Frequency-domain Airborne Electromagnetic Research Articles

Three-Dimensional Separate and Joint Inversions of Multi-Component Frequency-Domain Airborne Electromagnetic Data: Synthetic Model Studies.

Airborne electromagnetic (AEM) surveys using airborne mobile platforms enable rapid and efficient exploration of areas where groundwork is difficult. They have been widely used in fields such as shallow resource exploration and environmental engineering. Three-dimensional AEM inversion is the main technique used in fine structural interpretation. However, most current methods focus on separate component data inversions, which limit the kinds of structures that can be recovered in the inversion results. To address this issue, a method for the robust 3D joint inversion of multicomponent frequency-domain AEM data was developed in this study. First, a finite element method based on unstructured tetrahedral grids was used to solve the forward problem of frequency-domain AEM data for both isotropic and anisotropic media. During inversion, a limited-memory quasi-Newton (L-BFGS) method was used to reduce the memory requirements and enable the joint inversion of large-scale multicomponent AEM data. The effectiveness of our algorithm was demonstrated using synthetic models for both isotropic and anisotropic cases, with 5% Gaussian noise added to the modeling data to simulate the measured data for separate and joint inversions. The results of the synthetic models show that joint inversion has advantages over separate inversion in that it enables the recovery of finer underground structures and provides a novel approach for the fine interpretation of frequency-domain AEM data.

3D Airborne EM Forward Modeling Based on Finite-Element Method with Goal-Oriented Adaptive Octree Mesh

The finite-element (FE) method for three-dimensional (3D) airborne electromagnetic (AEM) modeling can flexibly simulate complex geological structures at high accuracy. However, it has low efficiency and high computational requirements. To solve these problems, one needs to generate meshes more reasonably. In view of this, we develop an adaptive octree meshing scheme for frequency-domain AEM modeling. The octree meshes have the characteristics of regularity and flexibility, while the adaptive algorithm can effectively refine the mesh locally. In our adaptive mesh generation, the posterior errors and weighted coefficients are used to construct the final weighted posterior errors. We verify the accuracy of our method by comparing its results with semi-analytical solutions for a half-space model. Furthermore, we use the spectral-element (SE) method and our method to calculate EM responses for an abnormal block model and compare their computational costs. The results show that our adaptive scheme has obviously technical advantages over SE method for AEM modeling with multiple frequencies and multiple survey stations. Finally, we calculate a model with complex geological structures to verify the feasibility of our algorithm in complex geological circumstances.

A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA

Groundwater-flow models require the spatial distribution of the hydraulic conductivity parameter. One approach to defining this spatial distribution in groundwater-flow model grids is to map the electrical resistivity distribution by airborne electromagnetic (AEM) survey and establish a petrophysical relation between mean resistivity calculated as a nonlinear function of the resistivity layering and thicknesses of the layers and aquifer transmissivity compiled from historical aquifer tests completed within the AEM survey area. The petrophysical relation is used to transform AEM resistivity to transmissivity and to hydraulic conductivity over areas where the saturated thickness of the aquifer is known. The US Geological Survey applied this approach to a gain better understanding of the aquifer properties of the Mississippi River Valley alluvial aquifer. Alluvial-aquifer transmissivity data, compiled from 160 historical aquifer tests in the Mississippi Alluvial Plain (MAP), were correlated to mean resistivity calculated from 16,816 line-kilometers (km) of inverted resistivity soundings produced from a frequency-domain AEM survey of 95,000 km2 of the MAP. Correlated data were used to define petrophysical relations between transmissivity and mean resistivity by omitting from the correlations the aquifer-test and AEM sounding data that were separated by distances greater than 1 km and manually calibrating the relation coefficients to slug-test data. The petrophysical relation yielding the minimum residual error between simulated and slug-test data was applied to 2,364 line-km of AEM soundings in the 1,000-km2 Shellmound (Mississippi) study area to calculate hydraulic property distributions of the alluvial aquifer for use in future groundwater-flow models.

Two-dimensional fast imaging of airborne EM data based on U-net

As an efficient geophysical exploration tool, the airborne electromagnetic (AEM) method has been widely used in mineral exploration, geological mapping, environmental and engineering investigation, etc. Currently, the imaging and 1D inversions are the mainstream means for AEM interpretation as the amount of AEM data is huge and 2D and 3D inversions are not efficient. In this paper, we propose a 2D fast imaging method for frequency-domain AEM data based on U-net network. The U-net is a symmetric full-convolution neural network, in which the partial pooling operation between the convolution layers is replaced by the up-sampling operation, while the target location is achieved by skipping connection. This method does not need to consider the complex coupling between the EM responses and underground structures, but instead it establishes a mapping relationship between EM responses and the resistivity model and can quickly achieve accurate imaging of AEM data. We use this network to image both synthetic and field survey data and compare the results with the traditional inversion algorithms. The results show that the U-net imaging have high resolution at high speed that provides a new way for interpreting large amounts 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.

AEM in Norway: A Review of the Coverage, Applications and the State of Technology

From the first use of airborne electromagnetic (AEM) systems for remote sensing in the 1950s, AEM data acquisition, processing and inversion technology have rapidly developed. Once used extensively for mineral exploration in its early days, the technology is increasingly being applied in other industries alongside ground-based investigation techniques. This paper reviews the application of onshore AEM in Norway over the past decades. Norway’s rugged terrain and complex post-glacial sedimentary geology have contributed to the later adoption of AEM for widespread mapping compared to neighbouring Nordic countries. We illustrate AEM’s utility by using two detailed case studies, including time-domain and frequency domain AEM. In both cases, we combine AEM with other geophysical, geological and geotechnical drillings to enhance interpretation, including machine learning methods. The end results included bedrock surfaces predicted with an accuracy of 25% of depth, identification of hazardous quick clay deposits, and sedimentary basin mapping. These case studies illustrate that although today’s AEM systems do not have the resolution required for late-phase, detailed engineering design, AEM is a valuable tool for early-phase site investigations. Intrusive, ground-based methods are slower and more expensive, but when they are used to complement the weaknesses of AEM data, site investigations can become more efficient. With new developments of drone-borne (UAV) systems and increasing investment in AEM surveys, we see the potential for continued global adoption of this technology.

Stratigraphic identification with airborne electromagnetic methods at the Hanford Site, Washington

Stratigraphic units can influence the fate and transport of subsurface contaminants within groundwater. Units having coarse-grained sediments act as preferential flow pathways, and therefore can accelerate the transport of contaminants to reach human and ecological receptors. At legacy waste sites, detailed knowledge of subsurface stratigraphy can be used for effective monitoring and remediation planning to help minimize risk to human health and the environment. Airborne electromagnetic (AEM) methods can non-invasively provide information on kilometer-scale or larger subsurface stratigraphic features and fill informational gaps in directly sampled data from sparsely located boreholes. In this paper, we present inversion results of a 412 line-km frequency-domain AEM survey to delineate subsurface stratigraphic features at the Hanford Site, located in southeastern Washington State. The inversion was performed using a massively parallel 3D electromagnetic modeling and inversion code, where the modeling is based on solving frequency-domain Maxwell's equations using an unstructured-mesh finite-element method and the inversion employs a Gauss-Newton optimization scheme. The results are compared to an underlying geologic framework model (GFM), built by interpolating contact depths of stratigraphic units interpreted from site borehole datasets. In areas with good borehole coverage, the inversion results show a good match with the GFM to a depth of about 60 m. Outside of these areas, the inversion results exhibit inconsistencies from the assumptions made to create the GFM, demonstrating that the AEM survey results can be used to improve the understanding of the geological conceptual model.

Sparse-Promoting 3-D Airborne Electromagnetic Inversion Based on Shearlet Transform

The conventional, L2-norm-based, regularization term in electromagnetic (EM) inversions implements smooth constraints on model complexity in the space domain, which can smoothen the boundaries of complex underground structures. To improve the resolution of 3-D frequency-domain airborne EM (AEM) inversions, we propose a new algorithm for sparse-regularized inversion based on the shearlet transform. Unlike traditional methods that invert the model parameters in the space domain, we first transform the 3-D resistivity model into the frequency domain and then invert the sparse coefficients using an L1-norm measure to ensure the sparseness of the solution. Finally, we transform the shearlet coefficients back to the space domain to update the model. The shearlet transform has inherent multiscale and multidirectional properties, making it capable of effectively extracting complex geometries such as curved boundaries. We adopt the finite-difference method and the iteratively reweighted least-squares scheme for our 3-D AEM modeling and inversions and apply the “moving footprint” technique to speed up the inversion. Tests using synthetic data show that sparse-regularized inversion based on the shearlet transform can obtain more-focused inversion results than conventional smoothness-constrained inversions based on the L2-norm. Tests using field survey data also reveal that the new method can achieve more realistic underground structures.

Quantifying model structural uncertainty using airborne electromagnetic data

SUMMARY The ability to quantify structural uncertainty in geological models that incorporate geophysical data is affected by two primary sources of uncertainty: geophysical parameter uncertainty and uncertainty in the relationship between geophysical parameters and geological properties of interest. Here, we introduce an open-source, trans-dimensional Bayesian Markov chain Monte Carlo (McMC) algorithm GeoBIPy—Geophysical Bayesian Inference in Python—for robust uncertainty analysis of time-domain or frequency-domain airborne electromagnetic (AEM) data. The McMC algorithm provides a robust assessment of geophysical parameter uncertainty using a trans-dimensional approach that lets the AEM data inform the level of model complexity necessary by allowing the number of model layers itself to be an unknown parameter. Additional components of the Bayesian algorithm allow the user to solve for parameters such as data errors or corrections to the measured instrument height above ground. Probability distributions for a user-specified number of lithologic classes are developed through posterior clustering of McMC-derived resistivity models. Estimates of geological model structural uncertainty are thus obtained through the joint probability of geophysical parameter uncertainty and the uncertainty in the definition of each class. Examples of the implementation of this algorithm are presented for both time-domain and frequency-domain AEM data acquired in Nebraska, USA.

The frequency-domain airborne electromagnetic method with a grounded electrical source

For surveys in areas with difficult access, ground-based electromagnetic (EM) systems are difficult to operate, time-consuming, and expensive, whereas airborne EM (AEM) systems are unsafe and uneconomic for small survey areas. We have developed a new frequency-domain airborne electromagnetic (FAEM) system called the ground-airborne frequency-domain EM (GAFEM) system to achieve rapid data acquisition in complex terrains with high safety and low cost. The system consists of a grounded electrical source and an airborne receiver carried by an unmanned rotorcraft. The grounded electrical source transmits a [Formula: see text] pseudorandom series obtaining signals at [Formula: see text] frequencies in one flight, which can maintain the resolution without reducing efficiency. The airborne receiver based on the unmanned rotorcraft makes the system flexible, convenient, and economical to operate in areas with difficult access. The system can conduct surveys in far- and non-far-field regions and can obtain the apparent resistivity to present a quick overview of the subsurface structure. To demonstrate the GAFEM system’s effectiveness and practical detection potential, we have performed two field surveys that revealed the shallow structure with tunnels and the deep structure at source-receiver distances of 3 and 6 km, respectively. The GAFEM system effectively revealed the apparent-resistivity difference with a sufficient investigation depth at large source-receiver distances. Thus, the system can be extended to various important applications for rapid surveys or deep investigations in areas with difficult access.

Quantifying Geophysical Inversion Uncertainty Using Airborne Frequency Domain Electromagnetic Data—Applied at the Province of Zeeland, the Netherlands

AbstractAn accurate understanding of the fresh‐saline distribution of groundwater is necessary for effective groundwater management. Airborne electromagnetic (AEM) surveys offer a rapid and cost‐effective method with which to map this, offering valuable additional information about the subsurface. To convert AEM data into electric conductivity and ultimately groundwater salinity, an inversion is undertaken. A number of algorithms are available for this purpose; however, these are affected by significant uncertainty, owing to inherent nonunique characteristics of this process. The most commonly used inversion codes in hydrogeophysical studies were quantitatively tested using frequency domain AEM and ground data from the province of Zeeland, the Netherlands. These include UBC1DFM code and quasi‐2D laterally constrained inversions. Following an investigation of inversion parameter settings, data were inverted for four inversion methods and interpolated into 3‐D volumes. Using geological data and empirical electrical conductivity and water salinity relationships, each inversion was converted into groundwater electrical conductivity and split into fresh‐brackish‐saline regions. For groundwater volume estimates out of a total volume of 2.8 billion m3, a fresh groundwater estimate could differ by as much as 178 million m3, depending on the inversion used. The primary factor here was the choice of model smoothness, which was shown to affect the thickness of the brackish interval. Fresh‐brackish‐saline interfaces were consistently mapped with an accuracy of ~3 m, the brackish being the most accurately resolved. The few layer method was less successful at resolving smoothly varying salinity distributions but more successful at mapping the brackish interface at greater depth.

2.5D forward modeling and inversion of frequency-domain airborne electromagnetic data

Frequency-domain airborne electromagnetics is a proven geophysical exploration method. Presently, the interpretation is mainly based on resistivity—depth imaging and one-dimensional layered inversion; nevertheless, it is difficult to obtain satisfactory results for two- or three-dimensional complex earth structures using 1D methods. 3D forward modeling and inversion can be used but are hampered by computational limitations because of the large number of data. Thus, we developed a 2.5D frequency-domain airborne electromagnetic forward modeling and inversion algorithm. To eliminate the source singularities in the numerical simulations, we split the fields into primary and secondary fields. The primary fields are calculated using homogeneous or layered models with analytical solutions, and the secondary (scattered) fields are solved by the finite-element method. The linear system of equations is solved by using the large-scale sparse matrix parallel direct solver, which greatly improves the computational efficiency. The inversion algorithm was based on damping least-squares and singular value decomposition and combined the pseudo forward modeling and reciprocity principle to compute the Jacobian matrix. Synthetic and field data were used to test the effectiveness of the proposed method.

2.5D Inversion Algorithm of Frequency-Domain Airborne Electromagnetics with Topography

We presented a 2.5D inversion algorithm with topography for frequency-domain airborne electromagnetic data. The forward modeling is based on edge finite element method and uses the irregular hexahedron to adapt the topography. The electric and magnetic fields are split into primary (background) and secondary (scattered) field to eliminate the source singularity. For the multisources of frequency-domain airborne electromagnetic method, we use the large-scale sparse matrix parallel shared memory direct solver PARDISO to solve the linear system of equations efficiently. The inversion algorithm is based on Gauss-Newton method, which has the efficient convergence rate. The Jacobian matrix is calculated by “adjoint forward modelling” efficiently. The synthetic inversion examples indicated that our proposed method is correct and effective. Furthermore, ignoring the topography effect can lead to incorrect results and interpretations.

Footprint for frequency-domain airborne electromagnetic systems

An airborne electromagnetic (AEM) survey often covers hundreds of square kilometers. Huge amounts of survey data make 2D/3D data inversion very difficult. However, due to the compact configurations of AEM systems, the sensitive area for each single survey station is much smaller than the whole survey area, which makes it possible to only invert partial survey data. The sensitive area is called the footprint. Based on “moving-footprint” technology, the entire survey can be divided into subareas and the data are first inverted individually and then combined to form the inversions of the entire survey area, so that the cost for forward and inverse modeling can be vastly reduced. Contrary to previous electromagnetic (EM) footprints defined only for an EM transmitter or for a perfectly conductive earth, we defined the frequency-domain AEM footprint by considering a complete AEM transmitter-receiver system over an earth with limited conductivity. We used the tensor Green’s function to calculate the secondary magnetic field from the induced underground current and evaluate the EM footprint as the volume in which the induced current contributes 90% to the total secondary magnetic field at the EM receiver. Numerical experiments for horizontal coplanar and vertical coaxial coil configurations revealed that among all influence factors on the AEM footprint, the flight altitude was dominant, with a high flight altitude corresponding to a large EM footprint, whereas the transmitting frequency and earth resistivity played a secondary role and in a combined way of induction number, with the low frequency or high earth resistivity (small induction number) corresponding to large EM footprints.

The bedrock electrical conductivity map of the UK

Airborne electromagnetic (AEM) surveys, when regionally extensive, may sample a wide-range of geological formations. The majority of AEM surveys can provide estimates of apparent (half-space) conductivity and such derived data provide a mapping capability. Depth discrimination of the geophysical mapping information is controlled by the bandwidth of each particular system. The objective of this study is to assess the geological information contained in accumulated frequency-domain AEM survey data from the UK where existing geological mapping can be considered well-established. The methodology adopted involves a simple GIS-based, spatial join of AEM and geological databases. A lithology-based classification of bedrock is used to provide an inherent association with the petrophysical rock parameters controlling bulk conductivity. At a scale of 1:625k, the UK digital bedrock geological lexicon comprises just 86 lithological classifications compared with 244 standard lithostratigraphic assignments. The lowest common AEM survey frequency of 3 kHz is found to provide an 87% coverage (by area) of the UK formations. The conductivities of the unsampled classes have been assigned on the basis of inherent lithological associations between formations. The statistical analysis conducted uses over 8 M conductivity estimates and provides a new UK national scale digital map of near-surface bedrock conductivity. The new baseline map, formed from central moments of the statistical distributions, allows assessments/interpretations of data exhibiting departures from the norm. The digital conductivity map developed here is believed to be the first such UK geophysical map compilation for over 75 years. The methodology described can also be applied to many existing AEM data sets.

3D inversion of entire airborne electromagnetic surveys using a moving footprint

We show that it is practical to invert entire airborne electromagnetic (AEM) surveys to 3D conductivity models with hundreds of thousands of cells within a day on a workstation. We have exploited the fact that the area of the footprint of an AEM system is significantly smaller than the area of an AEM survey to develop a robust 3D inversion method which uses a moving footprint. Our implementation is based on the 3D integral equation method for computing AEM data and sensitivities, and the re-weighted regularized conjugate gradient method is used to minimize the objective functional. Even for terranes which are arguably as close to 1D as geologically possible, we demonstrate that results from our 3D inversion are a significant improvement over those models obtained from layered earth inversion. We demonstrate this with 3D inversion of RESOLVE frequency-domain AEM data acquired for salinity mapping over the Bookpurnong Irrigation District in South Australia.

Constrained inversion of AEM data for mapping of bathymetry, seabed sediments and aquifers

A shallow (depth < 20 m) layer of water, fresh, brackish or saline, covers millions of square kilometres of sediments and bedrock along the world?s coastlines, rivers, lakes, and lagoons. These geological units are extremely important, both environmentally and economically. Airborne electromagnetic (AEM) has been used with some success to obtain the bathymetry of shallow surface water ((Macnae et al., 2004; Vrbancich et al., 2000; Vrbancich et al., 2005). Some attempts have also been made to retrieve information about the sediments under the water bottom (Vrbancich et al., 2000 ). The limited research that has been carried out so far tells us that we need to make improvements and further developments, both in hardware, as well as in data processing and modelling. This manuscript aims at making a contribution at the data inversion level, by applying constrained inversion methodology to different AEM datasets flown over water. In constrained inversion, adjacent model parameters are regularized through lateral constraints that allow information to flow from soundings that contain more information to those that contain less. We present results from constrained inversion (smooth and few layers) of a portion of SkyTEM survey (a helicopter borne time domain AEM system) flown in Denmark, and of a RESOLVE survey ( a helicopter borne frequency domain AEM system) carried out along the Murray River in Australia. In both cases bird height was included as an inversion parameter, allowing the inversion to compensate for errors in laser altimeter readings over water.

An example of holistic inversion of time domain AEM data

An inversion algorithm for time-domain airborne electromagnetic (AEM) data has been developed. A complete flight line of data are inverted in each inversion to simultaneously recover a 2D layered earth conductivity distribution and selected unmeasured elements of the AEM system geometry. The conductivity and thickness of each layer in the earth model and the system geometry, are all parameterised by separate along line 1D cubic B-splines. It is the coefficients of these splines that become the unknowns that we solve for in the inversion. This new method is motivated by the success of three separate concepts: the ?holistic approach? to the inversion and calibration of frequency-domain AEM data (Brodie and Sambridge, 2006); laterally constrained inversion (Auken et al., 2005; Vallée and Smith, 2008); and simultaneous estimation of subsurface conductivity and system geometry parameters (Lane et al., 2004; Sattel et al., 2004). The method could be described as a form of laterally constrained inversion with a spline model parameterisation and the additional capability of solving for system geometry.

The JAC airborne EM system: AEM-05

This paper describes the airborne electromagnetic (AEM) system operated by the Joint Airborne geoscience Capability (JAC), a partnership between the Finnish and British Geological Surveys. The system is a component of a 3-in-1, fixed-wing facility acquiring magnetic gradiometer and full spectrum radiometric data alongside the wing-tip, frequency-domain AEM measurements. The AEM system has recently (2005) been upgraded from 2 to 4 frequencies and now provides a bandwidth from 900 Hz to 25 kHz. The fixed-wing configuration of 4 dual vertical coplanar coils, offers a high signal/noise by virtue of the wingspan separation of the sensors. This unique configuration allows 3-in-1 surveys to be successfully performed at a variety of survey elevations when regulatory conditions are imposed. Its deployment on a twin-engine aircraft also permits low altitude surveying in countries, such as the UK, where this is a requirement. The development of the new AEM-05 system has been incremental and its history can be traced back over five decades. The AEM data acquired in the Finnish National Mapping project, and across northern Europe, have been used extensively in mineral exploration. More recent projects have investigated the application of the data to environmental, hydrogeological and land quality issues. These studies have been enhanced by reducing the flight line separation from 200 m (the national high-resolution scale) to 50 m. Our surveys also increasingly involve the application of AEM across populated areas often with extensive infrastructure. Additional secondary instrumentation has been introduced to provide an increased understanding of the data and the AEM responses observed. The secondary systems include an accurate, high sampling rate laser altimeter, a downward-looking digital camera to record the flight path, a 50/60 Hz power line monitor and a GPS gyroscope. The paper is intended as an overview and provides descriptions of the new AEM system, the secondary systems now employed and some of the software used to provide accurate and levelled AEM data. Recent applications of the system are reviewed and the challenging nature of the new subsurface information being revealed is demonstrated.

Sensitivity functions of frequency-domain magnetic dipole-dipole systems

The resolution capabilities of airborne electromagnetic (AEM) frequency-domain systems are traditionally analyzed in terms of the footprint, which provides a simple measure of the lateral extent of the earth volume involved in a given measurement. However, considerably more detailed insight into the system resolution capabilities can be obtained by studying the 3D sensitivity distribution as defined by the Fréchet derivatives. A qualitative analysis of the 3D sensitivity distributions for six typical magnetic dipole-dipole configurations demonstrates that the spatial resolution characteristics differ widely and that the optimal coil configuration for practical investigations depends on the expected target characteristics. For all six coil configurations, the 3D sensitivity distributions reveal significant sign changesdownwards and outwards from the center, stressing the necessity of reliable starting models for successful inversion of frequency-domain AEM data. Likewise, the central zone of sensitivity for the in-phase component is always larger than for the quadrature, indicating an inferior lateral resolution of the former. A new sensitivity footprint is defined, based on the at-surface behavior of the sensitivity distribution, simply as the area where the sensitivity is at least 10% of its maximum value. For the vertical coaxial (VCA) coil configuration, the size of the sensitivity footprint in the [Formula: see text]-direction (perpendicular to the flight path) is approximately a factor of two larger than in the [Formula: see text]-direction (along the flight path), while there is virtually no difference for the horizontal coplanar (HCP) coil configuration. The ratio of the HCP to VCA sensitivity footprint exceeds one in both [Formula: see text]- and [Formula: see text]-directions, suggesting that the VCA coil configuration has the best lateral resolution.