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 quantites 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 an algorithm for visualising FTG gravity datasets which displays 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. We demonstrate the applicability of the algorithm on two datasets: one synthetic dataset with various levels of noise, and one real-world dataset.
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