A Methodology for Determining the Concentration of Naturally Occurring Radioactive Materials in an Urban Environment

Swinney, Mathew W.; Peplow, Douglas E.; Patton, Bradley D; Nicholson, Andrew; Archer, D. E.; Willis, M.J.

doi:10.1080/00295450.2018.1458558

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…Background contribution is inevitably present in a radiation measurement given that radiation emission from naturally occurring radioactive materials (NORM) is omnipresent. Though background radiation may be defined as the radiation that emerged from NORM, in a nuclear security framework background may be considered the contribution that comes from radioactive sources that are not identified as a threat [36]. For instance, medical radioisotopes, which are used for treatment purposes, may be considered as part of the background radiation in a hospital unit.…”

Section: Background Radiation Estimationmentioning

confidence: 99%

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Alamaniotis

Heifetz

2021

Learning and Analytics in Intelligent Systems

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The increasing concerns over the use of nuclear materials for malevolent purposes (i.e., terrorist attacks) have fueled the interest in developing technologies that can detect hidden nuclear material before its use. The process of detecting and identifying nuclear materials for non-reported purposes is under the umbrella of nuclear security. Among several areas that contribute to the security and safeguards of nuclear materials, radiation data analytics has recently been marked as an area of high potential. Therefore, there is an increasing trend in applying machine learning for developing data analysis methods applied to radiation signals aiming at identifying patterns of interest associated with nuclear materials. This chapter aspires in providing a comprehensive survey and discussion of machine learning and data analytics methods pertained to nuclear security. The chapter will also discuss further trends and how data analytics can further enhance nuclear security by effectively analyzing radiation data.

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Section: Background Radiation Estimationmentioning

confidence: 99%

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Alamaniotis

Heifetz

2021

Learning and Analytics in Intelligent Systems

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“…(5.24 cm) thick. Swinney [10] used the measurements and detailed simulations of single-energy gammas distributed in layers to compute the concentrations (Bq/kg) of 40 K, 238 U/ 235 U and their daughters, and 232 Th and its daughters in 11 different materials. The concentration of 137 Cs was also found for soil.…”

Section: Measurements For Background K U and Th Concentrationsmentioning

confidence: 99%

“…The concentrations for the NORM materials used in computing the background/skyshine component were taken from a MUSE paper by Swinney et al [10]. These values are the averages of different measurements for the given material at the FTIG site.…”

Section: Other Modeling Topicsmentioning

confidence: 99%

Monte Carlo Simulation of Background and Source Measurements with CSG and CAD Geometries

Peplow

Davidson

Celik

et al. 2021

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“…V. FEATURES FROM PANORAMIC IMAGERY In [31], the panoramic images were labeled according to known visual classes at the FtIG MOUT facility, such as This class selection was motivated by the small variety of visual classes at the facility as well as the extensive ground truth measurements of those materials, which revealed relative uniformity within several of the visual classes [32]. For this dataset, since no ground truth exists but also because there are many possible visual classes, we decided to use other tools to label the imagery.…”

Section: Extracting Spectral Features Using the Nmf Modelsmentioning

confidence: 99%

“…This work is a part of the Modeling Urban Scenarios and Experiments (MUSE) collaboration [28], [29], where the RadMAP vehicle [30] was used to explore the connections between gamma-ray backgrounds and various contextual sensors. Previous work using RadMAP within MUSE consisted of analysis of panoramic imagery at a small mock urban area (the Military Operations in Urban Terrain or MOUT facility in Fort Indiantown Gap, Pennsylvania, or FtIG) [31], for which numerous gamma-ray ground truth measurements had been made [32]. This work investigates applying methods developed for analyzing data collected at the MOUT facility to data from a dense urban area for which no ground truth data exists but which offers realistic complexity and covers a much larger survey area.…”

mentioning

confidence: 99%

Correlations between Panoramic Imagery and Gamma-Ray Background in an Urban Area

Bandstra

Quiter

Bilton

et al. 2019

2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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When searching for radiological sources in an urban area, a vehicle-borne detector system will often measure complex, varying backgrounds primarily from natural gamma-ray sources. Much work has been focused on developing spectral algorithms that retain sensitivity and minimize the false positive rate even in the presence of such spectral and temporal variability. However, information about the environment surrounding the detector system might also provide useful clues about the expected background, which if incorporated into an algorithm, could improve performance. Recent work has focused on extensive measuring and modeling of urban areas with the goal of understanding how these complex backgrounds arise. This work presents an analysis of panoramic video images and gammaray background data collected in Oakland, California by the Radiological Multi-sensor Analysis Platform (RadMAP) vehicle. Features were extracted from the panoramic images by semantically labeling the images and then convolving the labeled regions with the detector response. A linear model was used to relate the image-derived features to gamma-ray spectral features obtained using Non-negative Matrix Factorization (NMF) under different regularizations. We find some gamma-ray background features correlate strongly with image-derived features that measure the response-adjusted solid angle subtended by sky and buildings, and we discuss the implications for the development of future, contextually-aware detection algorithms. I. INTRODUCTIONV EHICLE-borne gamma-ray detection systems have been developed and deployed for many years and play a key role in radiological and nuclear security missions, especially the search for sources outside of regulatory control [1], [2], [3], [4], [5], [6], [7]. These systems can be rapidly deployed and carry large volume detectors, giving them advantages in efficiency and the ability to cover wide areas relative to humanportable systems. However, with their higher efficiencies, these systems also suffer from the complex natural radiological backgrounds often found in urban areas [8], [9], whose spatial and temporal complexities can be exacerbated by the added

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A Methodology for Determining the Concentration of Naturally Occurring Radioactive Materials in an Urban Environment

Cited by 12 publications

References 15 publications

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Monte Carlo Simulation of Background and Source Measurements with CSG and CAD Geometries

Correlations between Panoramic Imagery and Gamma-Ray Background in an Urban Area

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