Synthetic survey data have been used to evaluate five aerial gamma-ray survey noise-reduction methods, four of them in industry use (NASVD, MNF, eMNF and Mathis adaptive filtering), as well as a new method, CSM. The CSM (short for CSIRO Self Organizing Map) method is based on deriving a self-organizing map from the full spectral data set and replacing individual spectra by others that best match them in a vector distance sense. Synthetic surveys of 6400 spectra each were prepared to represent: Case A: uniform ground, Case B: ground with varying K?U?Th but fixed Th/U ratio, Case C: ground as for B but with an additional area of different Th/U ratio and, Case D: ground with uncorrelated K?U?Th concentrations. For Cases A and B, the CSM and eMNF methods gave similar noise cleaning and best revealed the known Th?U correlation in Case B. For Case C, both MNF and eMNF showed ghosting in the Th/U ratio in some areas. NASVD did not return the correct Th/U ratio in the area of different Th/U ratio. CSM showed only a small area of ghosting and gave the overall best cleaning. Pre-zoning the data by Th/U ratio and separate processing of the zones is shown to enable the MNF and eMNF methods to avoid ghosting. For Case D, CSM performed poorly for individual radioelements, whilst the other methods gave some cleaning. However, for the Th/U image, the CSM method gave the best cleaning. This case illustrates the importance of some correlations in the data, for any of the spectral noise cleaning methods to give good results.
- Gamma-ray survey
- Noise reduction