Airborne Gravity 2010 - Geoscience Australia

Airborne Gravity 2010
Summary
From gravity gradients to density gradients
Peter Fullagar 1 and Glenn Pears 2
1 Fullagar Geophysics Pty Ltd (fullagargeophysics@yahoo.com)
2 Mira Geoscience Pty Ltd (glennp@mirageoscience.com)
The airborne gravity gradiometry systems used over the past decade have been capable of measuring
one or more combinations of gravity tensor components efficiently over large and inaccessible areas.
Key characteristics of the systems are the extremely small magnitude of the gradients and the
sensitivity of the response to density variations in the near-surface. In this paper, we explore some of
the issues encountered when gravity gradient data are prepared for either qualitative or quantitative
interpretation. Sensitivity to the near-surface accentuates the influence of topography, which often
overwhelms the geologically interesting signal. Terrain effects are modelled at the highest practical
resolution. Terrain models need not be uniform in density, e.g., over lakes. Near-surface sensitivity is
advantageous for definition of outcropping or sub-cropping targets, e.g., kimberlites. However,
sensitivity to the near-surface can also be equated to a loss of sensitivity at depth, seriously degrading
the effectiveness of gravity gradients for deep exploration. Gravity gradient data are low-passed
filtered along flight lines in order to suppress motion noise. It is important to apply an equivalent lowpass
filter to the calculated gravity gradients to avoid introduction of phantom anomalies, e.g., during
terrain correction. However, a complication can arise if the filtering is not fully documented by the
contractor. These points are illustrated in examples featuring both synthetic and field data.
Introduction
Gravity gradiometry has come of age in the past decade, with the development of airborne systems
capable of measuring individual gravity gradient tensor components or combinations thereof.
Commercial gravity gradiometry represents a major technical advance, delivering a completely new
capability to exploration companies. Gravity gradients can now be acquired rapidly in remote and
inaccessible areas, including down drill holes and, potentially, sub-sea.
The advantages of airborne gravity gradiometry relative to ground gravity are speed of acquisition and
completeness of coverage. The advantages with respect to airborne gravity are relative insensitivity to
aircraft motion noise and improved definition of the gravity field (Fitzgerald et al., 2009), and hence
there is scope to increase line spacing. The general advantages of gradiometry are superior resolution
of closely spaced sources and suppression of regional effects. These general advantages flow from
more rapid geometrical attenuation: like magnetic components, gravity gradients from point sources
are characterised by 1/r 3 spatial dependence. However, in contrast to magnetic properties where the
susceptibility of ordinary rocks is very low, the density is always substantial. Therefore, a defining
characteristic of gravity gradients is sensitivity to the near-surface.
Gravity gradients are recorded both for geological mapping and for direct target detection. This paper
explores some of the issues encountered when gravity gradient data are prepared for interpretation.
Sensitivity to the near-surface is the central theme. This sensitivity is a blessing for resolution of
shallow structure and stratigraphy, and for definition of outcropping or sub-cropping targets, e.g.,
kimberlites. However, enhancement of shallow features is a disadvantage in exploration for buried
targets. The problem is compounded by the inherent variability of the shallow sub-surface, owing to
weathering and to transported cover. Finally, sensitivity to the near-surface also implies sensitivity to
topography and flying height. Some possible pitfalls are illustrated in examples featuring both synthetic
and field data. The factors discussed here are relevant whether the data are interpreted qualitatively,
in terms of domains and trends, or quantitatively, in terms of density models. All modelling and
inversion described below was performed using VPmg (Fullagar et al., 2000, 2004, 2008; Fullagar and
Pears, 2007).
The focus here is on preparation for interpretation, more so than on interpretation itself. However, it
must never be forgotten that for successful interpretation of gravity gradient data, especially
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