Model-consistent rescaling to correct amplitude calibration problems in HEM data

A recent increase in the environmental usage of Airborne EM has shown the need to provide accurate values of depth and conductivity. Calibration problems in helicopter EM data produce imprecise conductivity depth inversions and images (CDIs), whether plotted in maps or sections. Accurate images are essential requirements in order to target smaller, near-surface objectives such as salinity outbreaks. To ensure agreement between ground-truth (such as conductivity logs or ground EM data inversions), data recalibration has been applied before processing. A limitation of ground-based methods is that they tend to provide spatially restricted constraints. This paper presents an alternative statistical method developed to provide consistency with simple conductivity models. Measured data and theoretical models are transformed, from amplitude in real and quadrature phase with the primary field, to two dimensionless quantities: b, the ratio between a data prediction and a calculated inductive limit, and a, which is a standard EM response parameter. In conductivity-independent ab domain, the response of a variety of synthetic models based on expected geology is calculated and compared to the median of the larger amplitude field data. The data are then rescaled in the ab domain so that the recalibrated median response lies exactly on the theoretical curve. The amplitude rescaling was applied to HEM datasets collected in the Riverland and Tintinara areas in South Australia. The results were compared using maps and CDI images of the raw and recalibrated data. The original data, as delivered, produced CDI images that were generally inconsistent with borehole conductivity data. However, amplitude rescaling to ensure thin-sheet consistency has produced remarkable agreement in depth between ground-truth and the CDI sections.