Abstract：A principal component analysis (PCA) and Mahalanobis distance (MD) based anomaly gamma spectra detection method is developed to improve the reliability of radiation scanning of goods and human bodies. This method first extracts all the principal components (PCs) of large numbers of benign gamma spectra by PCA and selects several largest PCs to form a subspace. The algorithm then projects the benign, unknown gamma spectra to this subspace, calculates their MDs, and completes the anomaly detection by comparing these MDs. Monte Carlo simulations and actual tests show that the method is reliable and effective when the subspace has more than 99% of the original information.
赵日, 刘立业, 李君利. 基于主成分分析和Mahalanobis距离的异常γ能谱识别[J]. 清华大学学报（自然科学版）, 2017, 57(8): 826-831.
ZHAO Ri, LIU Liye, LI Junli. Anomaly gamma spectra detection based on principal component analysis and the Mahalanobis distance. Journal of Tsinghua University(Science and Technology), 2017, 57(8): 826-831.
Boardman D, Reinhard M, Flynn A. Principal component analysis of gamma-ray spectra for radiation portal monitors[J]. IEEE Transactions on Nuclear Science, 2012, 59(1):154-161.
[4]
Runkle R, Tardiff F, Anderson K, et al. Analysis of spectroscopic radiation portal monitor data using principal components analysis[J]. IEEE Transactions on Nuclear Science, 2006, 53(3):1418-1425.
[5]
Dragovic S, Onjia A. Classification of soil samples according to their geographic origin using gamma-ray spectrometry and principal component analysis[J]. Journal of Environmental Radioactivity, 2006, 89:150-158.
[6]
Heggemanna T, Welpa G, Amelung W, et al. Proximal gamma-ray spectrometry for site-independent in situ prediction of soil texture on ten heterogeneous fields in Germany using support vector machines[J]. Soil & Tillage Research, 2017, 168:99-109.
[7]
Varley A, Tyler A, Smith L, et al. Mapping the spatial distribution and activity of <sup>226</sup>Ra at legacy sites through machine learning interpretation of gamma-ray spectrometry data[J]. Science of the Total Environment, 2016, 545:654-661.
[8]
Wang C L, Funk L, Riedel R, et al. Improved neutron-gamma discrimination for a <sup>3</sup>He neutron detector using subspace learning method[J]. Nuclear Instruments and Methods in Physics Research A, 2017, 853:27-35.
[9]
Hata H, Yokoyama K, Ishimori Y, et al. Application of support vector machine to rapid classification of uranium waste drums using low-resolution γ-ray spectra[J]. Applied Radiation and Isotopes, 2015, 104:143-146.
[10]
杨杨. 基于模糊聚类的地层岩性测井识别研究与配套软件研制[D]. 成都:西南石油大学, 2014.YANG Yang. Research and Development of Formation Lithology Logging Identification and Matching Software Based on Fuzzy Clustering[D]. Chengdu:Southwest Petroleum University, 2014. (in Chinese)
[11]
周志华. 机器学习[M]. 北京:清华大学出版社, 2016.ZHOU Zhihua. Machine Learning[M]. Beijing:Tsinghua University Press, 2016. (in Chinese)
[12]
Han J, Kamber M, Pei J. Data Mining:Concepts Techniques[M]. Singapore:Elsevier (Singapore) Pte Ltd, 2012.
2017, Vol. 57 
