩봉분해방법。수선,제출료중첩봉적GMSM묘술방법,병분석료기망최대화법(expectation maximization ,EM )적국부수렴문제;접착,장GMSM 삼수간작개체기인,이중첩봉수궤수거서렬적대수사연함수작위괄응도함수,병급출료목표함수치적쾌속산법;연후,채용유전산법적군체수색기술조출전국최우해,실현중첩봉분해。해방법장소유측량적수궤수거도당작“유용”래처리,기“유용”정도유기개솔대소래체현,실현료원보수거적“령손실”,수색도적GMSM시전국최대개솔의의하적“최가필배”모형,부합방사성측량과정적수궤성。통과대사개엄중중첩봉분해적실험표명,분해후적봉위、봉면적급표준편차구유교고정도,최대오차분별위0.7도,2.3%,2.17%,특별괄합우엄중중첩적정황,병가엄범용우기타능보중첩봉적분해。
In fluorescence analysis ,the phenomenon of overlapping often occurs among adjacent peaks .In the view of the random physical properties of formation process of X fluorescence spectra ,Gaussian Mixture Statistics Model (GMSM ) and Genetic Al‐gorithms were used for the decomposition of overlapping peaks .First ,the GMSM was proposed to describe the overlapping peaks ,and the local convergence problem of expectation maximization (EM ) was analyzed .Secondly ,the GMSM parameters were regarded as individual genes ,and the log‐likelihood function of overlapping peaks random data was set as fitness function . A fast algorithm for the objective function value was proposed .Finally ,the population search technology of Genetic Algorithm was used to find the global optimal solution ,and to realize the decomposition of overlapping peaks .All measured data were re‐garded as “useful” data .The “useful” degree was reflected by their probability .The GMSM method can achieve the “best match” effect in the maximum global probability with zero loss of original data ,which can fit the random of radiation measure‐ment process .The decomposition experiments of four serious overlapping peaks show high precision of the peak position ,peak area and standard deviation .The maximum error was 0.7 channel ,2.3% and 2.17% ,respectively ,which is especially suitable for the condition of serious overlap and can be widely used for the decomposition of other energy spectrum .