Frequency-domain Airborne Electromagnetic Data 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.

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.

Interpretation of Frequency-Domain Airborne Electromagnetic Data Based on the Deep Neural Network Incorporating Topographic Information

Deep neural networks (DNNs) have recently been used to interpret frequency-domain electromagnetic (EM) data, therefore vast amounts of information can be rapidly interpreted. However, in airborne surveys that include mountainous regions, severe topographic changes distort the EM data and they can lead to unreasonable interpretations. DNN-based inversions that reduce the effects of topographic changes have not yet been proposed whereas some conventional inversion techniques can correct topographic distortions. Since DNN-based interpretation needs various training dataset and its performance depends on the characteristics of training dataset, it is important to generate various AEM data and resistivity model pairs. However, it is almost impossible to derive the EM responses of two-dimensional (2D) or 3D resistivity models incorporating diverse topographic patterns because such models require many variables and computation costs are limited. Therefore, we suggested the pseudo 1D interpretation of EM data which can consider the topographic changes using EM responses and topographic information at three neighboring data points. We include topographic information in DNN training as slope angle between the data points. Compared to conventional inversion, our trained DNN model recovers the synthetic resistivity models more reasonably. In addition, we used the trained DNN model to evaluate a real AEM dataset from south-central Alaska. The trained DNN provided reasonable interpretation results despite that the data were acquired at mountainous area with rough topography. Since the trained DNN model can provide predictions rapidly, the interpretation time was drastically reduced. We believe that this research can increase the practical field applicability of AEM survey.

1-D inversion of frequency-domain airborne electromagnetic data using the open-source aempy toolbox

Over the past decade, Geological Survey Ireland has acquired, and continues to acquire, frequency and time-domain airborne electromagnetic data as part of the Tellus project. While the project advances towards national coverage, we have developed an open-source and freely available pre-inversion processing and one-dimensional inverse modelling toolbox, aempy, for numerical modelling of the existing and future electromagnetic data sets. aempy is a flexible tool facilitating the quantitative interpretation and optimal use of the data sets by the broader scientific community, supporting the ethos and intent in the free public release of the Tellus airborne data sets. Coded in Python, the toolbox is freely and readily adaptable for specific educational and research objectives and for application to data acquired using other electromagnetic systems. The inversion algorithms in the toolbox are currently implemented in 1-D, assuming that the Earth is horizontally stratified and has laterally-constant layer model parameters, e.g., conductivity and thickness. In its current state, modules and functions of aempy work on processed frequency-domain airborne electromagnetic data. In this paper, we introduce the concept, structure and capacities of aempy and present model results derived from the use of different filtering and inversion techniques implemented in the toolbox, as well as an analysis of the influence of the flight altitude parameter on resulting subsurface inversion models.

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.

3D sequential inversion of frequency-domain airborne electromagnetic data to determine conductive and magnetic heterogeneities

We have developed two inversion workflows that sequentially invert conductivity and susceptibility models from a frequency-domain controlled-source electromagnetic data set. Both workflows start with conductivity inversion using electromagnetic (EM) kernel and out-of-phase component data, which is mainly sensitive to conductivity, and then we adopt the susceptibility inversion using in-phase component data. The difference between these two workflows is in the susceptibility inversion algorithm: One uses an EM kernel and a conductivity model as the input model; the other uses a magnetostatic kernel and a conductivity model to generate the appropriate input data. Because the appropriate input data for magnetostatic inversion should not contain the EM induction effect, the in-phase induction effect is simulated through the conductivity model obtained by inverting out-of-phase data and subtracting them from observed in-phase data to generate an “induction-subtracted” in-phase data set that becomes input data for magnetostatic inversion. For magnetostatic inversion, we used a linear magnetostatic kernel to enable rapid computation. Then, we applied the two inversion workflows to a field data set of a DIGHEM survey, and we successfully reconstructed the conductivity and susceptibility models from each workflow using two zones within the data sets, in which conductive and susceptible anomalies were present. One important finding is that the susceptibility inversion results obtained from two different workflows are very similar to each other. However, computational time can be significantly saved with linear magnetostatic inversion. We found out how the results of the conductivity and susceptibility models could be well-imaged using a sequential inversion workflow and also how magnetostatic inversion could be used efficiently for airborne EM data inversion.

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.

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.

Holistic inversion of frequency-domain airborne electromagnetic data with minimal prior information

The holistic inversion approach for frequency domain airborne electromagnetic data has previously been employed to simultaneously calibrate, process and invert raw frequency-domain data where prior information was available. An alternative formulation has been developed, which is suitable in the case where explicit prior information is not available. It incorporates: a multi-layer vertically-smooth conductivity model; a simplified bias parameterisation; horizontal smoothing with respect to elevation; and cluster computer parallelisation. Without using any prior data, an inversion of 8.0 million data for 3.4 million parameters yields results that are consistent with independently derived calibration parameters, downhole logs and groundwater elevation data. We conclude that the success of the holistic inversion method is not dependent on a sophisticated conceptual model or the direct inclusion of survey-area specific prior information. In addition, acquisition costs could potentially be reduced by employing the holistic approach which largely eliminates the need for high altitude zero-level measurements.

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.

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.

A holistic approach to inversion of time-domain airborne EM

A “holistic” method for simultaneously estimating conductivity and calibration models from 1-D inversion of time-domain airborne electromagnetic (AEM) data is proposed. The work extends the concept of holistic inversion that been successfully applied to frequency-domain AEM data. The entire multi-component airborne dataset and available independent conductivity and interface-depth data are simultaneously inverted. A spline-based conductivity model covering the complete survey area is estimated. Unmonitored elements of the system geometry are included as unknown parameters of the calibration model and are solved for in the inversion.Conventional 1-D inversion methods invert each airborne sample in isolation from other samples. However, by simultaneously considering all of the available information together in a holistic inversion formulation, we are able to exploit the inter-component and spatial coherency characteristics of the airborne data. The formulation ensures that the conductivity and calibration models are optimal with respect to the airborne data and prior information.

A holistic approach to inversion of frequency-domain airborne EM data

We have developed a holistic method for simultaneously calibrating, processing, and inverting frequency-domain airborne electromagnetic data. A spline-based, 3D, layered conductivity model covering the complete survey area was recovered through inversion of the entire raw airborne data set and available independent conductivity and interface-depth data. The holistic inversion formulation includes a mathematical model to account for systematic calibration errors such as incorrect gain and zero-level drift. By taking these elements into account in the inversion, the need to preprocess the airborne data prior to inversion is eliminated. Conventional processing schemes involve the sequential application of a number of calibration corrections, with data from each frequency treated separately. This is followed by inversion of each multifrequency sample in isolation from other samples.By simultaneously considering all of the available information in a holistic inversion, we are able to exploit interfrequency and spatial-coherency characteristics of the data. The formulation ensures that the conductivity and calibration models are optimal with respect to the airborne data and prior information. Introduction of interfrequency inconsistency and multistage error propagation stemming from the sequential nature of conventional processing schemes is also avoided. We confirm that accurate conductivity and calibration parameter values are recovered from holistic inversion of synthetic data sets. We demonstrate that the results from holistic inversion of raw survey data are superior to the output of conventional 1D inversion of final processed data. In addition to the technical benefits, we expect that holistic inversion will reduce costs by avoiding the expensive calibration-processing-recalibration paradigm. Furthermore, savings may also be made because specific high-altitude zero-level observations, needed for conventional processing, may not be required.

Comparison of methods for estimating earth resistivity from airborne electromagnetic measurements

Earth resistivity estimates from frequency domain airborne electromagnetic data can vary over more than two orders of magnitude depending on the half-space estimation method used. Lookup tables are fast methods for estimating half-space resistivities, and can be based on in-phase and quadrature measurements for a specified frequency, or on quadrature and sensor height. Inverse methods are slower, but allow sensor height to be incorporated more directly. Extreme topographic relief can affect estimates from each of the methods, particularly if the portion of the line over the topographic feature is not at a constant height above ground level. Quadrature–sensor height lookup table estimates are generally too low over narrow valleys. The other methods are also affected, but behave less predictably. Over very good conductors, quadrature–sensor height tables can yield resistivity estimates that are too high. In-phase–quadrature tables and inverse methods yield resistivity estimates that are too high when the earth has high magnetic susceptibility, whereas quadrature–sensor height methods are unaffected. However, it is possible to incorporate magnetic susceptibility into the in-phase–quadrature lookup table. In-phase–quadrature lookup tables can give different results according to whether the tables are ordered according to the in-phase component or the quadrature component. The rules for handling negative in-phase measurements are particularly critical. Although resistivity maps produced from the different methods tend to be similar, details can vary considerably, calling into question the ability to make detailed interpretations based on half-space models.