Transient Electromagnetic Data Research Articles

Pulse Impedance: A New Approach to Defining Apparent Resistivity of Electrical Source Transient Electromagnetic Data

Pulse Impedance: A New Approach to Defining Apparent Resistivity of Electrical Source Transient Electromagnetic Data

Turn-Off Current and TEM Field Based on Distributed Capacitance Model of Multiple Coils in Helicopter Transient Electromagnetic System

For compensating the primary field coupling from the transmitting coil and the sensing coil, the bucking coil is often used to achieve pure secondary field for the helicopter transient electromagnetic (HTEM) system. Traditionally, the distributed capacitance of the bucking coil and the transmitting coil is ignored and pay few attentions to study it. In fact, it has very important influence on the turn-off current of the transmitter and the transient electromagnetic (TEM) field of the system. In this paper, a novel distributed capacitance model of the HTEM coils is theoretically established at the first time, its effects on the turn-off current and the TEM field are analyzed in detail. It finds that the turn-off current in the transmitting coil and the bucking coil are not equal caused by the distributed capacitance although they are connected in series. The field experiments are completed and proved that the measured TEM fields agree very well with the simulated results based on the distributed capacitance model. This is very helpful to optimize the early-time TEM field and promote the shallow TEM data interpretation capability for the HTEM surveying.

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.

3D Inversion of Grounded-source Transient Electromagnetic Data

The grounded-source transient electromagnetic method has the characteristics of convenient installation of emission source and large emission power, and is being widely used. To get a fine interpretation of observation data, we introduce a three-dimensional inversion algorithm for the grounded-source transient electromagnetic data. Firstly, the unstructured time-domain finite-element method is used to calculate the electromagnetic response, and then the quasi Newton method is used for data inversion. Finally, the reliability of this algorithm is verified by applying it to the theoretical model.

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

The transient electromagnetic (TEM) method has long been applied in tunnel advanced prediction. However, it remains questionable to what extent a geologic anomaly body will influence the induced electromagnetic response in front of the heading face. The dilemma is partly because observed TEM data are frequently interpreted by empirical formulas or proportional relationships, and a quantitative measurement has not been established. In this paper, we strive to understand the TEM characteristics from a 3D finite-element time-domain (FETD) modeling aspect. The modeling algorithm is based on unstructured space meshing and unconditional stable time discretization, which ensures its accuracy and stability. The modeling algorithm is verified by a half-space model, in which the misfit of late-time channels that we are concerned with is generally below 1%. The algorithm has also been utilized to carry out the TEM response of tunnel models with different types of TEM devices. Through model studies, we find that both the traditional central-loop device and the recently developed weak-coupling opposing-coil device are feasible in tunnel advanced detection. Nevertheless, the latter type of device better distinguishes low-resistivity anomalies at 30 m ahead of the heading face with a relative difference (between models with and without the anomaly) of more than 1000% at certain time channels, compared with only a 10% difference of the central-loop device. Also, we conclude that the vertical electromagnetic field component should be recorded and interpreted together with the horizontal field to provide more convincing results.

Occurrence of sign reversal or negative decay transients in SQUID based TDEM measurement systems – Is it due to ground geology or something else?

This paper seeks to explain the origin of sign reversal or zero-crossover effect observed at late decay times in the experimental data involving the application of SQUID in geophysical explorations based on TDEM technique. A complete SQUID based TDEM system has been developed and used for field survey after extensive characterization of the system in the laboratory. When the TDEM system was tested in a relatively noisy environment of the laboratory, the sign reversal effects was not observed but the effect was observed conspicuously in the experimental data recorded during an actual field survey at a site far away from the urban electromagnetic noise. The transient electromagnetic data recorded during the field survey showed that the decay transients surprisingly reached negative values in a terrain where a thin conducting layer is sandwiched between thick upper and thin lower resistive layers. The major advantage of using the SQUID sensor in TDEM applications is its ability to achieve extremely high sensitivity even at very low frequencies, enabling one to investigate the conductivity of the layers buried deep below the ground surface. While the decay transients recorded with the use of SQUID magnetometer were successful in providing information pertaining to the buried conducting layer, an unexpected zero crossover was found at later decay times as seen also by a few research groups in the past. The origin of this peculiar zero crossover has not been clearly established so far, and has been speculatively attributed to several parasitic phenomena such as instrumental drift, remnant responses of the previous transmitter pulses, induced polarization effect, poor offset removal, etc., Extensive analysis of our experimental data obtained in the field has, however, shown that the genuine reasons for the observation of zero crossover effect in the data recorded in transient electromagnetic survey using SQUID are actually attributable to the flow of the reverse current and its associated magnetic field generated by the transmitter loop. In this paper, we discuss the possible reasons for the observation of this zero crossover by various research groups, and present a detailed analysis of our own experimental data.

Semi-Airborne UAV-TEM System Data Inversion with S-Plane Method—Case Study over Lake Baikal

The article presents the results of transient electromagnetic (TEM) prospecting surveys using an unmanned aerial system carried out at Lake Baikal, which is a unique geoelectrical setting where low-resistivity lacustrine sediments are located under a relatively isotropic water body. The task was to investigate the possibility of using a drone-based TEM survey to delineate the electrical stratigraphy of the subsurface at depths between 50 and 300 m, separated into layers and blocks. A new version of the SibGIS UAV-TEM unmanned system was used, significantly improved compared to the prototype previously described in the literature. The current switch providing bipolar current pulses connected to a grounded electrical line was the source of the electromagnetic field in the geological environment. The hexacopter carrying a measuring system consisting of 18-bit ADC and sensor—analog of 50 × 50 loop, was the receiving system. We measured survey data of 16 traverses over the Baikal going from the shore to the depths. Significant attention is being paid to a new approach to data inversion. For fast interpretation of the TEM data, we used the Sτ-method, which allows for tracing the change in the apparent longitudinal conductivity with depth. It is shown that thanks to the new sensor and current switch, the data quality has increased significantly; now, the UAV system can register sounding curves up to 1 ms. As a result, new data on the geological structure of the shelf zone of Lake Baikal were obtained. They had a good fundamental agreement with the predecessor data obtained from terrestrial measurements (from ice cover), allowing us to conclude that the UAV-TEM technology can already replace conventional ground-based electromagnetic surveys.

Geocryological Structure of a Giant Spring Aufeis Glade at the Anmangynda River (Northeastern Russia)

Gigantic aufeis fields serve as indicators of water exchange processes within the permafrost zone and are important in assessing the state of the cryosphere in a changing climate. The Anmangynda aufeis, located in the upstream of the Kolyma River basin, is present in the mountainous regions of Northeast Eurasia. Recent decades have witnessed significant changes in aufeis formation patterns, necessitating a comprehensive understanding of cryospheric processes. The objective of the study, conducted in 2021–2022, was to examine the structure of the Anmangynda aufeis and its glade, aiming to understand its genesis and formation processes. The tasks included identifying above- and intra-frozen taliks, mapping groundwater (GW) discharge channels, determining permafrost base depth, and assessing ice thickness distribution. Soundings using ground-penetrating radar (GPR), capacitively coupled electrical resistivity tomography (CCERT), and the transient electromagnetic (TEM) method were employed. GW discharge channels originating from alluvial deposits and extending to the aufeis surface within river channels were identified through GPR and verified through drilling. Deep-seated sources of GW within the bedrock were inferred. CCERT data allowed us to identify large and localized frozen river taliks, from which water is forced onto the ice surface. According to the TEM data, the places of GW outlets spatially coincide with the zones interpreted as faults.

Geophysical and geological exploration of the Aurus clay pan as an archive of the long-term climatic and environmental history of the Namib Desert

The Namib Desert is one of the oldest deserts on Earth. Whilst the history of the desert during the last glacial-interglacial cycle is relatively well understood, information on the older Pleistocene and Neogene history is very scarce, because longer-term records such as those in lakes and caves have not been formed due to the aridity. Here, we investigate the potential of the sedimentary record in the Aurus clay pan in the Sperrgebiet Domain in the southern Namib Desert, Namibia, for longer-term paleoclimate research. For this purpose, we conducted a multi-disciplinary geophysical site survey along with initial sedimentological investigations. We used a combination of transient electromagnetic (TEM), seismic and magnetic data, that allows us to derive information with respect to tectonic features as well as the deeper sedimentary architecture of the Aurus clay pan. TEM and seismics were applied along two perpendicular transects. The data obtained reveal a three- to four-layered subsurface structure with a maximum sedimentary thickness between 31 and 65 m overlaying resistive bedrock. The aeromagnetic data show a fault zone cutting the southwestern corner of the clay pan that correlates with deep conductive anomalies visible in the TEM data. Radiocarbon ages of plant remains from the sediment surface evidence modern deflation, probably due to a particularly dry climate. The composition of a 4-m-long sediment core, which probably reflects the depositional history from 3400 to 23,000 cal. yr BP, however, suggests that most of the Holocene was characterized by a relatively high humidity, whereas the latest Pleistocene again was much dryer. According to an extrapolation of the sedimentation rates in the upper 4 m to the base of the sedimentary infill the record in Aurus clay pan penetrates in the order of 340,000 cal. yr BP. This would be longer than most other paleoclimate records in the Namib, but much less then recently recovered in the Atacama Desert in Chile. A comparison of the geophysical and geological data from Aurus with comparable data and two drill cores from clay pans in the Atacama furthermore shows that the physical parameter in all cases allow to decipher the sediment thickness and general architecture, but alone provides limited information on the sediment composition.

Technical note: High-density mapping of regional groundwater tables with steady-state surface nuclear magnetic resonance – three Danish case studies

Abstract. Groundwater is an essential part of the water supply worldwide, and the demands on this water source can be expected to increase in the future. To satisfy the need for resources and to ensure sustainable use of resources, increasingly detailed knowledge of groundwater systems is necessary. However, it is difficult to directly map groundwater with well-established geophysical methods as these are sensitive to both lithology and pore fluid. Surface nuclear magnetic resonance (SNMR) is the only method with a direct sensitivity to water, and it is capable of non-invasively quantifying water content and porosity in the subsurface. Despite these attractive features, SNMR has not been widely adopted in hydrological research, the main reason being an often-poor signal-to-noise ratio, which leads to long acquisition times and high uncertainty in terms of results. Recent advances in SNMR acquisition protocols based on a novel steady-state approach have demonstrated the capability of acquiring high-quality data much faster than previously possible. In turn, this has enabled high-density groundwater mapping with SNMR. We demonstrate the applicability of the new steady-state scheme in three field campaigns in Denmark, where more than 100 SNMR soundings were conducted with a depth of investigation of approximately 30 m. We show how the SNMR soundings enable us to track water level variations at the regional scale, and we demonstrate a high correlation between water levels obtained from SNMR data and water levels measured in boreholes. We also interpret the SNMR results jointly with independent transient electromagnetic (TEM) data, which allows us to identify regions with water bound in small pores. Field practice and SNMR acquisition protocols were optimized during the campaigns, and we now routinely measure high-quality data at 8 to 10 sites per day with a two-person field crew. Together, the results from the three surveys demonstrate that, with steady-state SNMR, it is now possible to map regional variations in water levels with high-quality data and short acquisition times.

Noise Characteristics and Denoising Methods of Long-Offset Transient Electromagnetic Method

The advantages of the long-offset transient electromagnetic method include deep detection and sensitive response to resistivity anomalies. It is widely used in underground mineral resources exploration, fluid identification in petroleum reservoirs, hydraulic fracturing, and dynamic residual oil and gas monitoring. After the primary field signal is turned off, grounded electrodes or coils are used to observe the secondary eddy field. The secondary field signal decays quickly and has a large dynamic range and a wide frequency band but is easily affected by various natural and human electromagnetic interferences. Therefore, noise reduction and distortion correction are important issues in the processing of transient electromagnetic data. This paper proposes a systematic noise interference suppression process. Multi-period and positive–negative bipolar signal stackings were used to remove random noise and suppress DC offset signals. Then, a time-domain inverse digital recursive method was applied to remove characteristic frequency signals, e.g., power frequency signals and their harmonic interference. A standard noise-free signal was constructed through forward modeling simulation and verified by adding different types of noise. Finally, high-quality transient electromagnetic secondary field attenuation signals were obtained through overlapping windowing technology. We applied this algorithm to obtain electromagnetic data from dynamic monitoring of hydraulic fracturing in Fuling shale gas and from a copper–iron metal mine in Daye City, demonstrating its effectiveness.

A method to construct statistical prior models of geology for probabilistic inversion of geophysical data

In probabilistic inversion of geophysical data, one must describe the expected noise in the system, and any prior information. In a geoscience context, prior information can provide a quantitative description of the expected spatial variability and correlations of the geology. But in practical inversion cases, driven by difficulty in quantifying geological information and computational complexity, an analytical mathematical smooth prior model is often chosen to describe the spatial variability. This is one of the primary reasons that realistic geological structures are difficult to resolve in geophysical models. Thus, there is currently a need for investigating and proposing practical ways of capturing complex (and often qualitative) geological information in statistical prior models that can be used in probabilistic inversion, which satisfies both the geologist, geophysicist, engineer and the geostatistician. In this research we show how a 1D statistical prior model can be designed that emulates the spatial distribution found in 188 boreholes with Miocene and Quaternary deposits from a study area (approx. 177,5 km2) near Horsens, Denmark. The prior model is built in two major steps 1) a multidimensional distribution describing the sub-division of major geological elements (here represented by lithologies from defined geological periods) and 2) a truncated pluri-Gaussian distribution describing the internal structure of lithologies within each element. The presented prior model can both be used to generate independent realizations, which can be used as part of the extended rejection sampler, as well as allowing the possibility of doing a “random walk” in the model space, as required by the extended Metropolis algorithm. We demonstrate, as an example, how the developed prior model can be used in a probabilistic inversion of airborne transient electromagnetic (AEM) data and discuss the implications the use of such informed prior models can have.

Implicit modelling and dynamic update of tunnel unfavourable geology based on multi-source data fusion using support vector machine

ABSTRACT Three-dimensional unfavourable geology models with complex structures and various attributes have become crucial for optimal design and risk control during tunnel construction. In practical applications, it is necessary to integrate multi-source advanced prediction data, including tunnel seismic prediction data, geological radar data, and transient electromagnetic data, to perform dynamic model construction. However, due to the implicit representation of the spatial distribution of single-source data and the heterogeneity of multi-source data, existing methods mainly rely on manual interpretation to perform comprehensive analysis, causing an increase in data uncertainty and unreliable, inaccurate modelling results. Therefore, this study proposes a dynamic implicit modelling method of tunnel unfavourable geology based on multi-source data fusion using a support vector machine (SVM). This method uses the SVM to fuse multi-source data and output unfavourable geological categories, including faults, fracture zones, water-rich areas, and weak rock masses, represented as spatially continuous unfavourable geological points. A globally supported radial basis function combined with a Boolean implicit calculation is used for model construction and local adaptive update. Experiments were implemented in a deep-buried tunnel, and by comparing the results with the realistic status throughout the excavation, the accuracy and adaptive ability of the proposed modelling method were well proven.

Improved adaptive thin-layer inversion for semi-airborne transient electromagnetic method

ABSTRACT To improve the resolution of electromagnetic inversion for thin layers, electromagnetic one-dimensional inversion was studied. The smooth conductivity model produced by Occam's inversion cannot accurately represent the information of the subterranean thin resistive layers, leading to erroneous inversion findings. The existing thin resistive layers’ inversion method sets the model constraint term at the thin resistive layers to 0, resulting in abrupt changes in resistivity values. Given these problems, we proposed an adaptive roughness matrix calculation method to improve the thin, low-resistive-layer resolution. The resistivity difference between neighboring layers of the updated inversion model determines the roughness matrix, allowing for the realization of adaptive inversion of the thin layer. It achieves semi-airborne transient electromagnetic enhanced adaptive thin-layer inversion and automatically manages the model constraint term. The calculation of the synthetic model demonstrates that the improved adaptive thin-layer inversion method does not need to know the thin, low-resistive layers information in advance. The model can produce appropriate inversion results regardless of the presence of thin, low-resistive layers. Finally, the drilling results are consistent with the inversed appearance of the semi-airborne transient electromagnetic field data. Other geophysical adaptive thin resistive layers inversion can also benefit from this paper's findings.

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.

One-dimensional full-waveform inversion for magnetic induction data in ground-based transient electromagnetic methods

Abstract The full-waveform effects refer to the total effects of turn-on, steady and turn-off durations for a transmitting-current waveform as well as its repetition number in transient electromagnetic (TEM) methods. In this study, the full-waveform effects are investigated using both forward-modeling and inversion methods considering typical background noise. The forward-modeling results of homogeneous half-space models show that the magnetic induction, bz, is less affected by the background noise but more affected by the full-waveform effects than the time derivative of magnetic induction ∂bz/∂t. Therefore, this study focuses on investigating the full-waveform effects on bz. The inversion results for synthetic and field examples show that the inversion algorithm without considering the full-waveform effects leads to over-estimated resistivities in deeper parts of the recovered models compared to the true model. As a result, it is crucial to consider the full-waveform effects when processing TEM data. Furthermore, a standard deviation factor (STDF) is estimated for model parameters of the inversion. The results show that the STDF increases as the layer depth increases for 1D layered models. This indicates that the inverted parameters are well resolved for shallower layers and moderately to poorly resolved for deeper layers.

CG-DAE: a noise suppression method for two-dimensional transient electromagnetic data based on deep learning

Abstract The transient electromagnetic method (TEM) is a geophysical exploration method that can efficiently acquire subsurface electrical parameters. For airborne, towed and other mobile platforms, TEM systems, large data volumes and the traditional one-dimensional denoising method with low efficiency and low signal-to-noise ratio (SNR) of late-time are the main bottlenecks limiting its reliable application. To address this problem, this paper proposes a neural network structure suitable for two-dimensional (2D) TEM data processing. The proposed structure combines a classical convolutional neural network denoising autoencoder with a gated recurrent neural network autoencoder, called the CNN-GRU dual autoencoder (CG-DAE). This method can directly input 2D TEM response data as images into the network for processing, which greatly improves data processing efficiency compared to single-time-channel processing. The simulation experiments verified the effectiveness of CG-DAE. After using CG-DAE denoising, the SNR of late-time (0.2–1 ms) signals is improved to nearly 29 dB, the 2D anomaly layer position is clear and the relative error (RE) between the denoised data and the corresponding clean data is <1.41%, while the RE of the late-time signals can be reduced to 3.68%. The proposed method can lay the foundation for fast processing of TEM data based on mobile platforms such as those that are airborne and/or towed.

High-resolution imaging of a coal seam based on quasi-2D TEM inversion

In a sedimentary environment, the conventional one-dimensional (1D) inversion based on the horizontal layered model has difficulty restoring the resistivity distribution of the inclined strata when a coal seam has some dip angle or a small interval between layers. In such cases, the inversion resistivity exhibits horizontal discontinuities, which cannot accurately represent actual geological conditions. Therefore, in view of the good horizontal continuity of the underground electrical structure of sedimentary strata, we propose a high-resolution inversion method based on weighted horizontal and vertical constraints. As a quasi-two-dimensional (2D) inversion, this not only ensures the horizontal continuity of resistivity and recovers the inclined strata, but also improves the vertical resolution. Because the constrained factor has a significant influence on the inversion result, different constrained factors are applied in the horizontal and vertical directions to adjust the constraint strength on the model parameters of each layer and the continuity of the layer interface. In the numerical experiments, we design synthetic models with different tilt angles and layer spacings to test the inversion method and optimize the constrained factors used for coal seam detection. Finally, the transient electromagnetic (TEM) field data processing results in Inner Mongolia show that the resistivity distributions of sedimentary strata can be accurately restored by the new method, and the inversion results are consistent with known geological information.

Machine-learning inversion via adaptive learning and statistical sampling: Application to airborne micro-TEM for seismic sand corrections

We develop a novel physics-adaptive machine-learning (ML) inversion scheme showing optimal generalization capabilities for field data applications. We apply the physics-driven deep-learning inversion to a massive helicopter-borne transient electromagnetic (TEM) field data set. The objective is the accurate modeling of the near surface for enhancing the exploration of low-relief structures in a sand covered desertic area. Enhanced generalization of a neural network (NN) or other ML techniques is obtained from automatic physics-based adaptive training using images of real-world data. Data reduction schemes, based on statistical sampling techniques, enable the use of small and fully informative data sets for accelerated training. Adaptive learning is implemented via an iterative physics-driven data augmentation strategy. A deterministic inversion is regularized by a penalty term built from the difference between the inverted models and the models predicted by ML. The resulting inverted models and calculated responses are used for augmenting the ML training base. The automated procedure converges rapidly, producing a trained network model, which captures the general background data statistics as well as the characteristics of field-specific geophysical data as embedded in the augmented samples. The high-resolution ML inversions with acceptable levels of data misfit are obtained for large volumes of the field geophysical data. We test several different learning models, such as artificial NNs, convolutional NN (U-Net), and the Gaussian process, which provide stable and comparable results with network-specific characteristics. The developed scheme is successfully applied to micro-TEM data providing accurate near-surface seismic corrections for the exploration of low-relief structures.

Inversion of TEM measurement data via a quantum particle swarm optimization algorithm with the elite opposition-based learning strategy

The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particle swarm optimization (PSO) nonlinear inversion methods suffer from prematurity, slow convergence, and low calculation accuracy. To solve these problems, a quantum PSO (QPSO) algorithm based on the elite opposition-based learning (EOL) strategy is proposed. Firstly, three performances tests of the EOL-QPSO algorithm are carried out with Peaks, Schaffer and Rastrigin functions. The results show that the EOL-QPSO algorithm has excellent solution accuracy, efficient calculation speed and balanced exploitation and exploration capability. Secondly, the conventional PSO algorithm and the EOL-QPSO algorithm are used to compare the inversion of the theoretical model and the synthetic data with noise, and combined with Bayesian method, the posterior model probability statistics of the synthetic data are carried out. The research shows that the EOL-QPSO inversion algorithm is improved in terms of calculation accuracy, calculation efficiency, anti-noise performance and exploitation and exploration capability, and it can accurately obtain the posterior estimates of the real model. Finally, the inversion of field-measured data demonstrates that the EOL-PSO inversion method accurately reflects the position of the water-accumulated goaf.