Airborne electromagnetic modelling options and their consequences in target definition
Alan Yusen Ley-Cooper 1 7 Andrea Viezzoli 2 Julien Guillemoteau 3 Giulio Vignoli 4 James Macnae 5 Leif Cox 6 Tim Munday 11 CSIRO Mineral Resources Flagship, Bentley, Perth, WA 6102, Australia.
2 Aarhus Geophysics, C. F. Møllers Allé 4 DK-8000 Aarhus C, Denmark.
3 University of Potsdam Institute of Earth and Environmental Science, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany.
4 Geological Survey of Denmark and Greenland (GEUS), Lyseng Allé 1, 8270 Højbjer, Denmark.
5 RMIT University School of Applied Sciences, GPO Box 2476, Melbourne, Vic. 3001, Australia.
6 TechnoImaging, 4001 S 700 E, Salt Lake City, UT 84107, USA.
7 Corresponding author. Email: yusen.ley@csiro.au
Exploration Geophysics 46(1) 74-84 https://doi.org/10.1071/EG14045
Submitted: 1 May 2014 Accepted: 8 July 2014 Published: 1 October 2014
Abstract
Given the range of geological conditions under which airborne EM surveys are conducted, there is an expectation that the 2D and 3D methods used to extract models that are geologically meaningful would be favoured over 1D inversion and transforms. We do after all deal with an Earth that constantly undergoes, faulting, intrusions, and erosive processes that yield a subsurface morphology, which is, for most parts, dissimilar to a horizontal layered earth.
We analyse data from a survey collected in the Musgrave province, South Australia. It is of particular interest since it has been used for mineral prospecting and for a regional hydro-geological assessment. The survey comprises abrupt lateral variations, more-subtle lateral continuous sedimentary sequences and filled palaeovalleys. As consequence, we deal with several geophysical targets of contrasting conductivities, varying geometries and at different depths. We invert the observations by using several algorithms characterised by the different dimensionality of the forward operator.
Inversion of airborne EM data is known to be an ill-posed problem. We can generate a variety of models that numerically adequately fit the measured data, which makes the solution non-unique. The application of different deterministic inversion codes or transforms to the same dataset can give dissimilar results, as shown in this paper. This ambiguity suggests the choice of processes and algorithms used to interpret AEM data cannot be resolved as a matter of personal choice and preference.
The degree to which models generated by a 1D algorithm replicate/or not measured data, can be an indicator of the data’s dimensionality, which perse does not imply that data that can be fitted with a 1D model cannot be multidimensional. On the other hand, it is crucial that codes that can generate 2D and 3D models do reproduce the measured data in order for them to be considered as a plausible solution. In the absence of ancillary information, it could be argued that the simplest model with the simplest physics might be preferred.
Key words: airborne, electromagnetics, exploration, inversion, target.
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