Journal article
Automated detection of radioisotopes from an aircraft platform by pattern recognition analysis of gamma-ray spectra
Journal of environmental radioactivity, Vol.192, pp.654-666
12/2018
DOI: 10.1016/j.jenvrad.2018.02.012
PMCID: PMC7331277
PMID: 29526495
Abstract
A generalized methodology was developed for automating the detection of radioisotopes from gamma-ray spectra collected from an aircraft platform using sodium-iodide detectors. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, multivariate classification models based on nonparametric linear discriminant analysis were developed for application to spectra that were preprocessed through a combination of altitude-based scaling and digital filtering. Training sets of spectra for use in building classification models were assembled from a combination of background spectra collected in the field and synthesized spectra obtained by superimposing laboratory-collected spectra of target radioisotopes onto field backgrounds. This approach eliminated the need for field experimentation with radioactive sources for use in building classification models. Through a bi-Gaussian modeling procedure, the discriminant scores that served as the outputs from the classification models were related to associated confidence levels. This provided an easily interpreted result regarding the presence or absence of the signature of a specific radioisotope in each collected spectrum. Through the use of this approach, classifiers were built for cesium-137 (137Cs) and cobalt-60 (60Co), two radioisotopes that are of interest in airborne radiological monitoring applications. The optimized classifiers were tested with field data collected from a set of six geographically diverse sites, three of which contained either 137Cs, 60Co, or both. When the optimized classification models were applied, the overall percentages of correct classifications for spectra collected at these sites were 99.9 and 97.9% for the 60Co and 137Cs classifiers, respectively.
•Automated detection of 137Cs and 60Co by airborne gamma-ray spectroscopy.•Supervised pattern recognition of digitally filtered gamma-ray spectra.•Methods development does not require field data of radioactive sources.•Detection decisions supplied with % confidence for ease of interpretation.•Methodology demonstrated with aerial surveys of six field sites.
Details
- Title: Subtitle
- Automated detection of radioisotopes from an aircraft platform by pattern recognition analysis of gamma-ray spectra
- Creators
- Brian W Dess - Department of Chemistry & Optical Science and Technology Center, University of Iowa, Iowa City, IA 52242, USAJohn Cardarelli - CBRN Consequence Management Advisory Division, Environmental Protection Agency, 4900 Olympic Blvd., Erlanger, KY 41018, USAMark J Thomas - CBRN Consequence Management Advisory Division, EPA Office of Emergency Management, 300 Minnesota Ave, Kansas City, KS 66101, USAJeff Stapleton - Kalman and Co., Inc., 5366 Virginia Beach Blvd., Ste. 303, Virginia Beach, VA 23462, USARobert T Kroutil - Kalman and Co., Inc., 5366 Virginia Beach Blvd., Ste. 303, Virginia Beach, VA 23462, USADavid Miller - Kalman and Co., Inc., 5366 Virginia Beach Blvd., Ste. 303, Virginia Beach, VA 23462, USATimothy Curry - CBRN Consequence Management Advisory Division, EPA Office of Emergency Management, 300 Minnesota Ave, Kansas City, KS 66101, USAGary W Small - Department of Chemistry & Optical Science and Technology Center, University of Iowa, Iowa City, IA 52242, USA
- Resource Type
- Journal article
- Publication Details
- Journal of environmental radioactivity, Vol.192, pp.654-666
- DOI
- 10.1016/j.jenvrad.2018.02.012
- PMID
- 29526495
- PMCID
- PMC7331277
- NLM abbreviation
- J Environ Radioact
- ISSN
- 0265-931X
- eISSN
- 1879-1700
- Publisher
- Elsevier Ltd
- Grant note
- DOI: 10.13039/501100001589, name: Environmental Protection Agency
- Language
- English
- Date published
- 12/2018
- Academic Unit
- Chemistry
- Record Identifier
- 9984216607402771
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