Exploration Geophysics - Volume 51, Issue 1, 2020

The aim of this research is to compare the results of three 3D inversion methods applied to greenfield airborne electromagnetic (AEM) data. The first two methods of inversion are the CDI3D and Maxwell codes, each parameterising the 3D conductor with a thin-plate approximation; inversion of which is overdetermined with AEM data. The third inversion method is an underdetermined 3D voxellated inversion using the finite volume method and regularisation based on minimising differences from a starting model. Deterministic, iterative, non-linear inversion requires a starting model, which in greenfield exploration is not available a priori using geological knowledge. We investigated three different starting models, each generated from fitting the data. These were: (1) a 1D background from CDI3D, (2) the approximate CDI3D solution including a discrete target, and (3) a stitched 1D starting model often called a conductivity–depth image (CDI). The CDI starting model is similar to the expected output of stitched 1D inversion codes. We investigated a volume of earth surrounding the IR-2 conductor at the Forrestania, Western Australia test site, located under five lines of VTEM Max data. The conductor response is seen in the central three of the five selected lines. Both plate approximations fit easily with aspects of the observed data, and their interpreted conductor location is consistent with the intersection of sulphides in the one drill hole on site. The voxellated 3D inversion consumed significant computer resources compared with approximate solutions. The result of the voxellated inversion was unsatisfactory when the stitched CDI was used as a starting model. We believe that this was because the stitched CDI shows an unreasonably deep, small conductor at the position of the true conductor. Because this deep conductor produces an undetectable 3D modelling response, it persists through the inversion process. We infer that all stitched 1D solutions, even those derived from inversion with or without lateral constraints, are likely to produce poor starting models for 3D inversion of finite conductors.

Electromagnetics has an important role to play in solving the next generation of geoscience problems. These problems are multidisciplinary, complex, and require collaboration. This is especially true at the base scientific level, where the underlying physical equations need to be solved, and data, associated with physical experiments, need to be inverted. In this paper, we present arguments for adopting an open-source methodology for geophysics and provide some background about open-source software for electromagnetics. Immediate benefits are the reduced time required to carry out research, being able to collaborate, having reproducible results, and being able to disseminate results quickly. To illustrate the use of an open-source methodology in electromagnetics, we present two challenges. The first is to simulate data from a time-domain airborne system over a conductive plate buried in a more resistive earth. The second is to jointly invert airborne time-domain electromagnetic (TDEM) and frequency-domain electromagnetic (FDEM) data with ground TDEM. SimPEG (Simulation and Parameter Estimation in Geophysics, https://simpeg.xyz) is used for the open-source software. The figures in this paper can be reproduced by downloading the Jupyter notebooks we provide with this paper (https://github.com/simpeg-research/oldenburg-2018-AEM). Access to the source code allows the researcher to explore simulations and inversions by changing model and inversion parameters, plot fields and fluxes to gain further insight on the electromagnetic phenomena, and solve a new research problem by using open-source software as a base. By providing results in a manner that allows others to reproduce, further explore, and even extend them, we hope to demonstrate that an open-source paradigm has the potential to enable more rapid progress in the geophysics community as a whole.

Inversion 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.

Frequency domain helicopter-borne electromagnetic (FHEM) surveys have been used as an effective tool for the exploration of underground resources for as long as airborne electromagnetics (AEM) has existed. Large FHEM data sets are commonly interpreted with very fast 1D inversion algorithms that often numerically adequately fit the data sets, even though they yield incorrect results near 2D/3D geological structures. The present study aims to compare 1D and 2D inversion algorithms when applied to the reconstruction of geologically complex regions. We have developed a 2D inversion algorithm incorporating the Levenberg–Marquardt least-squares approach regularised through spatial constraints to retrieve 2D electrical resistivity models associated with arbitrary surface topography. The approach uses a 2D finite element frequency domain solution and a tailored triangular meshing algorithm based on the Ruppert’s Delaunay refinement for the forward modelling. We illustrate how rough topographic effects obscure the FHEM response and affect recovered resistivity models through numerical experiments. We also demonstrate the influence of acquisition frequency and resistivity structure on the topographic effect. In the Appendix, we discuss the FHEM footprint concept from a 2D perspective to assess how 2D effects affect and bias 1D inversion results. Complex 2D synthetic scenarios are presented to compare 1D and 2D inversion in various settings. Two field cases from Norway and Iran are presented to show the model improvements with 2D inversion. For the Norwegian case, the 2D FHEM inversion aligns well with a model retrieved from ground-based electrical resistivity tomography. We show the bias imposed on the 2D inversion of the data set from Iran by improper system calibration.

The iterated extended Kalman filter (IEKF) is a tool within the theory of optimal estimation used for nonlinear problems. The IEKF minimises variance in the estimation error in terms of a probabilistic approach. Despite the special terminology, the Kalman filter algorithm minimises the objective function, representing the normalised squared difference between the measured and calculated vectors for the parameters of a selected model. It works like the weighted least squares method – a conventional method for airborne electromagnetic data inversion. In this article, I describe the essence of the Kalman approach to solving inverse problems. I show how one-dimensional inversion with lateral constraints can be performed in terms of the Kalman filter. The described algorithm takes account of the measurement noise, which is specified as the dispersion of signals in the corresponding measurement channels at high altitude. A specific covariance matrix representation allows use of the corresponding Kalman filter calculation methods. They provide numerical stability of the algorithm. The Kalman approach makes it possible to combine modern techniques used in airborne survey data processing. Some examples of Kalman filter use in frequency-domain airborne data processing are given.

Paleochannels typically act as pathways for groundwater movement and provide a potential source of groundwater. Their presence can be helpful in identifying areas suitable for recharge and at times in mitigating contamination problems in afflicted regions. Thus, mapping of paleochannels is significant in the planning and management of groundwater resources. An airborne electromagnetic (AEM) system employing dual pulse moments has been used extensively for this purpose in India. This paper presents the results over paleochannels defined in three different terranes. In northwest India, a 100 m wide by 80 m deep paleochannel within alluvium overlaying a Proterozoic basement illustrates the impact of neotectonic disturbances in changing the river course. In northeast India’s Ganga Plains, a paleochannel is mapped that provides insight into managing groundwater resources of areas polluted with arsenic. In south India, a paleochannel buried under ∼ 100 m thick sequence of coastal sediments is imaged with implications on submarine groundwater discharge.

Permafrost is ubiquitous at high latitudes, and its thickness is controlled by important local factors like geothermal flux, ground surface temperature and thermal properties of the subsurface. We use airborne transient electromagnetic resistivity measurements to determine permafrost thickness on the coast of Ross Island, Antarctica, which contains the active volcano Mt Erebus. Here, resistivity data clearly distinguish resistive permafrost from the electrically conductive fluid-saturated materials underlying it. For our study, we define permafrost as frozen material with a resistivity > 100 Ω·m; more conductive material contains a significant fraction of water or (more likely) brine. We observe that permafrost is very thin near the coast and thickens within several hundred metres inland to reach depths that are typically within the range of 300–400 m. We attribute the sharp near-shore increase in permafrost thickness to lateral heat conduction from the relatively warm ocean, possibly combined with seawater infiltration into the near-shore permafrost. We validate this result with a two-dimensional heat flow model and conclude that away from the thermal influence of the ocean, the local geothermal gradient and heat flux are about 45 ± 5 °C/km and 90 ± 13 mW/m², respectively. These values are in line with published estimates in the vicinity of Mt Erebus and within the actively extending Terror Rift, but do not reflect a strong heat flow anomaly from volcanic activity of Mt Erebus. Measurements made previously in the McMurdo Dry Valleys, on the other side of McMurdo Sound, tend to be a few dozens of mW/m² lower, likely reflecting its different tectonic setting on the uplifted rift shoulder of Transantarctic Mountains. Our study demonstrates a new approach towards constraining geothermal flux in polar regions using airborne electromagnetic (AEM) data that can be relatively efficiently collected on regional scales where ice coverage does not exceed the penetration limits of the AEM device, which for the device used is ∼ 500 m under the favourable conditions in the study area.

The time decay constant or “tau” of airborne electromagnetic (AEM) systems is commonly used to indicate the presence and relative conductivity or conductance of conductors in the survey area. In fact, it is not a constant because it depends on the system, the survey design and the method of calculation. The system dependence is a consequence of parameters relating to the acquisition and pre- and post-processing of the signal. Here, we propose a method for calculating tau, which is the time at which the transient voltage decays to 37%, or V37, of some initial value. The model utilises a semi-heuristic algorithm to estimate V37 for each transient in the database and then calculates the delay time at which that voltage is measured, which estimates tau. No calculation is involved with the data, instead, tau is given by a weighted average of the delay times associated with windows either side of the V37 value. We illustrate how this algorithm works using data collected using MEGATEM II at the Reid Mahaffy test site. The results show good agreement between tau-grids reported previously and those calculated using our V37 method. To account for all effects due to the acquisition and processing of EM data, the algorithm allows emphasis to be shifted away from early-time to late-time parts of the transient. It is envisaged that because this method does not apply any mathematical operation to the data it may serve as a robust means of validating other methods.

An efficient accurate algorithm has been implemented for the modelling of airborne, land, and marine controlled source electromagnetic data, providing 2D and 3D depth inversions of frequency and time domain data. This is achieved by decoupling the inversion grid from the modelling grid used in the finite difference simulation of the fields. The model grid consists of columns of rectangular prisms that can be arbitrarily discretised in the vertical direction: This helps to better define the topography and other interfaces without densely discretising the upper part of the resistivity model. The material averaging scheme used to map the model onto the forward simulation meshes on the fly was modified to increase modelling accuracy near topography. Furthermore, by setting the horizontal smoothing appropriately, the general geological setting of the survey area can be taken into account during the inversion.

The potential for extracting and interpreting induced polarisation (IP) data from airborne surveys is now broadly recognised. There is, however, still considerable discussion about the conditions under which the technique can provide knowledge about the subsurface and thus, its practical applications. Foremost among these is whether, or under what conditions, airborne IP can detect chargeable bodies at depth. To investigate, we focus on data obtained from a coincident-loop time-domain system. Our analysis is expedited by using a stretched exponential rather than a Cole-Cole model to represent the IP phenomenon. Our paper begins with an example that illuminates the physical understanding about how negative transients (the typical signature of an IP signal in airborne data) can be generated. The effects of the background conductivity are investigated; this study shows that a moderately conductive and chargeable target in a resistive host is an ideal scenario for generating strong IP signals. We then examine the important topic of estimating the maximum depth of the chargeable target that can generate negative transients. Lastly, some common chargeable earth-materials are discussed and their typical IP time-domain features are analysed. The results presented in this paper can be reproduced and further explored by accessing the provided Jupyter notebooks.

Electromagnetics (EM) has been used extensively for Volcanic Hosted Massive Sulphide (VHMS) exploration in Australia. Exploring under conductive cover introduces significant limitations when using EM to identify bedrock conductivity anomalies that may be associated with VHMS deposits. We present an alternative approach, whereby robust geological modelling of the Airborne Electromagnetic (AEM) data plays a major role in the exploration strategy in the Bryah and Yerrida Basins of central Western Australia. The AEM is not only used for the identification of bedrock conductors but also forms a critical dataset constraining a robust basin-wide geological model. This model is used to identify priority areas for follow up surface geophysics and geochemistry. A patchwork of AEM surveys, covering portions of the Bryah and Yerrida basins, has been acquired by various explorers and contractors during the last decade. Systems and system specifications vary greatly. Accordingly, accurate geological interpretation of a basin-scale area, flown using various systems, cannot be derived from either raw data or fast/approximate conductivity products provided by contractors. All datasets require reconciliation with a common workflow and robust modelling strategy. Historic AEM data acquired with different systems along the edges of the tenure have been reprocessed and inverted. The remaining central block awaits the contractor's arrival before the data is subject to the same workflow. The end result will be a seamless basin-wide 3D conductivity model (extending over 6500 km²), which will inform the geological interpretation and subsequent follow-up exploration efforts. The preliminary 3D models already allow clear identification and modelling of the pyritic shale horizons, enabling the anomalous geochemistry and strongly conductive nature of these units to be discounted in the targeting process

Airborne Electromagnetic (AEM) data are increasingly used in many geoscientific applications. BRGM conducted regional AEM surveys on the French overseas volcanic islands, where issues, including groundwater management or risk assessment are crucial. For such “infrastructural” surveys, the data are intended to be used for various purposes and over several years. In this type of environment, with a man-made imprint, a complex geology and rugged topography, the data processing can be very challenging. We therefore developed and implemented an original reprocessing method for regional AEM datasets. It aims to optimise, at a study scale, the coverage, the lateral and vertical resolution and the depth of investigation compared to more “standard” processing. This is highlighted through three examples. Our method includes regional processing procedures on both navigation and EM data: calculation of the effective tilt of the instrument frame, use of a DGPS-derived altitude, application of the singular value decomposition filtering and stacking with an adaptive trapezoid filter. Moreover, a local tuning of the inversion and manual editing are achieved for each study.

IP effects in AEM data are subject of current research around the world. There have been success stories and it is now practical to model AIP. It is widely accepted that failure to account for IP effects, when present, will produce artefacts in the resistivity model. However there still is a need to study more accurately the boundaries of the effect on AEM data and of its relevance, beyond common past acceptance. This paper provides a clear and extensive overview of detectable AIP effects in the data space, without imposing simplistic assumptions (e.g. fixing some parameters to arbitrary values or limited boundaries), beside using a 1D approach. We produce forward responses with dispersive resistivity for hundreds of thousands of combinations of Cole–Cole model parameters (different rock types) and AEM system transfer functions. The results are analysed using various metrics (e.g. sum of negative voltages, exponential fitting), presented with a series of 3D plots that capture different AIP signatures in the transients. Experiments include half spaces, 2 and 3 layer models, combined with different waveforms, Rx types (dB/dt and B), Tx-Rx geometries, flying heights, base frequencies. The results allow a clear assessment of the different aspects of AIP effects over a wide range of geological and geophysical situations. Measured AIP effects are mostly focused in the range of τ from 10⁻² s to 10⁻⁴ s. Measurable AIP effects depend on AEM system’s specs, are often unpredictable, can originate from chargeable layers at considerable depth, are heavily affected by layering and can occur over a wide range of situations. Deeper chargeable layers do not necessarily produce fainter AIP anomalies. What can be generalised is that AIP effects are increased most often by the presence of a resistive bedrock, often using slow turn-off of the waveform, are generally better observed with B field instead of dB/dt and lowering base frequencies. They can vary abruptly, due to the rapidly changing relative contribution of pure EM and pure IP responses. AIP effects can occur more often than previously thought and should not be discarded a-priori from any AEM survey.

This paper presents the results of airborne electromagnetic induced polarisation inversions using the Maximum Phase Angle (MPA) model for a helicopter time domain survey in the Quadrilátero Ferrífero area, Minas Gerais State (MG), Brazil. The inversions were conducted using a laterally constrained robust scheme, in order to decrease the difficulties to recover the multi-parametric model in a very ill-posed inverse problem, often found in induced polarisation studies. A set of six flight lines over the Lamego gold mine mineralised structure were inverted using the MPA re-parameterisation of the Cole–Cole model and also the classical resistivity-only parameterisation, in order to understand the implications of the induced polarisation effect in the data and, consequently, in the resistivity model. A synthetic study was also conducted, seeking to understand what to expect from the resistivity-only inversions in the real data. According to borehole lithological data and previous structural knowledge from the literature, the results from the Maximum Phase Angle approach indicate an important chargeable body that seems to be in good agreement with a sulfide enriched carbonaceous/graphite and altered mafic unities, which are important markers for the gold mineralisation.

In 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.

The AusAEM airborne electromagnetic (AEM) survey is one of the main components of the Exploring for the Future program. This Australian Government initiative is aimed at enhancing the geoscientific information available, to support resource exploration and to showcase Australia as a destination for investment opportunities. Regional geophysical mapping programs are the basis for informed mineral exploration and have enabled the building of a continental-scale inventory of Australia’s potential resource endowment. They also provide elements of the scientific backing for decision-making and answering a range of different geoscientific questions. The latest of these airborne initiatives AusAEM is composed of a series of wide line-spaced studies targeting acquisition of data for regional mapping in area of new frontiers; it is gathering data and unveiling information on extents never previously attempted by AEM surveys.