Recent advances in aerial gamma-ray surveying

Aerial gamma-ray surveying uses NaI(Tl) detectors mounted in small aircraft to measure gamma radiation, emitted from the earth's surface. The data are collected as gamma-ray spectra, typically with 1 s counting times, from which are derived K, U and Th concentrations in the ground. Applications of aerial surveying include geological mapping for mineral exploration, soil mapping for agriculture, pollution studies and location of lost sources. Recent advances in applying statistical methods to the spectral data have resulted in large reductions in the noise levels in the surveys. Some of the methods available to do this include noise adjusted singular value decomposition (NASVD) [Proceedings of Exploration 97: Fourth Decennial International Conference on Mineral Exploration (1997) 753] and maximum noise fraction (MNF) and enhanced MNF (eMNF) [Explor. Geophys. 31 (2000) 73]. These methods, in general, apply normalization for variance to the spectra, use a principal component method to obtain the 'significant' components of the data and reconstruct cleaned spectra, which are then processed in a standard manner to get radionuclide concentrations. However, they differ in the detail of the application and thus give slightly different results. In this paper, the application of noise reduction methods to various synthetic surveys is used to examine the strengths and weaknesses of the methods. In tests where there are high correlations between U and Th, the eMNF method performs best although the results are improved by prior clustering of the data by the Th/U ratio. If the data show no correlations, then the effectiveness of all the noise removal methods is reduced. If a data set is small (<1500 spectra), then MNF appears to be the better method. Consideration of the various tests suggests an optimum process whereby spectra are sorted into groups by the Th/U ratio of areas identified in a standard processing and then cleaned by eMNF or MNF, depending on the number of spectra in each group