Airborne Time-domain Electromagnetic Research Articles

Inversion for 3D Conductivity and Chargeability Models Using EM Data Acquired by the New Airborne TargetEM System in Ontario, Canada

This paper introduces an original approach to the joint inversion of airborne electromagnetic (EM) data for three-dimensional (3D) conductivity and chargeability models using hybrid finite difference (FD) and integral equation (IE) methods. The inversion produces a 3D model of physical parameters, which includes conductivity, chargeability, time constant, and relaxation coefficients. We present the underlying principles of this approach and an example of a high-resolution inversion of the data acquired by a new active time domain airborne EM system, TargetEM, in Ontario, Canada. The new TargetEM system collects high-quality multicomponent data with low noise, high power, and a small transmitter–receiver offset. This airborne system and the developed advanced inversion methodology represent a new effective method for mineral resource exploration.

Three-dimensional simulation and characterization of airborne transient electromagnetic response in rough media

In fractal, porous, and cranny rough media, the diffusion of the electromagnetic (EM) field differs from that in piecewise smooth media. This phenomenon is known as anomalous diffusion. To study the influence of anomalous diffusion on EM signals, we use the fractional time derivative and derive a governing equation describing the subdiffusion process of electric fields in rough media. Three-dimensional forward modeling of the time-domain airborne EM is performed using the finite-difference method and frequency-time transformation. We verify the accuracy of our forward-modeling method by comparing it with semianalytical solutions for a layered earth model. The numerical results indicate that as the conductivity of rough media decreases with time, the amplitude of the EM field decreases during the early periods but increases during subsequent periods compared with nonrough media. The EM field does not diffuse in the classical mode of the “smoke ring,” but instead demonstrates a “trailing pattern,” meaning that it maintains its maximum value at positions close to the source and diffuses outward and attenuates until it vanishes.

Research on Time-Domain Airborne EM Full-Field Apparent Resistivity Imaging Method for Arbitrary Transmitting Waveform

The time-domain airborne electromagnetic (AEM) system can do fast electromagnetic (EM) surveys over mountainous areas by carrying its detection equipment on an airplane. Due to the dense sampling of the AEM method, it generates a huge amount of data. As a result, resistivity imaging methods have become the first choice for data interpretation because they are fast. However, the traditional imaging methods do not fully consider the influence of the transmitting waveform. When they are used to deal with the AEM data with complex current waveform, the imaging results are seriously affected. Therefore, we develop a universal full-field apparent resistivity imaging method for AEM data with arbitrary transmitting waveform. Firstly, we calculate the convolution of the time derivative of the current waveform and step response to obtain the time-domain AEM response of the arbitrary transmitting waveform. Then the full-field apparent resistivity imaging method based on the inverse function theorem is used to complete the rapid imaging of AEM data with complex waveform. Finally, we apply our imaging code to both synthetic and field data to verify its correctness.

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.

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.

3D Modeling of Time-domain AEM Fields with IP Effect in Complex Media with Topography

We present two approaches to solving the airborne electromagnetic (AEM) problems given the induced polarization (IP) effect: with calculating the field in a polarizable medium directly in the time domain (TD) and with calculating the EM+IP field in the frequency domain (FD) followed by a transition to the time domain. The first approach is based on calculating the field at each time step given the sources that depend on the currents excited in the medium at previous time steps. This approach allows us to use any IP decay functions. The frequency domain approach is based on the Fourier series expansion of a non-stationary source and the Cole-Cole model. In order to reduce the computational cost, we use the Hermite spline interpolation. Both approaches allow modeling EM + IP processes in complex media with topography and the curved boundaries of layers containing 3D heterogeneities. 3D modeling is performed on non-conforming hexahedral meshes generated fully automatically. The analysis of computational efficiency and verification of the developed approaches are presented in comparison with the results from other researchers. Moreover, we compare the results obtained in the time and frequency domains. The results of 3D modeling the IP effects, which are obtained for some geoelectrical models typical for AEM exploration problems, are demonstrated.

3-D Time-Domain Airborne EM Forward Modeling With IP Effect Based on Implicit Difference Discretization of Caputo Operator

As an efficient geophysical exploration method, the time-domain airborne electromagnetic (AEM) data often show sign reversal in late-time channels due to induced polarization (IP) effect. The traditional imaging and inversion methods without considering the IP effect cannot recover the true electrical structure of the earth, so it is necessary to develop 3D EM forward modeling and inversion techniques with IP effect. In this paper, we propose a 3D forward modeling method for time-domain AEM with IP effect based on unstructured finite-element (FE) method. To describe the IP effect of a medium, we introduce the Cole-Cole model and transform it into fractional derivative form using frequency-time conversion. Then, we discretize it using the difference discretization format of Caputo fractional derivative. Finally, we use the vector FE method based on unstructured tetrahedral mesh and unconditionally stable second-order backward Euler’s scheme to discretize Maxwell’s equations in space and time. In this way, we can solve the 3D forward modeling problem for AEM with IP effect in time domain. We verify the accuracy of our algorithm by comparing it with the 1D semi-analytical solution for a half-space model, and then calculate EM responses for typical abnormal models and analyze the characteristics of IP effect.

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.

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.

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.

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.

The Abra sedimentary-hosted Pb-Ag-Cu-Au deposit, Western Australia: A geophysical case study

Abra is a high-grade sedimentary-hosted Pb deposit located in the Paleoproterozoic Edmund Basin in Western Australia. Mineralization is blind, with the top of the deposit occurring 250 m beneath the land surface. The deposit consists of a stratiform apron of Pb-Ag-Ba mineralization in laminated iron-oxide- and barite-altered dolomite and siltstone, which overlies a feeder zone of chlorite-altered, brecciated, and veined carbonatic siltstone that contains Pb-Ag mineralization in the core that transitions to Pb-Cu and Cu-Au at depth. Abra is characterized by discrete geophysical anomaly responses in magnetic, gravity, time-domain electromagnetic (TDEM), and induced polarization survey data. A +450 nT magnetic anomaly is caused by magnetite in the lower stratiform zone. Dense galena, barite, dolomite, and iron-oxide mineralization in the apron and galena in the feeder zone is surrounded by lower-density sedimentary host rocks, which results in a +1 mGal gravity anomaly. Airborne, ground, and downhole TDEM surveying resolved known mineralization as weak electromagnetic conductor responses, and petrophysical testing on core samples shows that this is caused by galena. Pole-dipole-induced polarization surveying resolved a +20 ms chargeability anomaly on the southern flank of the deposit. This chargeable anomaly response is related to disseminated galena, pyrite, chalcopyrite, and alteration. Joint audiomagnetotelluric-magnetotelluric 2D inverted data sections resolved Abra as a broad weakly conductive anomaly. Weak conductor responses associated with Abra were also resolved in 2D and 3D inversion modeling of airborne Z-axis tipper electromagnetic data. 2D seismic reflection surveying resolved Abra as strong flat-lying seismic reflectors, which are bounded and offset by faults and surrounded by a seismically bland zone. The seismic reflections are related to significant density contrasts between high-density stratiform mineralization that is in contact with low-density sedimentary host rocks, as the mineralization and host rocks have similar seismic velocities. Passive seismic horizontal to vertical spectral ratio surveying resolved the top of the deposit as a subtle layer sitting below a flat impedance contrast horizon that is interpreted as weathered siltstone on top of diagenetically cemented siltstone.

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.

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.

Airborne electromagnetic data denoising based on dictionary learning

Time-domain airborne electromagnetic (AEM) data are frequently subject to interference from various types of noise, which can reduce the data quality and affect data inversion and interpretation. Traditional denoising methods primarily deal with data directly, without analyzing the data in detail; thus, the results are not always satisfactory. In this paper, we propose a method based on dictionary learning for EM data denoising. This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal. In the process of dictionary learning, the random noise is filtered out as residuals. To verily the effectiveness of this dictionary learning approach for denoising, we use a fixed overcomplete discrete cosine transform (ODCT) dictionary algorithm, the method-of-optimal-directions (MOD) dictionary learning algorithm, and the K-singular value decomposition (K-SVD) dictionary learning algorithm to denoise decay curves at single points and to denoise profile data for different time channels in time-domain AEM. The results show obvious differences among the three dictionaries for denoising AEM data, with the K-SVD dictionary achieving the best performance.

Improvement of the Efficiency of Time-Domain Electromagnetic Measurement

The long-term sustainable development of mineral exploration under covers require effective deep detection techniques and methods. Start from enhancing the surface-borehole time-domain electromagnetic (TEM) technique, the present study established a new relationship between the pulse width (Δ), the target time constant (τ) and the measurement time (t). Under certain conditions, the new formula has been extended to all TEM systems that use square or trapezoidal waveforms. A series of numerical simulations illustrate the consistent behaviors of the surface-borehole, ground and airborne TEM fields. The new relationship allows us to evaluate optimal pulse widths for different off-times and to help TEM survey design.

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.

Finite element solution to 3-D airborne time-domain electromagnetic problems in complex geological media using non-conforming hexahedral meshes

We propose the approach to 3-D modeling of the electromagnetic (EM) field in time-domain airborne EM problems in complex media including rugged topography and curved borders between the geological layers, target objects of different shapes, lateral inhomogeneity within subhorizontal layers. For discretization of the electromagnetic field in space, the vector finite element method is used. We propose the approach to generating the optimized non-conforming finite element meshes with hexahedral cells in which several finite elements can be attached to the face of the other finite element which allows achieving the significant reduction of computational costs in comparison with the regular meshes on which the numerical solution has the same accuracy. The optimized non-conforming finite element mesh in a complex medium with the topography and curved borders of layers is generated with the use of special transformations. The method of constructing the finite element approximations on these non-conforming hexahedral meshes is proposed. For discretization of the electromagnetic field in time, the implicit three-point scheme “with backward overstepping” is proposed. For solving the finite element SLAE, we use the direct solver PARDISO the effectiveness of which is achieved due to the use of time stepping with a piecewise constant step and grouping the calculations of the electromagnetic field in time steps and transmitter positions. In order to increase the accuracy of calculating the signal in the receiver, a special procedure of smoothing the magnetic field B in space and special scheme of approximating the derivative of B in time are proposed. The verification of the proposed approaches is performed using the solution for the 1-D model and solutions for 3-D models obtained by other authors. We present the results of the comparative analysis of different time schemes in finite element calculating the electromagnetic field and in calculating the signal (in the form of EMF) in the receiver and choose the optimal step and the number of subintervals with the constant steps into which the entire modeling time interval is divided. We perform the comparative analysis of stair-like approximation of curved surfaces with the use of rectangular parallelepipeds and approximation with the use of the hexahedral elements and choose the optimal parameters of generating the finite element meshes. We present the results of the analysis of the possibility of replacing the model with topography with the “flat” 3-D model in which the topography is taken into account by correcting the airborne system height. For the “flat” 3-D model, we present the results of the analysis of the computational effectiveness through the use of the primary-secondary field approach with automatic choice of a horizontally-layered medium for calculating the primary field. For the complex geolelectrical model with topography, laterally-inhomogeneous overburden with curved boundaries, target objects under overburden and for a great number of airborne system positions, the computational costs are presented for sequential and parallel modes for modeling time from 0.01 ms to 1 ms and 10 ms. We present the analysis of the topography effect, the influence of the inhomogeneities in the overburden and target objects in the complex media and show that in this conditions, to discover the target objects using the form of the signal is practically impossible, i.e. in order to discover the target objects in these complex media, it is required to perform a very fine 3-D interpretation for carrying out of which fast and high accuracy forward 3-D modeling is of great importance. We obtain that for calculating the field for one transmitter and the entire modeling time interval in the sequential mode on one core of PC with processor Intel i7-8700K CPU of 3.7 GHz and 32 GB of DRAM, the required computational time is about 4–6 s. The required computational time in the parallel mode with the use of six cores of this PC is about 0.65–1 s.

Deep mineral exploration using multi-scale electromagnetic geophysics: the Lalor massive sulphide deposit case study

The Lalor deposit in Snow Lake, central Manitoba, is one of the most significant mineral discoveries in Canada in the past decade. Buried 600 m below the surface, the deposit remained undiscovered until a deep penetrating geophysical electromagnetic (EM) system was employed. Since then, the deposit has been a test site for many modern geophysical systems. This paper presents a comparative study of four EM data sets acquired at Lalor. We image the electrical conductivity structure of the subsurface by carrying out independent 3-D inversions of the data. The four data sets are acquired through airborne, surface, and borehole systems, including airborne natural source EM (ZTEM), airborne time-domain EM (HELITEM), surface large loop EM (SQUID), and borehole EM (PULSE-EM). ZTEM has good depth of penetration, but its inversion model may be biased if the background model is not properly chosen. The HELITEM system can complement ZTEM by validating the actual conductivity of the deposit. With the information provided by airborne surveys, surface EM can better define the geometry of the ore body at a local scale and help in defining drilling targets. Once boreholes are drilled, sensors can be sent downhole, possibly probing the ore lenses that are interbedded at a greater depth. Our 3-D imaging experiments demonstrate that modern geophysical technology is capable of making deep exploration and assisting a more informed process throughout the entire workflow from reconnaissance to drilling and development.