Airborne Time-domain Electromagnetic Data Research Articles

A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.

Three-Dimensional Inversion of Induced Polarization Effects in Airborne Time Domain Electromagnetic Data Using the GEMTIP Model

This paper discusses the physical and mathematical principles of the airborne induced polarization (IP) method. The possibility of extracting information about the IP properties of rocks from airborne survey data has become a subject of active research recently. We introduce a method for the joint inversion of the airborne EM data into the electrical conductivity and IP parameters based on the generalized effective-medium theory of induced polarization (GEMTIP). We also present the results of the inversion of the airborne EM data collected over the Echum Project Area, in Northwestern Ontario, Canada, into 3D conductivity and chargeability models. Obtaining IP physical property models from an airborne geophysical survey may result in a paradigm change in mineral exploration by pulling more information and value from airborne EM surveys.

Three-dimensional inversion of airborne time-domain electromagnetic data for ground sources

While most airborne time-domain electromagnetic (ATEM) surveys are carried out using moving-source systems, semi-airborne systems that use a ground-based source, such as GREATEM, have gained popularity in recent years because they allow for more in-depth exploration than moving-source systems. We presented a three-dimensional inversion method for interpreting transient data collected in semi-airborne surveys. Our method is based on a Gauss–Newton minimization approach. The forward problem is solved in the frequency domain using a secondary-field finite-difference technique, and the resulting solution and sensitivity are Fourier-transformed to the time domain using a digital filter. The sensitivities are evaluated by the adjoint-equation method, except for those of a surface region immediately below the source, which are derived by forward modeling the response of a perturbed model based on finite differences. We tested our inversion method on synthetic and real data. The synthetic tests show that the resolution capability of the semi-airborne surveys is dependent on the location of the ground source, which suggests that it is desirable to employ multiple source locations and perform joint inversion of all data sets to ensure the reliability of semi-airborne surveys. Finally, the real data example demonstrates that the recovered conductive zone is consistent with the known distribution of the mineralized zone.

Flexible quasi-2D inversion of time-domain AEM data, using a wavelet-based complexity measure

SUMMARYRegularization methods improve the stability of ill-posed inverse problems by introducing some a priori characteristics for the solution such as smoothness or sharpness. In this contribution, we propose a multidimensional scale-dependent wavelet-based ℓ1-regularization term to cure the ill-posedness of the airborne (time-domain) electromagnetic induction inverse problem. The regularization term is flexible, as it can recover blocky, smooth and tunable in-between inversion models, based on a suitable wavelet basis function. For each orientation, a different wavelet basis function can be used, introducing an additional relative regularization parameter. We propose a calibration method to determine (an educated initial guess for) this relative regularization parameter, which reduces the need to optimize for this parameter and, consequently, the overall computation time is under control. We apply our novel scheme to a time-domain airborne electromagnetic data set in Belgian saltwater intrusion context, but the scheme could equally apply to any other 2D or 3D geophysical inverse problem.

Integrating neural networks in least-squares inversion of airborne time-domain electromagnetic data

Airborne time-domain electromagnetic surveys produce extremely large data sets with thousands of line kilometers of data and millions of possible models to explain the data. Inversion of such data sets to obtain the resistivity structures of the subsurface is computationally intensive and involves calculation of a significant number of forward and derivative responses for solving the least-squares inverse problem. The flight altitude of the airborne system needs to be included in the modeling, which adds further complexity. We propose to integrate neural networks in a damped iterative least-squares inversion framework to expedite the inversion process. We train two separate neural networks to predict the forward responses and partial derivatives independently for a broad range of resistivity structures and flight altitudes. Data inversion is not only used for producing the final subsurface models but also used during data processing, or to produce intermediate results during a survey. With these purposes in mind, we provide three inversion schemes with a tunable balance between computational time and modeling accuracy: (1) numerical forward responses used initially in combination with neural network derivatives, and the derivatives switched to a numerical solution in final iterations, (2) numerical forward responses in combination with neural network derivatives used throughout the inversion, and (3) only neural network forward responses and derivatives used in inversion. Experiments on field data find that we improve inversion speed without any loss in modeling accuracy with our first approach, whereas the second scheme gives a significant speedup at the cost of minor and often acceptable deviations in the inversion results from the conventional nonlinear inversion. The last approach is the fastest and captures the overall resistivity structures quite well. Therefore, depending on the modeling accuracy, inversion speedup factors of up to 50 are realized by using the proposed schemes.

Geometric 3-D inversion of airborne time-domain electromagnetic data with applications to kimberlite pipes prospecting in a complex medium

We consider an approach to solving 3-D inverse problems of airborne time-domain electromagnetic (TD-EM) survey in complex geoelectrical conditions, when the responses of target objects in the measured signal are relatively small and mixed with responses of rugged topography, 3-D inhomogeneities in overburden layers, and other interfering 3-D objects. For parameterization of the geoelectrical model of the geological medium, global and local structures are used. A block structure is a grid with large cells whose boundaries can move during the inversion process. Block structures are characterized by two types of parameters: geometric and physical. Geometric parameters include the coordinates of the boundaries of blocks in block structures, which are used for recovering local bodies or 3-D inhomogeneities within the layers, and physical parameters are the conductivities inside the blocks. In addition, the geometric parameters include displacements of control points, which are used for describing curved surfaces. The functional employed for inversion includes regularizing terms that allow controlling the self-crossing of geometric boundaries and extension of physical parameters beyond the boundaries of the allowed values range. Regularization coefficients are calculated adaptively at each iteration of the nonlinear 3-D inversion. In order to separate the responses of 3-D inhomogeneities located under the overburdens from those of 3-D inhomogeneities inside the overburdens, we propose to carry out 3-D inversion in two stages. At the first stage, over a shortened time range, we recover the conductivity distribution using global block structures and the shape of the boundaries of the overburden layers using control points. At the second stage, the entire time range is used. In those places where the significant residuals remained at late times, local block structures are employed to recover local objects (including target ones) under the overburden layers. This enables us to define quite accurately the position in plan, top border depth, and type of 3-D inhomogeneities, which are located under the overburdens, at the second stage of geometric inversion. To calculate 3-D time-domain electromagnetic fields, we use the vector finite element method implemented on optimized non-conforming hexahedral meshes with the possibility of adjoining the faces of several finite elements to the face of one finite element. The workability of the proposed approaches is demonstrated on synthetic airborne TD-EM data as well as on experimental airborne data, which are obtained as a result of methodical work over the kimberlite pipe deposit. The proposed geoelectrical model parameterization and the approach to 3-D inversion allow the discovery of relatively small 3-D target objects, when many non-target objects, including 3-D inhomogeneities in the overburdens, give several times greater responses in the measured signals.

A novel trans-dimensional Bayesian inversion strategy for airborne time-domain electromagnetic data

The trans-dimensional Bayesian inversion method based on statistical theory regards the inversion parameters as random variable, it not only provides a reasonable inverse model based on the probability, but also offers the probability distribution and the uncertainty information of the inverse model parameters. However, for the conventional trans-dimensional Bayesian inversion, the model sampling efficiency and inversion convergence rate are influenced by several factors, and it requires sampling over a big model space, those limit the application of the trans-dimensional Bayesian inversion for airborne time-domain electromagnetic data. In this paper, we present a novel trans-dimensional Bayesian inversion strategy for airborne time-domain electromagnetic data, which uses a combining sampling update method to implement the model sampling. This method uses the block-wise updating method to run only one Markov chain with the initial state constructed by conductivity-depth imaging during the burn-in period, and adopts the component-wise updating method with an adaptive sampling step size to perform multiple Markov chains in parallel after the burn-in period ends. The effectiveness of the novel Bayesian inversion strategy was validated by both synthetic data and survey data. The experimental results showed that this inversion strategy can not only obtain better inversion results, but also shorten the burn-in period and reduce the total sampling times of the Markov chain.

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.

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

Structural mapping and gold mineralisation potential evaluation from airborne time – domain electromagnetic (TDEM) data of Ilesha Schist Belt, southwestern Nigeria

The airborne Time-domain electromagnetic data acquired over the Ilesha Schist Belt in southwestern Nigeria were filtered, enhanced and analysed to generate ground conductivity responses at different time gates and employed to characterise the subsurface in terms of rock distribution, structural framework as well as evaluate the gold and base metal mineralisation potential of the region. The near-surface sensitive early time gate conductivity responses (EMZ1 – EMZ3) indicate relatively high subsurface conductivity distribution (60–120 mS/m) in amphibole schist and pegmatite terrain in the west while medium to low-conductivity (<5 mS/m) values were recorded in the east underlain by granitic rocks. Major and minor linear/sub-linear conductive features having conductivity in excess of 2 mS/m and trending NE-SW, NNE-SSW, NNW-SSE and ENE-WSW directions were delineated on the mid conductivity time gate responses (EMZ3 – EMZ5). One of which presents geometry and pattern consistent with the well-reported Ifewara – Iwaraja fault system that runs through the entire Ilesha Schist Belt. Very high anomalous conductivity (160–300 mS/m) responses at late conductivity time gates (EMZ10 – EMZ13) with characteristic long decay curves were obtained over isolated zones, especially along fractures and veins suggesting the occurrence of structurally controlled gold and base metal mineralisation.

Three-Dimensional Anisotropic Inversions for Time-Domain Airborne Electromagnetic Data

Rocks and ores in nature usually appear macro-anisotropic, especially in sedimentary areas with strong layering. This anisotropy will lead to false interpretation of electromagnetic (EM) data when inverted under the assumption of an isotropic earth. However, the time-domain (TD) airborne EM (AEM) inversion for an anisotropic model has not attracted much attention. To get reasonable inversion results from TD AEM data, we present in this paper the forward modeling and inversion methods based on a triaxial anisotropic model. We apply three-dimensional (3D) finite-difference on the secondary scattered electric field equation to calculate the frequency-domain (FD) EM responses, then we use the inverse Fourier transform and waveform convolution to obtain TD responses. For the regularized inversion, we calculate directly the sensitivities with respect to three diagonal conductivities and then use the Gauss–Newton (GN) optimization scheme to recover model parameters. To speed up the computation and to reduce the memory requirement, we adopt the moving footprint concept and separate the whole model into a series of small sub-models for the inversion. Finally, we compare our anisotropic inversion scheme with the isotropic one using both synthetic and field data. Numerical experiments show that the anisotropic inversion has inherent advantages over the isotropic ones, we can get more reasonable results for the anisotropic earth structures.

The effect of power lines on time-domain airborne electromagnetic data

The acquisition of airborne electromagnetic (AEM) data colocated with water well data provides an essential data set for constructing an accurate rock-physics relationship between resistivity from AEM and lithology from wells. Results from an AEM survey in Butte and Glenn Counties, California, USA, where 41 water wells are located within 100 m of the AEM flight lines, provide a unique opportunity to investigate the effect of water wells on AEM data. Based on systematic numerical analyses and examination of the field data, we find that the only noise source related to the wells that affects the AEM signals in our study area is the power lines; the effect of the steel casings in the study area is negligible. Our numerical simulations yield two important findings: (1) the effect of the power line is mainly dependent upon the resistivity of the ground pathway between the base of the power-line poles, and (2) for the resistivity structure in the study area, when the ground contact resistance is sufficiently low, the effect of the power line will be observed in the AEM data until there is a separation distance of 174 m from the power line; the total power-line percentage effect less than 3% has been considered to be negligible. However, we find that, in the field data, an average separation distance between the power line and high-quality AEM data (i.e., no impact from the power lines) is only 60% of this predicted distance. We attribute this to the high ground contact resistance caused by degradation of the grounded electrodes located at the bottom of the power-line poles. For our 41 wells, after removing any soundings contaminated by power-line noise, we are left with high-quality soundings located 0–160 m from the water wells, with an average distance of 77 m.

An adaptive reversible jump MCMC inversion algorithm for airborne time-domain electromagnetic data

The reversible jump Markov Chain Monte Carlo algorithm is a trans-dimensional Bayesian inversion method, which can not only obtain the best inversion solution, but also provide the uncertainty information of inversion parameters, so as to effectively evaluate the reliability of inversion results. However, in trans-dimensional Bayesian inversion, the step size of model sampling has a great influence on the efficiency and precision of inversion. To solve the selection problem of sampling step size, this paper presents an adaptive reversible jump MCMC inversion algorithm for airborne time-domain electromagnetic data. In this method, the sampling step length is automatically adjusted in the sampling process through the data fitting error of each sampling model, so as to avoid the influence of unreasonable sampling step size and improve the acceptance rate of model sampling. The validity of the proposed method is verified by inversion test on the synthetic data and compared with the regularized inversion results. The inversion results demonstrate that the adaptive reversible jump MCMC inversion algorithm can improve the acceptance rate of model sampling and the accuracy of inversion results.

Inversion of time-domain airborne EM data with IP effect based on Pearson correlation constraints

Due to the induced polarization (IP) effect, the sign reversal often occurs in time-domain airborne electromagnetic (AEM) data. The inversions that do not consider IP effect cannot recover the true umderground electrical structures. In view of the fact that there are many parameters of airborne induced polarization data in time domain, and the sensitivity difference between parameters is large, which brings challenges to the stability and accuracy of the inversion. In this paper, we propose an inversion mehtod for time-domain AEM data with IP effect based on the Pearson correlation constraints. This method uses the Pearson correlation coefficient in statistics to characterize the correlation between the resistivity and the chargeability and constructs the Pearson correlation constraints for inverting the objective function to reduce the non uniqueness of inversion. To verify the effectiveness of this method, we perform both Occam’s inversion and Pearson correlation constrained inversion on the synthetic data. The experiments show that the Pearson correlation constrained inverison is more accurate and stable than the Occam’s inversion. Finally, we carried out the inversion to a survey dataset with and without IP effect. The results show that the data misfit and the continuity of the inverted section are greatly improved when the IP effect is considered.

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.

Airborne electromagnetic data levelling based on inequality-constrained polynomial fitting

We have developed a novel technique for airborne time-domain electromagnetic data levelling using inequality-constrained polynomial fitting. In the approach, channel data of a certain survey area are levelled under the constraint of off-time attenuation rule. Based on the correlations between flight lines, Huang has proposed that level errors could be fitted by the differences between the flight line data and its reference line data. Thus, the level errors represented as polynomial functions are determined by the least-squares algorithm subjecting to the constraint of the adjacent channel. In addition, only the survey area data in the absence of anomalous features are used in the levelling process to avoid fake level problem. We validate the levelling method by applying it to synthetic data and field data, comparing with line-to-line correlation levelling.

Improved near-surface resolution in geotechnical applications using very early AEM time gates

ABSTRACTIn a majority of geotechnical projects and in geohazard studies, knowledge of sediment thickness is crucial, along with information about sediment types such as possible occurrence of sensitive clay. We test the recently improved system response (SR) method on data from an airborne electromagnetic (AEM) survey carried out in Norway to support ground investigations for linear infrastructure projects. In geotechnical projects, especially the upper metres are important to resolve. The acquisition systems, calibration and data processing and inversion are continuously improved to increase the sensitivity of the AEM systems. SR is applied in the inversion of time-domain AEM data making it possible to utilise the very earliest time gates, which provide information about the shallower layers, aiming to increase the near-surface resolution of the models. We test this new method on data from a site where comparably small resistivity contrasts (5–10 Ωm embedded in 10–50 Ωm) are crucial to resolve, to successfully identify hazardous quick clay. The AEM field data inverted with the full SR provide models with more pronounced structures in the near surface, better reflecting true structures observed in resistivity borehole measurements. We see the same outcome when conducting synthetic modelling. In the synthetic models where these early gates were included, thinner layers with smaller resistivity contrasts are resolved. This is a promising result considering to further use AEM in projects where high resolution in the near surface is essential.

AEM, Seismic and Power-lines: A hydrogeological case study in Peel, Western Australia

Airborne time domain electromagnetic and high resolution 2D seismic data were acquired in 2018 to map structures of hydrogeological significance in the Peel region; Western Australia. Interpretatio...

Open-source software for simulations and inversions of airborne electromagnetic data

ABSTRACTInversion of airborne electromagnetic data is often an iterative process, not only requiring that the researcher be able to explore the impact of changing components, such as the choice of regularisation functional or model parameterisation, but also often requiring that forward simulations be run and fields and fluxes visualised in order to build an understanding of the physical processes governing what we observe in the data. In the hope of facilitating this exploration and promoting the reproducibility of geophysical simulations and inversions, we have developed the open-source software package SimPEG. The software has been designed to be modular and extensible, with the goal of allowing researchers to interrogate all of the components and to facilitate the exploration of new inversion strategies. We present an overview of the software in its application to airborne electromagnetics and demonstrate its use for visualising fields and fluxes in a forward simulation, as well as its flexibility in formulating and solving the inverse problem. We invert a line of airborne time-domain electromagnetic data over a conductive vertical plate using a 1D voxel inversion, a 2D voxel inversion and a parametric inversion, where all of the forward modelling is done on a 3D grid. The results in this paper can be reproduced using the provided Jupyter notebooks. The Python software can also be modified to allow users to experiment with parameters and explore the physics of the electromagnetics and intricacies of inversion.

A rational Krylov subspace method for 3D modeling of grounded electrical source airborne time-domain electromagnetic data

A rational Krylov subspace method for 3D modeling of grounded electrical source airborne time-domain electromagnetic data