Neil Symington

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Using AEM and GMR Methods for Non-Invasive, Rapid Reconnaissance Mapping and Characterisation of Groundwater Systems in the Kimberley Region, Northern Australia

ASEG Extended Abstracts (2018)

Ken Lawrie Neil Symington KokPiang Tan Niels B. Christensen Larysa Halas

The East Kimberley Region of north-western Australia has been identified as a priority for potential agricultural development. Within this region, the Ord Bonaparte Plain is remote, with limited access in an area of great cultural and environmental sensitivity. Initially, spatio-temporal mapping using remote sensing (and potential field) data, combined with data on the deeper basin geology was used to plan an airborne electromagnetics (AEM) survey. The relatively resistive nature of the basin sediments has enabled the AEM to map the hydrostratigraphy to depths of 300-500m, except in the coastal zone affected by seawater intrusion. Two overlying aquifers, separated by a faulted, ‘leaky’ aquitard, have been identified.The AEM and remote sensing data were subsequently used to plan a ground magnetic resonance (GMR) survey. The latter has enabled a water table map to be constructed in an area with almost no drilling, while also enabling key aquifer properties to be determined. The target aquifer has a high free water content and high transmissivity. The GMR results have been validated by drilling, borehole Nuclear Magnetic Resonance (NMR), and induction logging.Integration of AEM, GMR and temporal (Landsat) remote sensing data has enabled rapid mapping and characterisation of the groundwater system in a data-poor, culturally and environmentally sensitive area. These data have also revealed complex faulting within and bounding the aquifer system, delineated the sea-water intrusion interface, and mapped groundwater dependent ecosystems. These data have been used to target drilling and pump testing that will inform groundwater modelling, water allocations and development decisions.

10.1071/aseg2018abw8_4h

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An information theoretic Bayesian uncertainty analysis of AEM systems over Menindee Lake, Australia

Geophysical Journal International (2023)

Anandaroop Ray Yusen Ley‐Cooper Ross C. Brodie Richard G. Taylor Neil Symington

SUMMARY Long-range, active-source airborne electromagnetic (AEM) systems for near-surface conductivity imaging fall into two categories: helicopter (rotary-wing) borne or fixed-wing aircraft borne. A multitude of factors such as flying height, transmitter loop area and current, source waveforms, aerodynamic stability and data stacking times contribute to the geological resolvability of the subsurface. A comprehensive comparison of the relative merits of each system considering all such factors is difficult, but test flights over well-constrained subsurface geology with downhole induction logs are extremely useful for resolution studies. However, given the non-linear nature of the electromagnetic inverse problem, handling transmitter–receiver geometries in fixed-wing aircraft is especially challenging. As a consequence of this non-linearity, inspecting the closeness of downhole conductivities to deterministic inversion results is not sufficient for studying resolvability. A more comprehensive picture is provided by examining the variation in probability mass of the depth-wise Bayesian posterior conductivity distributions for each kind of AEM system within an information theoretic framework. For this purpose, probabilistic inversions of data must be carried out. Each acquiring system should fly over the same geology, survey noise levels must be measured and the same prior probabilities on conductivity must be used. With both synthetic models as well as real data from over the Menindee calibration range in New South Wales, Australia, we shed new light on the matter of AEM inverse model uncertainty. We do this using two information theoretic attributes derived from different Kullback–Leibler divergences—Bayesian information gain, and a strictly proper scoring rule, to assess posterior probabilities estimated by a novel Bayesian inversion scheme. The inversion marginalizes fixed-wing geometry attributes as generic nuisance parameters during Markov chain sampling. This is the first time-domain AEM study we know of, that compares nuisance marginalized subsurface posterior conductivities from a fixed-wing system, with rotary-wing derived posterior conductivities. We also compare field results with induction log data where available. Finally, we estimate the information gain in each case via a covariate shift adaptation technique that has not been used before in geophysical work. Our findings have useful implications in AEM system selection, as well as in the design of better deterministic AEM inversion algorithms.

10.1093/gji/ggad337

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