Automatic merging of gridded airborne gamma-ray spectrometric surveys

Abstract

Older airborne gamma-ray spectrometric survey data were often presented in units of counts per second. These data values are dependent on survey and equipment parameters such as detector volume, survey height, and the window energy limits used to measure the gamma radiation. Thus, data values on adjacent surveys may not be directly comparable. This is a critical problem for the interpretation of data over large areas, as it is difficult to reliably relate radiometric signatures in one survey area to those in another. The conventional solution to this problem is to 'back-calibrate' the data from older surveys to ground level concentrations of the radio-elements. This requires a linear transformation of the data that incorporates both a scaling (to accommodate detector volume, flying height and window widths) and a base-level shift (to accommodate inadequate background removal). The usual method of determining these scale and shift parameters is through field measurements using a calibrated portable spectrometer. However, because field work is expensive, an alternative solution is sought. An alternative approach is to use the differences between gridded survey data values in those areas where the surveys overlap to automatically estimate a base-level shift and scaling factor which, when applied to one of the surveys, minimises the differences in the overlap region. In this way an un-calibrated survey can be merged with a survey already calibrated to elemental concentrations of the radio-elements. Surveys can then be sequentially merged (or 'levelled') to produce regional compilations of the data. A similar approach has been used for many years to level aeromagnetic data. However, this sequential approach tends to propagate joining errors and introduce regional trends into the merged data. A new method overcomes this problem by considering the levelling of all of the grids in the regional compilation as a single inverse problem. Using a two-stage approach, the best base-level shift and scale for each survey grid is estimated. The first stage estimates the best relative shift and scale for each overlapping grid pair which, when applied to one of the pair, gives a least-squares fit to the grid values in the overlap area. The second stage uses the relative shift and scale parameters to estimate an absolute shift and scale for each grid that both honours the relative shift and scale parameters (in a least-squares sense) and brings each grid to the same absolute level as one or more base grids. The method works well as long as the data in the overlapping survey areas has a reasonable dynamic range.