Airborne Transient Electromagnetic Data Research Articles

Noise suppression of airborne transient electromagnetic data with minimum curvature

The airborne transient electromagnetic (ATEM) method is a geophysical exploration technique that offers several advantages, including rapid exploration, minimal interference from complex terrains, dense data sampling, and environmental friendliness. However, the full-frequency time-domain secondary field signal acquisition of the ATEM data is susceptible to various electromagnetic interferences, resulting in noise in the collected data. This noise complicates subsequent processing and interpretation. To address this issue, we have developed a 1D minimum-curvature difference equation with unequal spacing based on minimum-curvature differential equations. Through research, we develop an ATEM noise-suppression method based on multiple single-step and multiple superposition-step implicit iterative formats. In addition, we have investigated the convergence of explicit and implicit iterative schemes via Fourier spectrum analysis and proven that the implicit iterative schemes converge. This method effectively suppresses noise in synthetic and real ATEM data during processing. Our results show that the minimum-curvature method is particularly effective in suppressing sferic and Gaussian noises in ATEM data. As a result, our method provides high-quality and reliable ATEM data for further data processing and interpretation. Moreover, the minimum-curvature method is also capable of suppressing noise in ground and ground-space transient electromagnetic data, as well as magnetotelluric data noise. Therefore, this method exhibits a wide range of potential applications.

Weighted Laterally Constrained Inversion of Airborne Transient Electromagnetic Data with IP Effects

Due to the induced polarization (IP) effects, abnormal fast decay and sign reversals are often observed in the field airborne transient electromagnetic (ATEM) responses, this phenomenon can well be explained by introducing the four-parameter Cole-Cole resistivity model. However, the introduction of multiple parameters makes the non-uniqueness of inversion more serious. To improve this problem, a weighted 1D laterally constrained inversion is proposed to recover the four IP parameters and thickness via adding different weighted factors chosen by lateral constraint conditions, in this paper, a synthetic polarization model is developed and 1D forward modeling with synthetic natural noise is conducted to imitate the field response. The proposed inversion is employed on the synthetic data and the results demonstrate that the method can improve the inversion stability and obtain more reliable result.

Denoising for airborne transient electromagnetic data using noise-whitening-based weighted nuclear norm minimization

Abstract Recovering anomalous information covered under noise in late gates can enhance airborne transient electromagnetic (ATEM) detection. Conventional denoising mainly comprises filtering and gate correlation-based decomposition algorithms; the former fails to extract anomalies contaminated by noise and the latter relies on the correlation between gates, which may yield false late gate anomalies caused by early large-amplitude anomalies in early gates. In ATEM profiles, the correlation between anomalies in adjacent gates makes the anomalies to be measured to have low-rank characteristics relative to the noise-contaminated profiles; the noise is uniformly distributed in the profiles, which have nonlocal self-similarity. Therefore, the low-rank matrix approximation algorithm is applicable to ATEM data denoising. In this study, an algorithm—noise-whitening-based weighted nuclear norm minimization (NW-WNNM)—is designed to remove ATEM profile noise. First, we analyze the influence of patch size in block matching on anomalous and noisy patches and estimate the profile patch size adaptively. Then, we combine the estimation of noise variance in weighted nuclear norm minimization (WNNM) with the noise whitening of similar patch matrices to reduce the noise interference on the nuclear norm and add a whitening factor in the weight vector to make the soft-thresholding function applicable to the low-rank reconstruction of the whitened matrix. By analyzing the reconstructed low-rank matrix and its feature distribution, compared with WNNM, NW-WNNM can detect the feature information more accurately and eliminate the influence of noise on the nuclear norm. Simulation and field profile results indicate that NW-WNNM is superior to comparison denoising methods.

Inversion of airborne transient electromagnetic data based on reference point lateral constraint

The airborne transient electromagnetic method (ATEM) has become a popular tool in mineral and resource exploration due to its speed and high efficiency. The distance between two adjacent measured points is minuscule for a flight line because of the oversampling in the ATEM, which results in a sharp change in the horizontal direction. The conventional lateral constraint inversion method accumulates errors and has a significant calculation cost. Therefore, a laterally constrained, segmented inversion method based on a reference point is proposed to reduce errors and improve accuracy. Electrical information about the known logging data is employed as the lateral constraint of the reference point; alternatively, the inversion results of previous point can be used as lateral constraints, which guarantees a continuous inversion profile. The golden-section method is applied to solve the Lagrange multiplier to ensure a quick and effective convergence of inversions. Constraint inversions of a one-dimensional model show that the strategy converges quickly, and the inversion results are closer to the true resistivity of the model. Constraint inversion of a 2D model indicates that lateral constraints considerably reduce the discontinuity of a single-point inversion and smooths the inversion's resistivity pseudo-section. Finally, the effectiveness of the proposed algorithm is verified by the inversion of the airborne transient electromagnetic survey data in Xinjiang province.

Comparison of ground-based and airborne transient electromagnetic methods for mapping glacial and permafrost environments: Cases from McMurdo Dry Valleys, Antarctica

The transient electromagnetic (TEM) method is a non-invasive geophysical tool well-suited for subsurface imaging in cold and polar regions, where common targets are associated with strong contrasts in electrical resistivity. By imaging the electrical properties of the subsurface, the TEM methods can discriminate between geological units such as frozen ground (permafrost), fresh/saline groundwater systems, and bedrock/glacier ice. In this study, we compare TEM data acquired with ground-based and airborne TEM systems. We demonstrate the mapping capabilities of these two approaches in high latitude polar environments with datasets from Taylor Glacier, Lake Vanda, and Canada Glacier in the McMurdo Dry Valleys of Antarctica. The results show a high consistency between the airborne and ground-based TEM data, both with a high resolution and a deep penetration depth down to hundreds of meters due to the resistive background material, which makes both approaches capable of mapping hydrological systems and identifying the base of glaciers. The airborne TEM approach offers an unmatched spatial data coverage in difficult terrain and a far improved lateral resolution compared to the static ground-based system. The ground-based TEM system offers the possibility for using larger transmitter coils and longer stacking times and therefore has potential for reaching deeper penetration depths. The ground-based TEM approach is hence a valuable tool that can provide consistent imaging results while also being far more accessible in terms of cost and field logistics compared to an airborne TEM campaign.

Accelerated 2.5-D inversion of airborne transient electromagnetic data using reduced 3-D meshing

SUMMARY Airborne systems collecting transient electromagnetic data are able to gather large amounts of data over large areas in a very short time. These data are most often interpreted through 1-D inversions, due to the availability of robust, fast and efficient codes. However, in areas where the subsurface contains complex structures or large conductivity contrasts, 1-D inversions may introduce artefacts into the models, which may prevent correct interpretation of the results. In these cases, 2-D or 3-D inversion should be used. Here, we present a 2.5-D inversion code using 3-D forward modelling combined with a 2-D model grid. A 2.5-D inversion is useful where the flight lines are spaced far apart, in which case a 3-D inversion would not add value in relation to the added computational cost and complexity. By exploiting the symmetry of the transmitter and receiver system we are able to perform forward calculations on a reduced 3-D mesh using only half the domain transecting the centre of the transmitter and receiver system. The forward responses and sensitivities from the reduced 3-D mesh are projected onto a structured 2-D model grid following the flight direction. The difference in forward calculations is within 1.4 per cent using the reduced mesh compared to a full 3-D solution. The inversion code is tested on a synthetic example constructed with complex geology and high conductivity contrasts and the results are compared to a 1-D inversion. We find that the 2.5-D inversion recovers both the conductivity values and shape of the true model with a significantly higher accuracy than the 1-D inversion. Finally, the results are supported by a field case using airborne TEM data from the island of Mayotte. The inverted flight line consisted of 418 soundings, and the inversion spent an average of 6750 s per iteration, converging in 16 iterations with a peak memory usage of 97 GB, using 18 logical processors. In general, the total time of the 2-D inversions compared to a full 3-D inversion is reduced by a factor of 2.5 while the memory consumption was reduced by a factor of 2, reflecting the half-mesh approach.

Comparing induced polarization effect on semi-airborne and airborne transient electromagnetic data: A numerical study

In a transient electromagnetic (TEM) survey, when a chargeable body presents, its induced polarization (IP) will generate an additional electromagnetic field that further superposes on the TEM data. If the added fields are not considered properly when designing a TEM survey or inverting the TEM datasets, erroneous results may be obtained. Thus, we compare the IP effects on the airborne and semi-airborne TEM methods in this study. One big difference between the two methods is that the semi-airborne method mainly uses the ground-based galvanic source and the airborne method utilizes the air-based inductive source. To fully illustrate this difference, we calculated the IP-TEM coupled responses at a time range of 10−6 s to 10 s. Despite the commonly known reversal of signs in the transient, we found that in some cases of airborne TEM response, the sign will reverse twice. The relative errors caused by the IP effect are further demonstrated. A minimum peak at the early time that is closely related to the IP parameters is revealed, indicating that the IP fields will drop rapidly at the early time and then decay slowly. Finally, a misfit space analysis is performed, showing that both methods can well resolve the resistivity and the IP parameters of a conductive target, while it is difficult to recover the IP parameters of a resistivity target.

Joint inversion of airborne TEM data and surface geoelectrical data. The Egebjerg case

In modern hydrogeological investigations with dense data coverage, the aim is to combine several different sources of information about subsurface structures to enable the best possible geological and hydrogeological interpretations from which a dynamic hydraulic model can be constructed. In this paper, the Egebjerg area in Denmark is used as an example of the process of combining airborne transient electromagnetic data with ground-based galvanic geoelectrical data in a joint inversion to produce a better resolution of the subsurface resistivity structure than would be possible using each of the methods alone. The joint inversion is realised by mutually constraining the inversion models of one data set with the inversion models of the other, overcoming the differences in scale and resolution power that are inherent in this type of effort.

Convolutional neural network inversion of airborne transient electromagnetic data

ABSTRACTAs an efficient geophysical exploration technique, airborne transient electromagnetics shows strong adaptability to complex terrains and can provide subsurface resistivity information rapidly with a dense spatial coverage. However, the huge volume of airborne transient electromagnetic data obtained from a large number of spatial locations presents a great challenge to real‐time airborne transient electromagnetic interpretation due to the high computational cost. Moreover, the inherent non‐uniqueness of the inverse problem also limits our ability to constrain the underground resistivity structure. In this study, we develop an entirely data‐driven convolutional neural network to solve the airborne transient electromagnetic inverse problem. Synthetic tests show that the convolutional neural network is computationally efficient and yields robust results. Compared with the Gauss–Newton method, convolutional neural network inversion does not depend on the choices of an initial model and the regularization parameters and is less prone to getting trapped in a local minimum. We also demonstrate the general applicability of the convolutional neural network to three‐dimensional synthetic airborne transient electromagnetic responses and the field observations acquired from Leach Lake Basin, Fort Irwin, California. The efficient convolutional neural network inversion framework can support real‐time resistivity imaging of subsurface structures from airborne transient electromagnetic observations, providing a powerful tool for field explorations.

Induced polarization effects in airborne transient electromagnetic data collected in the McMurdo Dry Valleys, Antarctica

SUMMARYAirborne electromagnetics (EM) is a geophysical tool well suited to mapping glacial and hydrogeological structures in polar environments. This non-invasive method offers significant spatial coverage without requiring access to the ground surface, enabling the mapping of geological units to hundreds of metres depth over highly varied terrain. This method shows great potential for large-scale surveys in polar environments, as common targets such as permafrost, ice and brine-rich groundwater systems in these settings can be easily differentiated because of their significant contrasts in electrical properties. This potential was highlighted in a 2011 airborne EM survey in the McMurdo Dry Valleys that mapped the existence of a large-scale regional groundwater system in Taylor Valley. A more comprehensive airborne EM survey was flown in November 2018 to broadly map potential groundwater systems throughout the region. Data collected in this survey displayed significant perturbations from a process called induced polarization (IP), an effect that can greatly limit or prevent traditional EM workflows from producing reliable geological interpretations. Here, we present several examples of observed IP signatures over a range of conditions and detail how workflows explicitly designed to handle IP effects can produce reliable geological interpretations and data fits in these situations. Future polar EM surveys can be expected to encounter strong IP effects given the likely presence of geological materials (e.g. ice and permafrost) that can accentuate the influence of IP.

Airborne geophysical investigations for landslide risk investigation along the Ångerman River, central Sweden

A study of landslide risks along the Ångerman River in Sweden is currently being performed by the Swedish Geotechnical Institute (SGI) with assistance from the Geological Survey of Sweden (SGU). In this work, knowledge of the distribution of different types of sediment is crucial. The area shows large variations in both sediment stratigraphy and sediment thickness (depth to bedrock) which makes it difficult to describe geotechnical properties for larger areas based on sparse geotechnical field investigations (soundings and sampling). Within the study area, the Ångerman River valley contains numerous fluvial terraces at various elevations separated from the river by steep erosional scarps. Although fluvial sand caps the terraces, their heights are often derived from the underlying silt and clay, which in turn overlies till or glaciofluvial sediment. The thickness of these general stratigraphic units is difficult to determine from existing data. Thus, in 2018, an airborne transient electromagnetic (ATEM) survey was performed along the river and the surrounding river banks with the objective of mapping and characterizing the sediments down to bedrock. The surveyed area is 2 km wide and covers 80 km along the river with a flight line distance of 75 m. From the ATEM data, the 3D resistivity distribution of the ground down to about 100 m below the surface can be determined. The silt and clay show very low resistivity compared to the overlaying sand layers. Therefore, they can be easily distinguished in the resistivity models from the ATEM data. Furthermore, the depth to the more resistive underlaying till and bedrock can be delimited in most areas. The 3D resistivity model derived from the ATEM data was integrated with other geoscientific data such as; geotechnical drillings, information from water wells and surficial deposits maps using 3D modeling software (Geoscene 3D from I-GIS). Based on the modelling results, the area was divided into six different classes based on the soil stratigraphy. The current method of analysis uses detailed geotechnical investigations performed in one location to ground truth the interpretation of geophysical data over a broad area. With the new results based on the ATEM data, the planning of new geotechnical drillings can be better focused on more poorly-understood areas. Furthermore, the number of geotechnical field investigations can be reduced.

Single weighted laterally constrained inversion of airborne transient electromagnetic data with sharp boundary

Traditional laterally constrained inversion (LCI) can quickly and efficiently invert large geophysical data with minimizing the difference between adjacent model parameters. However when processing the data with large boundary differences, the inversion generates the result whoes the boundaries are poorly fitness with the actual underground geology. In order to overcome this problem, some specific measurement stations of original LCI has been adjusted to weaken the horizontal continuity constraint of the horizontal direction near the sharp boundary. The simulation results show that the determinations of station and weights value depend on the thickness or depth change at sharp boundary. In this paper, we employed single weighted laterally constrained inversion (SWLCI) method on the model with sharp boundary. The result showed that SWLCI can improve both the recovering of sharp boundary and the stability of inversion.

Lithology Identification of Alluvial and Lacustrine Aquifers in Baiyangdian Area by an Airborne Transient Electromagnetic Survey

An airborne transient electromagnetic (TEM) has been conducted in Baiyangdian Area to map resistivity distribution within the alluvial and lacustrine aquifers. This investigation evaluated the reliability of resistivity models of the TEM data to infer lithological distribution in alluvial and lacustrine aquifers. The collected airborne TEM data were processed and inverted using spatially constrained 1D inversion method. And the 3D electrical structure, which maps electrical resistivity trends to depths of about 160 m, can be established through interpolation. Comparison of the resistivity models to drill logs indicates resistive permeable coarse-grained sediments and conductive fine-grained sediments which are relatively impermeable. Hence, the 3D electrical structure that are related to lateral and vertical variation in lithology, can serve as baseline information for groundwater potential identification. This research indicates that groundwater detection is an ideal target for mapping with airborne TEM techniques because of the high electrical resistivity of permeable coarse-grained deposits and its contrast with impermeable fine-grained deposits.

Calculation and application of full-wave airborne transient electromagnetic data in electromagnetic detection

Airborne electromagnetic transient method enjoys the advantages of high-efficiency and the high resolution of electromagnetic anomalies, especially suitable for mining detection around goaf areas and deep exploration of minerals. In this paper, we calculated the full-wave airborne transient electromagnetic data, according to the result of numerical research, the advantage of switch-off time response in electromagnetic detection was proofed via experiments. Firstly, based on the full-wave airborne transient electromagnetic system developed by Jilin University (JLU-ATEMI), we proposed a method to compute the full-waveform electromagnetic (EM) data of 3D model using the FDTD approach and convolution algorithm, and verify the calculation by the response of homogenous half-space. Then, through comparison of switch-off-time response and off-time response, we studied the effect of ramp time on anomaly detection. Finally, we arranged two experimental electromagnetic detection, the results indicated that the switch-off-time response can reveal the shallow target more effectively, and the full-waveform airborne electromagnetic system is an effective technique for shallow target detection.

Noise reduction of grounded electrical source airborne transient electromagnetic data using an exponential fitting-adaptive Kalman filter

The grounded electrical source airborne transient electromagnetic (GREATEM) system, which uses a grounded electrical transmitter and an aircraft for the receiver, offers deep exploration capability and detection efficiency. However, GREATEM field data usually includes mixed varied noises (white noise, sferics noise and human noise), which make identifying the exponential decaying signal too difficult. Traditional filtering methods mainly focus on suppressing specific noise types, which may cause the distortion of GREATEM signal, especially when the signal is affected by high residual sferics noise. This paper presents an exponential fitting-adaptive Kalman filter (EF-AKF) to remove mixed electromagnetic noises, while preserving the signal characteristics. The EF-AKF consists of an exponential fitting procedure and an adaptive scalar Kalman filter (SKF). The adaptive SKF uses the exponential fitting results in the weighting coefficients calculation. The EF-AKF is verified on an analytical three-layer model. It is compared with the SKF and wavelet threshold-exponential adaptive window width-fitting denoising algorithm (WEF) in synthetic data. The results showed that the EF-AKF outperformed the other methods in the noise reduction of GREATEM data. The EF-AKF is also tested on a synthetic quasi-2D earth model and applied to GREATEM field data in Huaide, Jilin province, China. Application of the EF-AKF allowed considerable improvement of the quality of the GREATEM field data.

An efficient hybrid scheme for fast and accurate inversion of airborne transient electromagnetic data

Airborne transient electromagnetic (TEM) methods target a range of applications that all rely on analysis of extremely large datasets, but with widely varying requirements with regard to accuracy and computing time. Certain applications have larger intrinsic tolerances with regard to modelling inaccuracy, and there can be varying degrees of tolerance throughout different phases of interpretation. It is thus desirable to be able to tune a custom balance between accuracy and compute time when modelling of airborne datasets. This balance, however, is not necessarily easy to obtain in practice. Typically, a significant reduction in computational time can only be obtained by moving to a much simpler physical description of the system, e.g. by employing a simpler forward model. This will often lead to a significant loss of accuracy, without an indication of computational precision.We demonstrate a tuneable method for significantly speeding up inversion of airborne TEM data with little to no loss of modelling accuracy. Our approach introduces an approximation only in the calculation of the partial derivatives used for minimising the objective function, rather than in the evaluation of the objective function itself. This methodological difference is important, as it introduces no further approximation in the physical description of the system, but only in the process of iteratively guiding the inversion algorithm towards the solution. By means of a synthetic study, we demonstrate how our new hybrid approach provides inversion speed-up factors ranging from ~3 to 7, depending on the degree of approximation. We conclude that the results are near identical in both model and data space. A field case confirms the conclusions from the synthetic examples: that there is very little difference between the full nonlinear solution and the hybrid versions, whereas an inversion with approximate derivatives and an approximate forward mapping differs significantly from the other results.

3D inversion for multipulse airborne transient electromagnetic data

Multipulse airborne transient electromagnetic (ATEM) systems transmit one high-power pulse and one low-power pulse containing more high-frequency EM signals. Such systems have better near-surface resolutions while maintaining the depth of exploration of other conventional systems. ATEM systems are especially suitable for geologic mapping and mineral exploration. The inversion of multipulse ATEM data has been mainly limited to 1D modeling, which is not suitable for complex underground structures. We have investigated an algorithm for 3D multipulse ATEM data inversion based on direct Gauss-Newton optimization with quite-fast convergence. The forward problems were solved in the frequency-domain based on the secondary scattered electrical field equation, and then the inverse Fourier transform and the convolution with transmitting waveform were applied to calculate the arbitrary waveform response and sensitivity matrix in the time domain. To optimize the number of computations and memory, we further used an EM “footprint” concept in our inversions to reduce the forward model size and sparse the sensitivity matrix. The inversion results of synthetic data showed that our 3D algorithm is very effective for inverting the multipulse data with results combining advantageous resolutions of different transmitting pulses. Finally, we applied our algorithm to invert real survey data obtained at McMurray, Alberta, Canada, to further test its effectiveness.

Quasi-3D inversion of full size AEM datasets

SUMMARY We present a new algorithm for quasi-3D inversion of airborne transient electromagnetic data (AEM). The algorithm uses a 3D voxel grid for the model domain while the forward response and derivatives are calculated using interpolated virtual 1D models collocated at the AEM measurement points. The algorithm efficiently decouples the model domain from the practical spatial sampling which are often dictated by the landscape, ground installations ect., rather than the optimal model resolution. The algorithm uses sparse storage with efficient solvers and it scales linearly both with the number of CPU's and with the survey size. This means that it can be used to invert entire surveys of thousands of kilometres on small multiprocessor servers priced less than 5000 Euro. The algorithm allows inputting a-priori information from boreholes, joint inversion with groundbased electrical/electromagnetic data and modelling of flight altitude and shift parameters.

Artificial neural networks for efficient removal of coupled airborne transient electromagnetic data

Modern airborne transient electromagnetic (ATEM) surveys typically span thousands of line kilometres requireing careful data processing. When surveys are flown in populated areas data processing becomes particularly time consuming, since the acquired data is contaminated by couplings to man made conductors (power lines, fences, pipes, etc). Coupled soundings must be removed from the dataset prior to inversion, but since the signature of couplings can be subtle and difficult to describe in general terms it has so far remained mostly a manual task. Here we train an artificial neural network (ANN) to recognize coupled soundings in previously processed data and use this network to identify couplings in other data. The approach provides a dramatic reduction in the time required for data processing, since one can directly apply the network to the raw data. We describe the neural network we use and present the inputs and normalizations required for maximizing the effectiveness of the network. We present the training state and performance of the network and finally compare inversions based on manually processed data and ANN processed data. The results show that a well trained network can produce a high quality processing of ATEM data, which is either ready for inversion or in need of minimal manual processing. The results are very promising and can significantly reduce the processing time and cost of large ATEM surveys.

Artificial neural networks for removal of couplings in airborne transient electromagnetic data

ABSTRACTModern airborne transient electromagnetic surveys typically produce datasets of thousands of line kilometres, requiring careful data processing in order to extract as much and as reliable information as possible. When surveys are flown in populated areas, data processing becomes particularly time consuming since the acquired data are contaminated by couplings to man‐made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, and this is a process that is difficult to automate. The signature of couplings can be both subtle and difficult to describe in mathematical terms, rendering removal of couplings mostly an expensive manual task for an experienced geophysicist.Here, we try to automate the process of removing couplings by means of an artificial neural network. We train an artificial neural network to recognize coupled soundings in manually processed reference data, and we use this network to identify couplings in other data. The approach provides a significant reduction in the time required for data processing since one can directly apply the network to the raw data. We describe the neural network put to use and present the inputs and normalizations required for maximizing its effectiveness. We further demonstrate and assess the training state and performance of the network before finally comparing inversions based on unprocessed data, manually processed data, and artificial neural network automatically processed data. The results show that a well‐trained network can produce high‐quality processing of airborne transient electromagnetic data, which is either ready for inversion or in need of minimal manual processing. We conclude that the use of artificial neural network scan significantly reduce the processing time and its costs by as much as 50%.