Multichannel models for the estimation of radon background in airborne gamma-ray spectrometry

Abstract

Abstract Adequate background correction is a crucial step in processing airborne gamma-ray spectrometric data because any errors are amplified during subsequent processing procedures. Two multichannel models for the estimation of atmospheric radon background are proposed. The spectral-ratio method uses the relative heights of uranium (U) series photopeaks to estimate the contribution of atmospheric radon to observed spectra. The full-spectrum method estimates the atmospheric radon contribution through the weighted least-squares fitting of potassium (K), U, thorium (Th), and radon component spectra to the observed spectra. Both the spectral-ratio and full-spectrum methods are adequately calibrated through the estimation of component spectra from calibration experiments on the ground using radioactive calibration sources and wood to simulate the attenuation of gamma rays by air. The simulated heights used in these calibrations must be mapped onto real heights through calibration flights over an airborne calibration range. The spectral-ratio method is also adequately calibrated using a heuristic calibration procedure. An iterative minimization method is used to find the optimum values of the calibration constants such that the radon background over suitable calibration lines is best removed.