
%0 Journal Article
%A Holden, Eun-Jung 
%A Kovesi, Peter 
%A Wedge, Daniel 
%A Aitken, Alan 
%T Effective methods to highlight and delineate anomalies from geophysical images
%J ASEG Extended Abstracts
%D 2013/08/12
%V 2013
%N 1
%P 1-4
%K 
%R https://doi.org/10.1071/ASEG2013AB147
%U https://www.publish.csiro.au/paper/ASEG2013AB147 
%X Geophysical data interpretation is largely an anomaly detection task which involves recognising and synthesising anomalous patterns within single or multiple datasets. The accuracy and efficiency of these interpretations heavily relies on the skills and practices of interpreters, thus the greatest challenge is to minimise personal biases to produce objective and consistent interpretation outcomes. We present an innovative data visualisation method which can empower interpreters to effectively delineate anomalies of varying frequency scales within aeromagentic data using a single image display. This is achieved by harnessing the power of image enhancement and visualisation techniques to assist interpretation. We adapted and extended the use of colour composite techniques to present different frequencies presented in potential field data. Aeromagnetic data from an area in Kirkland Lake, Ontario, Canada is used for our experiment. long wavelength and short wavelength anomalies are identified from the data using low pass- and high pass filters respectively. These two different frequency enhanced images and the original image are represented as separate colour channels which are then combined to generate a composite image. The luminance of the composite image is scaled to highlight high frequency signals as they hold the key for detailed structure interpretation. We use a technique called dynamic range compression, which preserves the integrity of the phase component of the signal while performing high pass filtering. The resulting display is compared to the geological map of the area to validate the effectiveness of the method. The proposed technique is widely adaptable for different types of datasets. 
