Mass anomaly visualisation and depth estimation from full tensor gradient gravity data

Daniel Wedge; Yathunanthan Sivarajah; Eun-Jung Holden; Peter Kovesi; Chris Wijns; Paul Johnston

doi:10.1071/ASEG2013ab152

ISSN: 2202-0586
E-ISSN:

oa Mass anomaly visualisation and depth estimation from full tensor gradient gravity data
Authors Daniel Wedge^a, Yathunanthan Sivarajah^a, Eun-Jung Holden^a, Peter Kovesi^a, Chris Wijns^a,b and Paul Johnston^c
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^a Centre for Exploration TargetingThe University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, , Australia, [email protected] ^b First Quantum Minerals Ltd, 24 Outram Street West Perth, WA, 6005, , Australia ^c School of PhysicsThe University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2013, Issue ASEG2013 - 23rd Geophysical Conference, Dec 2013, p. 1 - 5
DOI: https://doi.org/10.1071/ASEG2013ab152
- Published online: 01 Dec 2013

Abstract

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.

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 3D locations of mass anomalies 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.

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References

Barnes, G. and Lumley, J., 2011, Processing gravity gradient data. Geophysics, 76(2):133-147.
Beiki, M. and Pedersen, L. B., 2010, Eigenvector analysis of gravity gradient tensor to locate geologic bodies. Geophysics, 75(6):137-149.
Blakely, R. J. And Simpson, R. W., 1986, Approximating edges of source bodies from magnetic or gravity anomalies. Geophysics, 51(7):1494-1498.
Cabral, B., 1993, Imaging vector fields using line integral convolution, in Proceedings of SIGGRAPH93.
Dransfield, M. H., 1994, Airborne Gravity Gradiometry, PhD thesis, The University of Western Australia.
Droujinine, A., Vasilevsky, A. and Evans, R., 2007, Feasibility of using full tensor gradient data for detection of local lateral density contrasts during reservoir monitoring. International Journal of Geophysics, 169:795-820.
Hotz, I., Feng, L., Hagen, H., Hamann, B., Joy, K. and Jeremic, B., 2004, Physically based methods for tensor field visualisation, in Proceedings of IEEE Visualisation.
Hotz, I., Feng, L., Hamann, B. and Joy, K., 2009, Tensor- fields visualization using a fabric-like texture applied to arbitrary two-dimensional surfaces. Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration, 139-155.
Pedersen, L. B. and Rasmussen, T. M., 1990, The gradient tensor of potential field anomalies: Some implications on data collection and data processing of maps. Geophysics, 55(12):1558-1566.
Stasinowsky, W., 2010, The advantages of the full tensor over Gz. In Airborne Gravity 2010 - Abstracts from the ASEG- PESA Airborne Gravity 2010 Workshop.

/content/journals/10.1071/ASEG2013ab152

Article Type: Research Article

Keyword(s): depth estimation; FTG gravity visualisation

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oa Mass anomaly visualisation and depth estimation from full tensor gradient gravity data

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