
%0 Journal Article
%A Wedge, Daniel 
%A Sivarajah, Yathunanthan 
%A Holden, Eun-Jung 
%A Kovesi, Peter 
%A Wijns, Chris 
%A Johnston, Paul 
%T Mass anomaly visualisation and depth estimation from full tensor gradient gravity data
%J ASEG Extended Abstracts
%D 2013/08/12
%V 2013
%N 1
%P 1-5
%K 
%R https://doi.org/10.1071/ASEG2013AB152
%U https://www.publish.csiro.au/paper/ASEG2013AB152 
%X Full Tensor Gradient (FTG) gravity data measures the derivatives of the Earth's gravitational field. Such variations in the gravitational field may be due to the presence of bodies of higher or lower density relative to the surrounding rock. Recent technological advances have made airborne measurement of FTG data possible, resulting in the rapid collection of vast quantities of data particularly for mineral, oil and gas exploration purposes. As the gravity tensor contains 5 independent components, effective visualisation of this high-dimensional dataset is advantageous for efficient processing of the FTG data. We present two aspects of visualising mass anomalies in FTG gravity datasets. First, we create a textured image where the orientations of the resulting texture reflect local lateral orientations encoded in the FTG data. It uses a colour map to highlight geologically significant structures such as linear features and radially symmetric points by identifying different geological features and using colour components to represent different feature types. This visualisation method is shown to be robust to significant levels of modelled noise, and we demonstrate its applicability to a field FTG survey. Second, we present an algorithm for estimating the depths of mass anomalies in FTG data. A voxel representation of the subsurface is created and voxels are voted for according to gravitational curvature properties encoded in the FTG tensor. A visualisation of the volume at successive depths highlights locations of mass anomalies at each depth, with mass anomalies located at local maxima of the volume. The algorithm is evaluated on a forward-modelled FTG dataset where the depths of mass anomalies are known. The depths of mass anomalies are shown to be accurately located in the presence of noise. 
