Background correction method for improving the automated detection of radioisotopes from airborne gamma-ray surveys

Abstract

An altitude-based background correction strategy was developed for use in the application of pattern recognition methods to the classification of gamma-ray spectra collected during airborne surveys. Application of this methodology helped to suppress the background spectral variation that serves to obscure the photopeaks associated with low levels of gamma-ray emission. The correction method was implemented by optimizing a database of background gamma-ray spectra collected at various locations and altitudes. Given this background database, a field-collected spectrum was corrected by performing linear regression onto a background spectrum from the database at a matching altitude. The residuals about the regression fit were then digitally filtered and submitted to nonparametric linear discriminant analysis for the purpose of computing classification models for targeted radioisotopes. The resulting classifiers were applied to predict the presence or absence of specific radioisotope signatures in data acquired during airborne surveys. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, classification models were computed to detect the presence of cesium-137 (137Cs) and cobalt-60 (60Co). The optimized classifiers were tested over 12 diverse locations, with nine of these data sets containing the target radioisotopes. Correct classification percentages of 99.4% and 99.8% were obtained for the 137Cs and 60Co classifiers, respectively, on the basis of comparisons to visual inspections of the corresponding spectra.