Enhancement of airborne magnetic data using the variation method of fractal dimension estimation

Abstract

Textural-based processing of airborne magnetic data is becoming a recognised tool for image enhancement. The variation method of fractal dimension (FD) estimation is a measure of texture that can resolve subtle textural contrasts as well as edge features that are otherwise difficult to discern. Application of the variation method to synthetic fractal datasets highlighted its ability to distinguish textural contrasts when using local estimates of FD. The variation method was also able to enhance thin linear anomalies, linear ramp anomalies and sinusoidal ramp anomalies contained in synthetic datasets. The variation method clearly resolved these features even in the presence of Gaussian noise. The results demonstrated that smaller window sizes will more effectively discriminate and enhance edges. Application of the variation method on the Ghanzi-Chobe aeromagnetic dataset highlighted features that were not resolved in the greyscale total magnetic intensity image. Comparison of the variation method with standard derivative-based enhancements showed that the variation method enhanced similar trends but with greater clarity. It also enhanced features that were not revealed by horizontal- and vertical-derivative images. Whilst the variation method needs to be applied to data from a wider variety of magnetic regimes, the results suggest that the technique can provide useful information not available from conventional enhancement processes.