Airborne Electromagnetic Response Research Articles

Three-Dimensional Airborne Electromagnetic Data Inversion With Flight Altitude Correction

The flight altitude has a large effect on the airborne electromagnetic (AEM) responses. Due to the dynamic environment of the aircraft, the recorded sensor altitudes may contain errors. Research demonstrates that the AEM responses caused by a several meters altitude errors can be larger than caused by some anomalous body. Ignoring these errors will create erroneous results in AEM data interpretation. Considering that there is not yet a published 3D AEM inversion method that takes into account the flight altitude, we develop in this paper a 3D inversion algorithm for AEM with the flight height treated as an inversion parameter. For the forward modeling we use the finite element method, while for the inversion we use the Gauss-Newton optimization method. To make our inversion works for variable flight altitudes, we propose a scheme of 3D Jacobean matrix calculation for both the resistivities and flight altitudes without much increasing the computational cost. The numerical simulation result confirms that the flight altitude really has a large effect on the AEM responses. The inversions of synthetic data show that our 3D inversion method can both recover the resistivity distribution in the underground and decrease the altitude errors recorded, while the field data inversion demonstrates that our method can deliver a better inversion model with a smaller data misfit.

Fast Bayesian Inversion of Airborne Electromagnetic Data Based on the Invertible Neural Network

The inversion of airborne electromagnetic (AEM) data suffers from severe non-uniqueness of the solution. Bayesian inference provides the means to estimate structural uncertainty with a rich suite of statistical information. However, conventional Bayesian methods are computationally demanding in nonlinear inversions, especially considering the huge volumes of observational data, and thus are not feasible in practice. In this study, we develop a fast Bayesian inversion operator based on the invertible neural network (INN) to fully explore the posterior distribution and quantitatively evaluate the model uncertainty. The INN uses a latent variable to capture the information loss during measurement and constructs bijective mappings between AEM data and the resistivity model. We also introduce another noise variable into the INN to account for data uncertainties. Synthetic tests demonstrate that the INN can effectively recover the posterior distribution by a relatively small ensemble of predicted resistivity models whose AEM responses show a significant agreement with the true signal. We also apply the INN inversion operator to a field data set and obtain results consistent with previous studies. The INN shows considerable adaptability to field observations and strong noise robustness. Meanwhile, the INN delivers the inversion result with posterior model distribution for 23366 AEM time series in 20 seconds on a common PC. The inversion efficiency can be further improved for large data set due to its natural parallelizability. The proposed INN method can support fast Bayesian inversion of AEM data and offer tremendous potential for near real-time uncertainty evaluation of underground structures.

A deep learning-based network for the simulation of airborne electromagnetic responses

SUMMARYAirborne electromagnetic (AEM) method detects the subsurface electrical resistivity structure by inverting the measured electromagnetic field. AEM data inversion is extremely time-consuming when huge volumes of observational data are involved. Forward modelling is an essential part and represents a large proportion of computational cost in the inversion process. In this study, we develop an AEM simulator using deep learning as a computationally efficient alternative to accelerate 1-D forward modelling. Inspired by Google's neural machine translation, our AEM simulator adopts the long short-term memory (LSTM) modules with an encoder–decoder structure, combining the advantages in time-series regression and feature extraction. The well-trained LSTM network describes directly the mapping relationship between resistivity models with transceiver altitudes and time-domain AEM signals. The prediction results of the test set show that 95 per cent of the relative errors at most sampling points fall in the range of ±5 per cent, with average values within the range of ±0.5 per cent, indicating an overall prediction accuracy. We investigate the effects of the distributions of both resistivity and transceiver altitude in the training set on the prediction accuracy. The LSTM-based AEM simulator can effectively handle the resistivity characteristics involved in the training set and yields great sensitivity to the variations of transceiver altitudes. We also examine the adaptability of our AEM simulator for discontinuous resistivity variations. Synthetic tests indicate that the application effect of the AEM simulator relies on the completeness of the training samples and suggest that enriching the sample diversity is necessary to ensure the prediction accuracy, in cases of observation environments dominated by extreme transceiver altitudes or under-represented geological features. Furthermore, we discuss the influence of network configuration on its accuracy and computational efficiency. Our simulator can deliver ∼13 600 1-D forward modelling calculations within 1 s, which significantly improves the simulation efficiency of AEM data.

3D Unstructured Spectral Element Method for Frequency-Domain Airborne EM Forward Modeling Based on Coulomb Gauge

In this article, to solve the frequency-domain airborne electromagnetic (EM) modeling problem, we express the electric and magnetic field by the vector magnetic potential and the scalar electric potential and derive the governing equation using the Galerkin weighted residual method under the Coulomb gauge. To avoid the strong singularity of the solution near the transmitting source, we directly solve the relatively slow-changing secondary potential. By introducing the spectral element method (SEM) based on unstructured tetrahedral grids, in which the EM field in each element is characterized by high-order Proriol–Koornwinder–Dubiner (PKD) orthogonal polynomials, we can obtain stable and accurate numerical solutions. By establishing the mapping relationship between the physical domain, the right-angled tetrahedral reference domain, and the orthogonal hexahedral domain, we can easily accomplish the element matrix analysis and calculation. The numerical examples show that, when the SEM is used to simulate the airborne EM response, a high accuracy can be obtained even if a very coarse grid is used. Furthermore, since the tetrahedral grids can easily model the complex boundaries, the SEM based on unstructured grids has significant advantages for simulating complex underground structures.

3D inversion of large-scale frequency-domain airborne electromagnetic data using unstructured local mesh

Airborne electromagnetic (AEM) methods have been more and more widely used in mineral exploration, environmental and engineering studies, and ground water investigation. However, compared with ground-based electromagnetic (EM) methods, such as magnetotelluric or controlled-source EM, AEM methods generally produce large amounts of data, which leads to very costly 3D EM inversions. We have developed a new 3D AEM inversion scheme based on the finite-element method and unstructured tetrahedral local meshes. This is different from the traditional local mesh method in that the traditional method uses regular cuboids for 3D AEM inversions, whereas our scheme uses irregular tetrahedral meshes that can easily accommodate the topography and complex underground structure. Moreover, because we create our local mesh by extracting from part of the global model mesh, the relationship between the local and global meshes is straightforward, so we can easily create a projection of the Jacobian matrix between global and local meshes and rapidly construct the global Jacobian matrix for 3D EM inversions. After formulating the boundary value problem based on the finite-element method, we verify the accuracy of our modeling algorithm by checking against the semianalytical solution for a homogeneous half-space model, and we test our inversion algorithm by running inversions on synthetic and survey data collected over Vesterålen, Norway. The numerical experiments demonstrate that our method can model the AEM responses at high accuracy and recover the subsurface main resistivity structures from synthetic and field data.

Fast 3D time-domain airborne EM forward modeling using random under-sampling

The high sampling rate of airborne electromagnetic (AEM) systems can create huge data volumes, causing major challenges for three-dimensional (3D) electromagnetic modeling and inversions and constraining the practical applicability of AEM. Rapid and accurate forward modeling is the key to efficient 3D inversion. Here a new strategy for improving the efficiency of 3D time-domain AEM forward modeling is presented, based on compressed-sensing theory combined with the finite-volume method. A Poisson disk random under-sampling method was used to obtain random survey stations and to calculate AEM responses via the finite-volume method at these sampling stations. Coefficients from the curvelet transform with anisotropic characteristics for sparse expression were used to analyze the sparsity of AEM data, with the AEM responses of all survey stations being obtained by reconstruction. Key factors influencing the accuracy of the reconstruction are also discussed, including the sampling method and rate, the sparse transformation, and the noise. To test the effectiveness of the method, reconstructed results were compared with those from traditional schemes for forward modeling using all stations. Results indicate that the new strategy requires only 20% ~ 50% of samples to achieve high-precision reconstruction of the AEM signal even for complex geoelectrical structures, and the efficiency of 3D forward modeling can be enhanced by a factor of two to five, laying a foundation for fast AEM inversions of large datasets and complex models.

3D Airborne EM Forward Modeling Based on Time-Domain Spectral Element Method

Airborne electromagnetic (AEM) method uses aircraft as a carrier to tow EM instruments for geophysical survey. Because of its huge amount of data, the traditional AEM data inversions take one-dimensional (1D) models. However, the underground earth is very complicated, the inversions based on 1D models can frequently deliver wrong results, so that the modeling and inversion for three-dimensional (3D) models are more practical. In order to obtain precise underground electrical structures, it is important to have a highly effective and efficient 3D forward modeling algorithm as it is the basis for EM inversions. In this paper, we use time-domain spectral element (SETD) method based on Gauss-Lobatto-Chebyshev (GLC) polynomials to develop a 3D forward algorithm for modeling the time-domain AEM responses. The spectral element method combines the flexibility of finite-element method in model discretization and the high accuracy of spectral method. Starting from the Maxwell's equations in time-domain, we derive the vector Helmholtz equation for the secondary electric field. We use the high-order GLC vector interpolation functions to perform spectral expansion of the EM field and use the Galerkin weighted residual method and the backward Euler scheme to do the space- and time-discretization to the controlling equations. After integrating the equations for all elements into a large linear equations system, we solve it by the multifrontal massively parallel solver (MUMPS) direct solver and calculate the magnetic field responses by the Faraday's law. By comparing with 1D semi-analytical solutions for a layered earth model, we validate our SETD method and analyze the influence of the mesh size and the order of interpolation functions on the accuracy of our 3D forward modeling. The numerical experiments for typical models show that applying SETD method to 3D time-domain AEM forward modeling we can achieve high accuracy by either refining the mesh or increasing the order of interpolation functions.

3-D Time-Domain Airborne EM Inversion for a Topographic Earth

The topography has serious effects on time-domain airborne electromagnetic (AEM) signal, and the EM responses resulted from the topography frequently overwhelm those from the underground abnormal bodies. This brings big challenges to the traditional AEM interpretations based on a flat ground model. In this article, we develop a 3-D AEM inversion algorithm for a topographic earth model. The time-domain finite-element algorithm based on unstructured mesh is used to model the AEM responses. The tetrahedral grids provide the flexibility to fit the rugged topography. Furthermore, we adopt the Gauss–Newton method for our inversion of time-domain AEM data. In the forward modeling and the calculation of Jacobian matrix, we introduce an unstructured local mesh and decouple the meshes for forward modeling and inversion to improve the computational efficiency. For that purpose, we first set up an unstructured inversion mesh and then extract those cells corresponding to the sensitive area of AEM system for each survey station from the inversion mesh and construct a local forward mesh with the extracted cells as the core. After that, we take advantage of the spatial relationship between the local forward meshes and the inversion ones to set up the global sensitivity matrix for Gauss–Newton inversion. We test the effectiveness of our algorithm by applying our 3-D inversion code to both synthetic and survey data. The numerical experiments show that the Earth topography can have big influence on AEM inversions, and ignoring the topography can create serious distortion to AEM inversion results.

Removal of multisource noise in airborne electromagnetic data based on deep learning

Existing noise removal processes for airborne electromagnetic (AEM) data generally consist of several steps, with each using a specific method to remove a specific type of noise. To improve the efficiency of AEM denoising and reduce the impact of the subjective judgment of the operators on the processing results, we have adopted a deep learning method based on a denoising autoencoder (DAE), which enables in one single processing step the removal of multisource noise. The most common noise sources in AEM data, including motion-induced noise, nearby or moderately distant sferics noise, power-line noise, and background electromagnetic noise, will be combined with a large number of simulation responses to build a training set. The data in the training set will be used to train the deep learning DAE neural network so that the neural network could fully learn the respective characteristics of the signal and noise and further effectively distinguish the AEM response signal (useful signal) from the above noise. The field data were processed using this method, and the processing results were compared with those obtained using traditional methods. The comparison test revealed that this method is helpful to reduce the influence of subjective factors on the quality of data results and compress the entire AEM data processing time.

Imaging subsurface resistivity structure from airborne electromagnetic induction data using deep neural network

ABSTRACTDue to the rapid development and spread of deep learning technologies, potential applications of artificial intelligence technology in the field of geophysical inversion are being explored. In this study, we applied a deep neural network (DNN) to reconstruct one-dimensional electrical resistivity structures from airborne electromagnetic (AEM) data for varying sensor heights. We used numerical models and their simulated AEM responses to train the DNN to be an inversion operator, and determined that it was possible to train the DNN without the use of stabilisers on the subsurface structures. We compared the quantitative performance of DNN and Gauss–Newton inversion of synthetic datasets, and demonstrated that DNN inversion reconstructed the subsurface structure more accurately, and within a significantly shorter period. We subsequently applied DNN inversion to a field dataset to quantify the effectiveness and applicability of the proposed method for real data. The results of the current study will open new avenues for real-time imaging of subsurface structures from AEM data.

3D airborne EM anisotropic effect and identification modeling by SE method

Spectral-element (SE) method is a kind of higher-order finite-element method based on weighted residual technique; however, the basis functions for SE are polynomial, like Gauss–Lobatto–Legendre (GLL) or Gauss–Lobatto–Chebyshev (GLC) polynomials. Because of its high modeling accuracy and flexibility, it has been successfully used in computational electromagnetism. In this paper, we use the SE method for 3D frequency-domain airborne electromagnetic (AEM) modeling for an anisotropic earth and we take horizontal coplanar and vertical coaxial coil systems as example for the modeling. We first derive the discrete governing equation from Maxwell equations, in which the conductivity tensor is obtained by 3 Euler rotations of a principal conductivity tensor. GLL polynomial is selected as the vector SE basis functions, while GLL integration is applied for calculating matrix elements. A direct solver is used for the solution of the matrix equations system. The modeling accuracy is checked against a semi-analytical solution. Further, we calculate AEM responses for different anisotropic models and demonstrate that SE method canobtain high precision by either increasing SE order or refining meshes, so that it can save computation cost vastly. Numerical results further confirm that the anisotropy of both 3D body and host rock can be identified from the polar plots of ratio of magnetic field components.

Mapping Aquifer Systems with Airborne Electromagnetics in the Central Valley of California.

The passage of the Sustainable Groundwater Management Act in California has highlighted a need for cost-effective ways to acquire the data used in building conceptual models of the aquifer systems in the Central Valley of California. One approach would be the regional implementation of the airborne electromagnetic (AEM) method. We acquired 104 line-kilometers of data in the Tulare Irrigation District, in the Central Valley, to determine the depth of investigation (DOI) of the AEM method, given the abundance of electrically conductive clays, and to assess the usefulness of the method for mapping the hydrostratigraphy. The data were high quality providing, through inversion of the data, models displaying the variation in electrical resistivity to a depth of approximately 500 m. In order to transform the resistivity models to interpreted sections displaying lithology, we established the relationship between resistivity and lithology using collocated lithology logs (from drillers' logs) and AEM data. We modeled the AEM response and employed a bootstrapping approach to solve for the range of values in the resistivity model corresponding to sand and gravel, mixed coarse and fine, and clay in the unsaturated and saturated regions. The comparison between the resulting interpretation and an existing cross section demonstrates that AEM can be an effective method for mapping the large-scale hydrostratigraphy of aquifer systems in the Central Valley. The methods employed and developed in this study have widespread application in the use of the AEM method for groundwater management in similar geologic settings.

Adaptive finite element for 3D time-domain airborne electromagnetic modeling based on hybrid posterior error estimation

Airborne electromagnetic (AEM) forward modeling has been extensively developed in past years. However, not much attention has been paid to the adaptive numerical algorithms for time-domain electromagnetic modeling. We have created an adaptive method that can generate an effective mesh for time-domain 3D AEM full-wave modeling using an unstructured finite-element method and a backward Euler scheme. For the estimation of the posterior error in the adaptive process, we use a hybrid technique based on the continuity of the normal current density for modeling the off-time channels, and on the continuity of the tangential magnetic field for the on-time channels. To improve the stability of the forward modeling and control the number of grids in the adaptive process, a random grid-selection technique is applied. We check the modeling accuracy of the algorithm by comparing our adaptive results with the semianalytical solution for a time-domain AEM system over a homogeneous half-space. Furthermore, we test the effectiveness of our algorithm for multiple-source time-domain AEM systems by analyzing the meshes generated by the adaptive method and the model results. Finally, we study the topographic effect by calculating time-domain AEM responses over a hill model with an abnormal body embedded.

Comparing induced polarization responses from airborne inductive and galvanic ground systems: Lewis Ponds, New South Wales

We have evaluated the mapping of polarizable material using a Cole-Cole model to fit second-order effects in concentric-loop airborne electromagnetic system responses. At Lewis Ponds in New South Wales, an inverted ground dipole-dipole array data has accurately imaged in 3D disseminated sulfide extending above and around ore grade massive sulfides. The polarizable zone is present in the near-surface, where, from modeling, airborne systems may have sensitivity to the small inductive induced polarization effects. Although the inverted chargeability measured from galvanic current injection into the ground was spatially coincident with the mineralized target, the estimated chargeabilities from induced polarization effects in an airborne versatile time-domain electromagnetic survey were substantially displaced to the east. The airborne induced polarization response may be associated with finer grained mineralization in the hanging wall of the sulfide deposits, or have a quite different source, such as clays associated with faulting.

A goal-oriented adaptive finite-element method for 3D scattered airborne electromagnetic method modeling

We have developed a goal-oriented adaptive unstructured finite-element method based on the scattered field for 3D frequency-domain airborne electromagnetic (AEM) modeling. To guarantee the EM field divergence free within each element and the continuity conditions at electrical material interfaces, we have used the edge-based shape functions to approximate the electrical field. The posterior error for finite-element adaptive meshing procedure is estimated from the continuity of the normal component of the current density, whereas the influence functions are estimated by solving a dual forward problem. Because the imaginary part of the scattered current is discontinuous and the real part is continuous, we use the latter to estimate the posterior error. For the multisources and multifrequencies problem in AEM, we calculate the weighted posterior error for each element by considering only those transmitter-receiver pairs that do not adhere to our convergence criteria. Finally, we add a minimum volume constraint to improve the stability of the adaptive procedure. To check the accuracy, we compared our adaptive results with the semianalytical solutions for AEM systems over a half-space model. To test the effectiveness of our algorithm for multiple sources and multiple frequencies of AEM, we analyzed meshes for separate frequencies and for combined frequencies. Finally, we calculated the AEM responses over a hill model with and without embedded abnormal bodies to prove the feasibility of our algorithm for AEM variable topography modeling.

Near-surface geophysical surveys at the Duport gold deposit, Ontario, Canada: Relating airborne responses to small-scale geologic features

Near-surface geophysical measurements using magnetometer, magnetic susceptibility, terrain conductivity, and time-domain electromagnetic instruments were made at the shear-hosted Duport gold deposit on Cameron Island in Shoal Lake, western Ontario, Canada, to help relate airborne total magnetic intensity (TMI) and helicopter electromagnetic survey data to small-scale geologic features. The magnetic airborne response provides a weak indication of a narrow anomaly within the Duport deformation zone, and the airborne electromagnetic response provides an indication of enhanced conductivity in the northwest of Cameron Island. In contrast, surface magnetic responses are dominated by the narrow 10,000–15,000 nT magnetic anomaly of a talc-chlorite-dolomite schistose basalt unit, a feature barely visible in the airborne TMI data. This geologic unit hosts the veins containing gold mineralization, so the surface TMI data provide a valuable response for delineating the corresponding rocks. Modeling of the TMI data indicates that the unit has a susceptibility of up to 0.7 SI and a corresponding magnetite content of up to 20%. The TMI data also reveal along-strike variations in the magnetic anomaly providing information on the component of ultramafic rocks in the protolith of the unit. The surface geophysical data allow the enhanced conductivity in the northwest part of Cameron Island to be attributed to several narrow sulfidized zones containing up to 10% pyrrhotite. Additional conductive and positively magnetized zones are associated with concentrations of 5%–10% of magnetite. The near-surface geophysics at the Duport deposit provided a rapid and inexpensive method for defining the magnetic and electrical properties of the geologic units at the site and for defining the exact location, width, and internal structure of features observed in the airborne geophysical data. Availability of magnetic susceptibility measurements from a single drill core enhanced the accuracy of the interpretations and the ability to relate the near-surface geophysical responses to geologic features.

Decomposing the electromagnetic response of magnetic dipoles to determine the geometric parameters of a dipole conductor

A novel automatic data interpretation algorithm is presented for modelling airborne electromagnetic (AEM) data acquired over resistive environments, using a single-component (vertical) transmitter, where the position and orientation of a dipole conductor is allowed to vary in three dimensions. The algorithm assumes that the magnetic fields produced from compact vortex currents are expressed as a linear combinations of the fields arising from dipoles in the subsurface oriented parallel to the [1, 0, 0], [0, 1, 0], and [0, 0, 1], unit vectors. In this manner, AEM responses can be represented as 12 terms. The relative size of each term in the decomposition can be used to determine geometrical information about the orientation of the subsurface conductivity structure. The geometrical parameters of the dipole (location, depth, dip, strike) are estimated using a combination of a look-up table and a matrix inverted in a least-squares sense.Tests on 703 synthetic models show that the algorithm is capable of extracting most of the correct geometrical parameters of a dipole conductor when three-component receiver data is included in the interpretation procedure. The algorithm is unstable when the target is perfectly horizontal, as the strike is undefined. Ambiguities may occur in predicting the orientation of the dipole conductor if y-component data is excluded from the analysis.Application of our approach to an anomaly on line 15 of the Reid Mahaffy test site yields geometrical parameters in reasonable agreement with previous authors. However, our algorithm provides additional information on the strike and offset from the traverse line of the conductor. Disparities in the values of predicted dip and depth are within the range of numerical precision. The index of fit was better when strike and offset were included in the interpretation procedure. Tests on the data from line 15701 of the Chibougamau MEGATEM survey shows that the algorithm is applicable to situations where three-component AEM data is available.

Quantifying Airborne Induced Polarization effects in helicopter time domain electromagnetics

This paper derives the Airborne Induced Polarization (AIP) response of an airborne electromagnetic (AEM) system to a horizontal, thin sheet conductor. A vertical component double-dipole approximates helicopter systems with towed concentric horizontal transmitter and receiver loops in frequency- or time-domain. In time domain, the AIP effect typically shows up as late-time negative data with amplitude 4 to 5 orders of magnitude smaller than the early-time peak of the positive AEM responses. Because of limited bandwidth from the short sample time after the decay of inductive responses, accurate extraction of intrinsic AIP parameters other than a minimum chargeability is almost impossible. Modelling further suggests that AIP effects in double-dipole AEM systems can only be reliably detected from polarizable material in the top few tens of metres. A titanium mineral exploration case history from the Lac Brûlé area, Quebec, Canada illustrates strong spatial coherence of AIP minimum chargeability estimates and their independence from other effects such as conductivity and magnetic susceptibility.

Sensitivity of airborne geophysical data to sublacustrine and near-surface permafrost thaw

Abstract. A coupled hydrogeophysical forward and inverse modeling approach is developed to illustrate the ability of frequency-domain airborne electromagnetic (AEM) data to characterize subsurface physical properties associated with sublacustrine permafrost thaw during lake-talik formation. Numerical modeling scenarios are evaluated that consider non-isothermal hydrologic responses to variable forcing from different lake depths and for different hydrologic gradients. A novel physical property relationship connects the dynamic distribution of electrical resistivity to ice saturation and temperature outputs from the SUTRA groundwater simulator with freeze–thaw physics. The influence of lithology on electrical resistivity is controlled by a surface conduction term in the physical property relationship. Resistivity models, which reflect changes in subsurface conditions, are used as inputs to simulate AEM data in order to explore the sensitivity of geophysical observations to permafrost thaw. Simulations of sublacustrine talik formation over a 1000-year period are modeled after conditions found in the Yukon Flats, Alaska. Synthetic AEM data are analyzed with a Bayesian Markov chain Monte Carlo algorithm that quantifies geophysical parameter uncertainty and resolution. Major lithological and permafrost features are well resolved by AEM data in the examples considered. The subtle geometry of partial ice saturation beneath lakes during talik formation cannot be resolved using AEM data, but the gross characteristics of sub-lake resistivity models reflect bulk changes in ice content and can identify the presence of a talik. A final synthetic example compares AEM and ground-based electromagnetic responses for their ability to resolve shallow permafrost and thaw features in the upper 1–2 m below ground outside the lake margin.

3D inversion of airborne electromagnetic data

Time-domain airborne surveys gather hundreds of thousands of multichannel, multicomponent samples. The volume of data and other complications have made 1D inversions and transforms the only viable method to interpret these data, in spite of their limitations. We have developed a practical methodology to perform full 3D inversions of entire time- or frequency-domain airborne electromagnetic (AEM) surveys. Our methodology is based on the concept of a moving footprint that reduces the computation requirements by several orders of magnitude. The 3D AEM responses and sensitivities are computed using a frequency-domain total field integral equation technique. For time-domain AEM responses and sensitivities, the frequency-domain responses and sensitivities are transformed to the time domain via a cosine transform and convolution with the system waveform. We demonstrate the efficiency of our methodology with a model study relevant to the Abitibi greenstone belt and a case study from the Reid-Mahaffy test site in Ontario, Canada, which provided an excellent practical opportunity to compare 3D inversions for different AEM systems. In particular, we compared 3D inversions of VTEM-35 (time-domain helicopter), MEGATEM II (time-domain fixed-wing), and DIGHEM (frequency-domain helicopter) data. Our comparison showed that each system is able to image the conductive overburden and to varying degrees, detect and delineate the bedrock conductors, and, as expected, that the DIGHEM system best resolved the conductive overburden, whereas the time-domain systems most clearly delineated the bedrock conductors. Our comparisons of the helicopter and fixed-wing time-domain systems revealed that the often-cited disadvantages of a fixed-wing system (i.e., response asymmetry) are not inherent in the system, but rather reflect a limitation of the 1D interpretation methods used to date.