Gamma-ray anomaly detector for airborne surveys based on a machine learning methodology

Abstract

Machine learning methods were used to develop an automated classification model for detecting gamma-ray spectra with anomalous (i.e., non-background) signatures collected during airborne surveys. Spectra were preprocessed with an altitude-based normalization procedure, followed by application of a digital filter to remove baseline and noise effects. Segments of the preprocessed spectra were then used as input patterns to piecewise linear discriminant analysis (PLDA). For use in building the classification model, a training set was constructed based on two data classes: (1) spectra containing various radioisotope signatures and (2) background spectra. Through the use of a piecewise linear discriminant based on seven separating boundaries, a general-purpose spectral anomaly detector was developed for use in the automated screening of large spectral datasets collected during airborne surveys. The intended application of the methodology is to aid first responders in locating lost or stolen radioactive sources or in managing other incidents in which radioactive material is released into the environment. In developing and testing the methodology, laboratory spectra, spectra collected during airborne surveys, and mathematically synthesized spectra were used. When applied to 17 airborne surveys in which known radioisotope sources were present, the anomaly detector was able to locate each source with high confidence. While false detections were observed at a rate of 3.7%, many of these were in the vicinity of the known source location but the radioisotope signature could not be visually observed in the spectrum. For false detections away from the source locations, elevated signatures from naturally occurring background components were typically observed.