Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization

• Published in: IEEE transactions on nuclear science Vol. 67; no. 5; pp. 777 - 790
• Main Authors: Bandstra, M. S., Joshi, T. H. Y., Bilton, K. J., Zoglauer, A., Quiter, B. J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerial surveys; Airborne gamma-ray spectroscopy; Anomalies; Atmospheric modeling; atmospheric radon; Background noise; Computer simulation; cosmic radiation; Data integration; Decomposition; Detectors; Emission spectra; Emissions; Factorization; Fluxes; gamma ray background; Gamma rays; Geological surveys; Geology; Mathematical analysis; Mathematical model; Matrix decomposition; Matrix methods; non-negative matrix factorization (NMF); NUCLEAR PHYSICS AND RADIATION PHYSICS; Photonics; Photons; Pollution measurement; Public health; Radon; Singular value decomposition
• ISSN: 0018-9499; 1558-1578
• DOI: 10.1109/TNS.2020.2978798

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 in the 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 the 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 with the background components obtained by noise-adjusted singular value decomposition (NASVD), which contain negative photon fluxes and, thus, do not represent the emission spectra in as straightforward a way. Finally, we comment on the potential areas of research that are enabled by NMF decompositions, such as new approaches to spectral anomaly detection and data fusion.