Automated photopeak detection and analysis in low resolution gamma-ray spectra for isotope identification
Automated photopeak detection and analysis in low resolution gamma-ray spectra for isotope identification
复制标题
低分辨率伽马射线光谱中的自动光峰值检测和分析，用于同位素识别
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
J. Lu
中科院分区：
文献类型：
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作者：
C. Sullivan;J. Lu
长期以来，自动同位素识别一直是国土安全和核应急响应中的一个重要问题。这一过程对于低分辨率光谱来说是困难的，因为峰可能会显著重叠。此外，它们的面积可能很难确定，因为整个光谱上的康普顿连续统导致基线波动。小波变换因其具有去噪、模式匹配和同时多分辨率信号分析等能力，在解决这一问题的众多潜在解决方案中脱颖而出。本文介绍了一种新的基于小波的峰值检测和面积测量算法，并选择特定的小波来寻找信号分析的最佳尺度。用模拟信号和真实伽马能谱对它们的定位、分辨重叠峰和确定峰面积的能力进行了评价。
Automated isotope identification has long been an important problem in homeland security and nuclear emergency response. This process is difficult for low-resolution spectra because peaks can be significantly overlapping. Also, their areas can be difficult to determine because of the fluctuating baseline due to the Compton continuum across the whole spectrum. The wavelet transform stands out among many potential solutions of this problem, owing to its ability to de-noise noisy signals, pattern matching, and simultaneous multi-resolution signal analysis. In this paper, a novel wavelet-based algorithm for detecting peaks and measuring their areas is introduced, and specific wavelets are selected to find the optimal scale of signal analysis. Their abilities in locating peaks, resolving overlapping peaks, and determining peak areas are presented and assessed with both simulated signals and real gamma-ray spectra.