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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. [ABSTRACT FROM AUTHOR]

We have applied a Bayesian inference algorithm and released open-source code for the 1D inversion of audio-frequency magnetotelluric data. The algorithm uses a trans-dimensional Markov chain Monte Carlo technique to solve for a probabilistic resistivity-depth model. The inversion employs multiple Markov chains to generate an ensemble of millions of resistivity models that adequately fit the data given the assigned noise levels. The trans-dimensional aspect of the inversion means that the number of layers in the resistivity model is solved for rather than being predetermined. The inversion scheme favours a parsimonious solution and the acceptance criterion ratio is theoretically derived such that the Markov chain will eventually converge to an ensemble that is a good approximation of the posterior probability density (PPD). Once the ensemble of models is generated, its statistics are analysed to assess the PPD and to quantify model uncertainties. This approach gives a thorough exploration of model space and a more robust estimation of uncertainty than deterministic methods allow. We demonstrate the application of the method to cover thickness estimation for a number of regional drilling programs. Comparison with borehole results demonstrates that the method is capable of identifying major stratigraphic units with resistivity contrasts. Our results have assisted with drill site targeting and have helped to reduce the uncertainty and risk associated with intersecting targeted stratigraphic units in covered terrains. Interpretation of the audio-frequency magnetotelluric data has improved our understanding of the distribution and geometries of sedimentary basins. From an exploration perspective, mapping sedimentary basins and covered near-surface geological features supports the effective search for mineral deposits in greenfield areas. • Bayesian inference algorithm via trans-dimensional Markov chain Monte Carlo technique. • Probabilistic inversion of audio magnetotelluric data to characterise cover. • Cover thickness estimation helps drill site targeting by reducing uncertainty. • Regional drilling programs provide ground-truth data to validate the models. • Mapping sedimentary basins promotes mineral exploration in covered terrains. [ABSTRACT FROM AUTHOR]