基于非负矩阵分解的空中Gamma-Ray背景建模
Modeling Aerial Gamma-Ray Backgrounds using Non-negative Matrix Factorization
摘要：
航空伽马射线勘测对许多应用都很有用，从地质学和采矿到公共卫生和核安全。在所有这些情况下，将测量光谱分解成背景源项的线性组合的能力可以提供对数据的有用见解，并导致对使用光谱能量窗口的技术的改进。存在用于光谱的线性分解的多种方法，但受到各种缺点的影响，例如允许负光子通量或需要详细的蒙特卡罗建模。我们建议使用非负矩阵分解（NMF）作为数据驱动的方法来进行谱分解。使用空中调查，包括飞行在水上，我们证明了NMF的数学方法发现物理相关的结构在航空伽马射线背景下，即测量的光谱可以表示为附近的地面发射，遥远的地面发射，氡和宇宙发射的总和。将这些NMF背景分量与使用噪声调整奇异值分解（NASVD）获得的背景分量进行比较，NASVD包含负光子通量，因此不能以简单的方式表示发射光谱。最后，我们评论潜在的研究领域，使NMF分解，如新的方法，光谱异常检测和数据融合。
英文摘要：
Airborne gamma-ray surveys are useful for many applications, ranging from geology and mining to public health and nuclear security. In all these contexts, the ability to decompose a measured spectrum into a linear combination of background source terms can provide useful insights into the data and lead to improvements over techniques that use spectral energy windows. Multiple methods for the linear decomposition of spectra exist but are subject to various drawbacks, such as allowing negative photon fluxes or requiring detailed Monte Carlo modeling. We propose using Non-negative Matrix Factorization (NMF) as a data-driven approach to spectral decomposition. Using aerial surveys that include flights over water, we demonstrate that the mathematical approach of NMF finds physically relevant structure in aerial gamma-ray background, namely that measured spectra can be expressed as the sum of nearby terrestrial emission, distant terrestrial emission, and radon and cosmic emission. These NMF background components are compared to the background components obtained using Noise-Adjusted Singular Value Decomposition (NASVD), which contain negative photon fluxes and thus do not represent emission spectra in as straightforward a way. Finally, we comment on potential areas of research that are enabled by NMF decompositions, such as new approaches to spectral anomaly detection and data fusion.
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