【摘要】 为进一步提高环境样品中低活度放射性核素定量分析的准确性，并降低分析方法对标准谱等预设模型的依赖，提出了一种基于模糊C均值高斯混合聚类算法（fuzzy C-means Gaussian mixture model, FCGMM）的γ能谱定量分析方法。采用FCGMM算法对HPGe γ能谱仪（GMX40P）测量的3组土壤样品中的²²⁶Ra、²³²Th、⁴⁰K及5组水样品中的²³⁸U、²³⁵U、²³²Th进行分析，结果表明：FCGMM算法分析结果的相对误差为0.89%～7.20%,平均相对误差为4.28%;与高斯拟合法（相对误差1.73%～14.99%,平均相对误差8.59%）和一阶导数法（相对误差2.67%～16.29%,平均相对误差9.81%）两种通用方法相比，FCGMM算法的相对误差分别降低1.87%～144.15%和9.81%～173.61%,平均相对误差分别降低50.17%和56.37%;对于活度低于40 Bq的核素，FCGMM算法展现出更高的准确性。该方法能够在无需预设模型条件下，快速、准确地实现低活度放射性核素伽马能谱定量分析，为环境样品γ能谱定量分析提供新的参考。更多还原

【Abstract】 To further enhance accuracy and reduce reliance on standard spectra and preset models, a novel γ spectrum quantitative method based on fuzzy C-means Gaussian mixture model（FCGMM） was proposed for low-activity environmental samples. The FCGMM algorithm was utilized to analyze the activities of ²²⁶Ra, ²³²Th and ⁴⁰K in three soil reference samples and ²³⁸U, ²³⁵U and ²³²Th in five water reference samples, respectively, as measured by the HPGe γ spectrometer（GMX40P）. To compare against the Gaussian fitting method（1.73%～14.99%, 8.59% on average） and the first derivative method（2.67%～16.29%, 9.81% on average） by calculating the relative errors, the FCGMM algorithm reduced relative errors by 1.87%～144.15% and 9.81%～173.61%, as well as average relative errors by 50.17% and 56.37%, respectively. For activities below 40 Bq, the FCGMM algorithm demonstrated significantly higher accuracy. The results indicate that the presented method can serve as a reference in low-activity γ spectrum analysis, providing rapid and accurate performance without the need of preset models.更多还原