Electrical Tomography Research Articles

Assessment of Near-Surface Geophysical Methods Used to Discover Karst Bauxite Deposits in the Dinarides Using the Example of Posušje Area, Bosnia and Herzegovina

Geophysical exploration of bauxite deposits has been carried out in the area of Posušje in Bosnia and Herzegovina, which were formed on an Upper Cretaceous carbonate substrate, whereas the hanging wall rocks can be Paleogene limestones and sedimentary clastic rocks. Karst terrains are demanding for geophysical exploration due to the relatively complex geological relationships and exceptional near-surface inhomogeneities that generate large noises and challenging conditions for taking field measurements. The fundamental question is whether geophysical research can detect exceptionally irregular karst bauxite deposits with relatively small dimensions. The basic idea is to combine several geophysical methods and a joint interpretation of several data sets to increase the efficiency of geophysical surveying in detecting complex bauxite deposits in karst terrains. Therefore, fundamental near-surface research methods, electrical tomography and seismic refraction are used. In addition, magnetometry was used to examine whether bauxite deposits yield potential magnetic anomalies that could help in detecting them. Research undertaken in the area of Posušje was carried out in the first step on already discovered and known bauxite deposits to determine whether geophysical responses correlate with the occurrence of bauxite deposits and to evaluate the effectiveness of each of the applied surface geophysical methods. Measurements were taken at several locations, and results for two micro-locations, Krstače and Mratnjača, are shown. Geophysical measurements were firstly performed on discovered bauxite deposits in order to reliably determine the possibility of identifying deposits in geophysical inverse models. Bauxite deposits were clearly recognised as characteristic geophysical responses in inversion models using both methods, electrical tomography and seismic refraction. Although the response of bauxite deposits is expressed in both models, resistivity and velocity, it is much more evident in resistivity models. The characteristic resistivity response was confirmed by the discovery of a new deposit. Therefore, the conclusion is that electrical resistivity tomography should be considered a basic method for exploring karst bauxite deposits. Seismic refraction provides a better characterisation of deposits and reduces the interpretation ambiguity. This solution can generally be applied to the problem of researching bauxite deposits in the Dinarides and similar geological models in the Mediterranean. Magnetometric measurements have shown that no magnetic anomalies could be associated with bauxite deposits, and only magnetometry was not successful in discovering bauxite deposits.

Insights Into the Structure of the Mongol‐Okhotsk Suture Zone, Adaatsag Ophiolite, and Tectonic Boundaries of the Central Asian Orogenic Belt (Mongolia) From Electrical Resistivity Imaging and Seismic Velocity Models

AbstractThe Mongol‐Okhotsk suture and the Adaatsag ophiolite belt are associated with the closure of the Mongol‐Okhotsk paleo‐ocean and are located within the Central Asian Orogenic Belt (CAOB) and Mongolia. The suture zone is flanked by volcanic‐plutonic belts that host significant metallogenic zones, containing deposits of copper and gold. The tectonic evolution of this region is not fully understood and the lithospheric structure has been poorly studied. We analyze magnetotelluric data and generate a model of the electrical resistivity distribution across this region. Whereas the northern segment has a sharp transition from a high‐resistivity upper crust to a low‐resistivity lower crust, as observed beneath the Hangai Dome, the southern segment does not show this transition. A wide, low‐resistivity zone (1–100 Ωm) imaged in the crust and lithospheric mantle is coincident with the Mongol‐Okhotsk suture and ophiolite, revealing a clear and significant lithospheric‐scale feature. Across the profile, numerous narrow, vertically oriented, low‐resistivity features (1–100 Ωm) are spatially associated remarkably well with the proposed boundaries of tectonic domains. These results confirm ideas about the development of the CAOB. Some of these low‐resistivity features are beneath the surface locations of large mineral zones, and likely represent fossil fluid pathways. We show congruent seismic velocity models for comparison and the results show a large‐scale low‐velocity anomaly (decrease of 2%–3%) that correlates with the location of the low‐resistivity anomaly below the Mongol‐Okhotsk suture. The geophysical results, combined with geological and geochemical data, provide insights into the structure of this region and help shed light on unanswered questions.

TSS-ConvNet for electrical impedance tomography image reconstruction

Objective. The objective of this study was to propose a novel data-driven method for solving ill-posed inverse problems, particularly in certain conditions such as time-difference electrical impedance tomography for detecting the location and size of bubbles inside a pipe. Approach. We introduced a new layer architecture composed of three paths: spatial, spectral, and truncated spectral paths. The spatial path processes information locally, whereas the spectral and truncated spectral paths provide the network with a global receptive field. This unique architecture helps eliminate the ill-posedness and nonlinearity inherent in the inverse problem. The three paths were designed to be interconnected, allowing for an exchange of information on different receptive fields with varied learning abilities. Our network has a bottleneck architecture that enables it to recover signal information from noisy redundant measurements. We named our proposed model truncated spatial-spectral convolutional neural network (TSS-ConvNet). Main results. Our model demonstrated superior accuracy with relatively high resolution on both simulation and experimental data. This indicates that our approach offers significant potential for addressing ill-posed inverse problems in complex conditions effectively and accurately. Significance. The TSS-ConvNet overcomes the receptive field limitation found in most existing models that only utilize local information in Euclidean space. We trained the network on a large dataset covering various configurations with random parameters to ensure generalization over the training samples.

Avoidance of axonal stimulation with sinusoidal epiretinal stimulation

Objective. Neuromodulation, particularly electrical stimulation, necessitates high spatial resolution to achieve artificial vision with high acuity. In epiretinal implants, this is hindered by the undesired activation of distal axons. Here, we investigate focal and axonal activation of retinal ganglion cells (RGCs) in epiretinal configuration for different sinusoidal stimulation frequencies. Approach. RGC responses to epiretinal sinusoidal stimulation at frequencies between 40 and 100 Hz were tested in ex-vivo photoreceptor degenerated (rd10) isolated retinae. Experiments were conducted using a high-density CMOS-based microelectrode array, which allows to localize RGC cell bodies and axons at high spatial resolution. Main results. We report current and charge density thresholds for focal and distal axon activation at stimulation frequencies of 40, 60, 80, and 100 Hz for an electrode size with an effective area of 0.01 mm2. Activation of distal axons is avoided up to a stimulation amplitude of 0.23 µA (corresponding to 17.3 µC cm−2) at 40 Hz and up to a stimulation amplitude of 0.28 µA (14.8 µC cm−2) at 60 Hz. The threshold ratio between focal and axonal activation increases from 1.1 for 100 Hz up to 1.6 for 60 Hz, while at 40 Hz stimulation frequency, almost no axonal responses were detected in the tested intensity range. With the use of synaptic blockers, we demonstrate the underlying direct activation mechanism of the ganglion cells. Finally, using high-resolution electrical imaging and label-free electrophysiological axon tracking, we demonstrate the extent of activation in axon bundles. Significance. Our results can be exploited to define a spatially selective stimulation strategy avoiding axonal activation in future retinal implants, thereby solving one of the major limitations of artificial vision. The results may be extended to other fields of neuroprosthetics to achieve selective focal electrical stimulation.

Geophysical and Sedimentological Characterization of Oil Sand Deposit in Part of Eastern Dahomey Basin Southwestern Nigeria

Unconventional oil is the group name given to unconventional petroleum deposits such as oil sands and heavy oils. Oil sand, commonly referred to as tar sand or bituminous sand is a sandstone formation impregnated with viscous bitumen. This study utilized the two-dimensional electrical resistivity imaging technique to characterize oil sand and validate its true resistivity value and sedimentological analysis to evaluate the physical properties of the oil sand deposit. The two-dimension electrical resistivity imaging data acquisition was done along seven profiles systematically in a grid format using dipole-dipole electrode configuration and processed using the RES2DINV software. Fresh samples were collected from three points within the study area for grain sizes and textural properties analysis. The result of the 2-D ERI in the study area showed two to three geoelectric layers and the oil sand being the second layer as evident in the 3-D model obtained from the systemic data acquisition procedure. The resistivity obtained for oil sand in the study area range between 2,500 Ωm to 105 Ωm which validates the report of high resistivity value of oil sand in the study area. The sedimentological analysis showed that the sediments in the study area are of medium sand moderately sorted sand grain and leptokurtic to mesokurtic sand suggesting unimodal and bimodal parent source.

Beyond the Marketing Hype: Analysis of Communication Campaigns of Electric Vehicles Eco-Friendly Image and Battery Waste Concerns

Electric Vehicles (EVs) are gaining widespread attention for their environmentally friendly nature and potential to reduce air pollution. As non-renewable fossil fuels deplete, the need for sustainable alternatives like EVs becomes imperative. The EV market emphasizes the cost-effectiveness of electric fuel and extended travel range, despite the initial higher cost of purchasing an EV. In Indonesia, the government encourages EV adoption through subsidies and improved electric refuelling infrastructure, including exempting EV users from the odd-even vehicle license plate rule in Jakarta.
 To promote EV adoption, brands employ diverse marketing strategies, urging customers to embrace EVs as agents of environmental change. However, the lack of transparency in how EV brands handle nickel-based battery waste, with a limited lifespan, raises environmental concerns. The accumulation of unusable batteries could pose soil pollution risks as EV usage grows. This research aims to explore the environmental implications of battery waste from EVs in Indonesia and analyze how EV brands address these concerns in their marketing communication campaigns.
 Adopting a cybernetic approach, the study examines the interconnectedness of marketing communication and consumer buying decisions. Qualitative research is utilized, drawing on various EV studies, facts about EV brand communication, and interviews with experts to provide a comprehensive analysis. Although battery waste might not be a pressing issue in Indonesia currently, inadequate waste handling raises potential future concerns about soil and water contamination. Addressing battery waste responsibly is crucial for promoting sustainable EV adoption.

Geoelectric model of the exposure of basement rocks to the surface in the Uimon depression of Gorny Altay based on three-dimensional numerical modeling

In the western part of the Uimon depression of the Altai Mountains, during the 2023 field season, additional measurements were carried out using electrical tomography along three profiles to clarify the structure of the area with basement rocks exposed to the surface. During the study, the results of three-dimensional inversion were obtained taking into account new electrical tomography data, and a refined geoelectric model of the study area was constructed. To verify the resulting model, 2D and 3D numerical modeling was performed with the help of which the geometric dimensions and electrical resistivity of bedrock outcrops were justified. The influence of the daytime surface topography on geoelectric sections in mountainous regions with significant differences in elevation on the earth's surface has been shown. Determining the structure of the depressions is necessary to understand the stages of neotectonic formation of the Uimon depression and the entire region, as well as to assess the seismic hazard of the Altai Mountains. In the course of further research, it is possible to trace the object to a greater depth (~100 m) using a three-electrode electrical tomography installation or alternative equipment with an increased size of the measuring-generator cable.

Experiments and predictions for severe slugging of gas-liquid two-phase flows in a long-distance pipeline-riser system

Experiments concerning the flow regimes observed in gas-liquid two-phase flows in a long-distance pipeline-riser system were carried out and analyzed in this paper. The test loop with 0.075 m inner diameter consisted of a 314 m horizontal pipeline, followed by a 25 m downward-inclined pipe, and finally a 21.5 m riser pipe. An industrial-scale global flow regime map including 5 flow regimes was derived from differential pressure measurements in the riser and was used as a reference for improvements. Electrical capacitance volume tomography installed in the horizontal section was used to reconstruct the phase interface and calculate the average gas void fraction (GVF). An appropriate model for this system was selected by comparing the experimental GVF with different, widely used models. The occurrence of severe slugging flow (SS) can be predicted accurately using an improved model involving additional corrections that consider pipeline length, accurate GVF calculation, and gas density. This study supports the development of a suitable SS prediction model enabling safe design, operation, and control of practical systems.

Research on quantitative inversion characterization of high-definition electrical imaging logging in oil-based mud based on backpropagation neural network and multiple population genetic algorithm-Levenberg-Marquardt algorithm

Electrical imaging logging in oil-based mud (OBM) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multifrequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a backpropagation neural network (BPNN) and the multiple population genetic algorithm (MPGA)-Levenberg-Marquardt (LM) algorithm for high-resolution quantitative imaging is developed. First, the numerical simulation is used to calculate the well-logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of more than 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures, etc.

All-electrical skyrmionic magnetic tunnel junction.

Topological whirls or 'textures' of spins such as magnetic skyrmions represent the smallest realizable emergent magnetic entities1-5. They hold considerable promise as robust, nanometre-scale, mobile bits for sustainable computing6-8. A longstanding roadblock to unleashing their potential is the absence of a device enabling deterministic electrical readout of individual spin textures9,10. Here we present the wafer-scale realization of a nanoscale chiral magnetic tunnel junction (MTJ) hosting a single, ambient skyrmion. Using a suite of electrical and multimodal imaging techniques, we show that the MTJ nucleates skyrmions of fixed polarity, whose large readout signal-20-70% relative to uniformly magnetized states-corresponds directly to skyrmion size. The MTJ exploits complementary nucleation mechanisms to stabilize distinctly sized skyrmions at zero field, thereby realizing three non-volatile electrical states. Crucially, it can electrically write and delete skyrmions to both uniform states with switching energies 1,000 times lower than the state of the art. Here, the applied voltage emulates a magnetic field and, in contrast to conventional MTJs, it reshapes both the energetics and kinetics of the switching transition, enabling deterministic bidirectional switching. Our stack platform enables large readout and efficient switching, and is compatible with lateral manipulation of skyrmionic bits, providing the much-anticipated backbone for all-electrical skyrmionic device architectures9,10. Its wafer-scale realizability provides a springboard to harness chiral spin textures for multibit memory and unconventional computing8,11.

Dynamic bubble tracking in fluidized beds via electrical capacitance volume tomography

This work sets forth the development of a novel, dynamic bubble detection algorithm for use with electrical capacitance volume tomography (ECVT) sensors for bubbling fluidized beds. Starting with an in-depth review of existing bubble detection methods, the novel phase detection method was developed to address the shortcomings of the other published methods by more fully utilizing the three-dimensional capability of the sensors. After the sensor parameters were optimized to verify capability of bubble detection, measurements were made with glass beads and quartz sand. A range of air velocities in a 10 cm diameter bubbling fluidized bed were used. The results largely agree with the fundamentals of bubbling fluidization and results from publications with similar experimental setups. Within each measurement the bubble dimensions, velocities and frequencies over the axial and radial position could be evaluated. Due to the three-dimensional nature of the novel bubble detection technique, insight into the directional tendencies of detected bubbles was gained. For example, bubble migration toward the radial center of the bed, radial and axial bubble coalescence, and splitting are more evident from the trends that are producible with this method.

Joint focusing method based on optimal image-distance interval for multi-image-distance imaging system

The multi-image-distance imaging system captures multiple images simultaneously with different focused depths, and uses image fusion to obtain an all-in-focus image for extending the depth of field (DoF). However, an image-distance adjustment strategy that takes into account the optimal DoF extension capability and imaging quality remains to be explored. In this paper, we propose a joint focusing method based on the optimal image-distance interval between the three sensors. First, we propose multi-sensor image-distance adjustment control strategies for different scenes. Then, theoretical analyses and calibration experiments of the optimal image-distance interval are conducted for continuous depth scenes. In order to quantitatively evaluate the DoF extension performance of the imaging system, a DoF estimation method based on point spread function measurement is proposed. Additionally, an evaluation metrics for extended DoF imaging based on the range and continuity of DoF is presented. We have developed a multi-image-distance electric focusing imaging system. Imaging experiments are conducted in various scenes to verify the effectiveness of the joint focusing method. The joint focusing method, based on the optimal image-distance interval, provides an image-distance adjustment strategy to achieve high-quality extended DoF imaging for multi-image-distance imaging systems.

Increased extra-neurite conductivity of brain in patients with Alzheimer's disease: A pilot study

The objectives of this study were to investigate how the extra-neurite conductivity (EC) and intra-neurite conductivity (IC) were reflected in Alzheimer's disease (AD) patients compared with old cognitively normal (CN) people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between those conductivity values and cognitive decline. To do this, high-frequency conductivity (HFC) at the Larmor frequency was obtained using MRI-based electrical property tomography (MREPT) and was decomposed into EC and IC using information of multi-shell multi-gradient direction diffusion tensor images. This prospective single-center study included 20 patients with mild or moderate AD, 25 patients with amnestic MCI, and 21 old CN participants. After decomposing EC and IC from HFC for all participants, we performed voxel-based and regions-of-interest analyses to compare conductivity between the three participant groups and to evaluate the association with either age or the Mini-Mental State Examination (MMSE) scores. We found increased EC in AD compared to CN and MCI. EC was significantly negatively associated with MMSE scores in the insula, and middle temporal gyrus. EC might be used as an imaging biomarker for helping to monitor cognitive function.

MR-based electrical property tomography using a physics-informed network at 3 and 7 T.

Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of and by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.

High-density electric source imaging patterns and outcomes following temporal lobectomy in patients with hippocampal sclerosis.

The objective of this study was to ascertain specific patterns of electrical source imaging (ESI) that are associated with a good surgical outcome (no seizure recurrence) using 256-channel high-density (HD) electroencephalography (EEG) in patients with temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) who underwent temporal lobectomy. Adult patients (age ≥ 18 years) were prospectively recruited from September 2016 to May 2020 at the authors' center. All patients underwent phase I presurgical evaluation and were subsequently advised to proceed with surgery based on consensus from a multidisciplinary epilepsy conference, without knowing HD-ESI results. All recruited patients were followed for at least 12 months after surgery. The outcome of interest was a status of no seizure recurrence, which was assessed at the end of the study. The association between ESI patterns and outcome was assessed using the chi-square or Fisher exact test. Associated p values as well as odds ratios and 95% CIs were reported. The diagnostic performance of the significant pattern associated with the outcome was also evaluated. Fifty-eight patients with known predictors for either good or worse surgical outcomes were recruited. The mean postoperative follow-up period was 33.34 (SD 13.88) months. Forty-seven patients had sufficient interictal epileptiform discharges for HD-ESI analysis. Thirteen of these 47 patients experienced seizure recurrence. The most common source localizations were at Brodmann area (BA) 20 (inferior temporal area) and BA 21 (middle temporal area). A specific ESI pattern of BA 21 without extratemporal sources was significantly associated with no seizure recurrence (p = 0.047). This pattern had a high positive predictive value of 100% and false-positive rate of 0% associated with no seizure recurrence following the surgery. A specific ESI pattern that was highly associated with no seizure recurrence following surgery was demonstrated by a 256-channel HD-EEG. If this pattern can be reproducibly proven in further studies, some TLE-HS patients may be able to proceed with surgery without further investigations.

CBR Mapping Prediction from 2D Resistivity Imaging Using Regression Following Archie’s Formula

Indirect geophysical methods are increasingly associated with direct underground methods in investigating the subsurface to address environmental and geotechnical problems and reduce the cost of underground studies. These methods make it possible to explore large areas with acceptable precision, time, and cost. The present study combines the electrical resistivity method with the California Bearing Ratio for exploring an area located west of the Nouakchott port in Mauritania. The study aims firstly to verify that the thickness of the backfill layer must be less than 2 m throughout the entire study area and secondly to map areas with low California Bearing Ratio values (<80%) indicating substandard backfill compaction using 2D Electrical Resistivity Tomography imaging survey and regression. The measured Electrical Resistivity values exhibit a good nonlinear regression with California Bearing Ratio, following Archie's equation. A map of California Bearing Ratio variation was derived from the Electrical Resistivity values, indicating the distribution and variation of soil strength in the study area. The results revealed that the backfill layer did not meet the standards, with approximately 35% of the total area having a California Bearing Ratio value below 80%. The areas with poor compaction requiring treatment were primarily located in the southern sector, followed by the middle and eastern sectors.

Task Scheduling Optimization for UAV Electrical Image Fault Detection with Wireless Power Transfer

The smooth operation of the power system is of significant importance for ensuring societal stability. Therefore, regular inspections of the power system are necessary. The most efficient method for power inspection is to utilize Unmanned Aerial Vehicles (UAVs) to assist inspection. However, the UAV inspection still needs human resources to operate and manage the UAVs effectively. This paper proposes a UAV-airport power inspection system, where the airport can operate and manage UAVs instead of humans. The key contribution of the system is the computation offloading technology and the Wireless Power Transfer (WPT) capability. In other words, the airport can help UAVs execute electrical image fault detection and the UAVs can be charged by the airport. We propose a joint optimization strategy for UAV flight speed, local execution frequency for fault detection, and WPT time to maximize the number of images in the electrical dataset captured by the UAV that have undergone power fault detection within the inspection time. Comprehensive experiments are conducted to verify the performance of the UAV-airport power inspection system and our optimization strategy.

Application of electrical impedance tomography imaging technology combined with generative adversarial network in pulmonary ventilation monitoring

Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.

CGAN-ECT: Reconstruction of Electrical Capacitance Tomography images from capacitance measurements using Conditional Generative Adversarial Networks

Due to the rapid growth of applications of Electrical Capacitance Tomography (ECT) across diverse industrial sectors, there is a pressing demand for swift and high-fidelity methodologies for image reconstruction from raw capacitance measurements. Deep learning, as an effective non-linear mapping tool for complicated functions, has been going viral in many fields including electrical tomography. This paper introduces a pioneering contribution by proposing a Conditional Generative Adversarial Network (CGAN) model explicitly tailored for the purpose of reconstructing ECT images from capacitance measurements. We propose a novel modulator to transform the raw input capacitance measurements into a 2D image. To the best of our knowledge, this is the first time to represent the capacitance measurements in an image form. We have created a new massive ECT dataset of synthetic image-measurements pairs for training and testing purposes of the proposed model. This extensive ECT dataset comprises 320,000 synthetic image-measurement pairs. It forms the basis for evaluating the feasibility and generalizability of the CGAN-ECT model, with assessments conducted on testing data, contaminated data, and flow patterns which are intentionally excluded from the model’s training phase. The evaluation results prove the efficacy of the proposed CGAN model in generating ECT images with heightened accuracy compared to traditional methods and alternative deep learning-based image reconstruction algorithms. The CGAN-ECT model has succeeded in a achieving an average image correlation coefficient exceeding 99.3%, coupled with an average relative image error of approximately 0.07.

Detection Method and Application of Nuclear-Shaped Anomaly Areas in Spatial Electric Field Power Spectrum Images

It is found that there are some anomalous high-energy nuclear-shaped regions in the VLF frequency band of the space electric field. To detect and statistically analyze these nuclear-shaped anomaly areas, this paper proposes a nuclear-shaped anomaly area detection method based on the electric field power spectrum image data of the China Seismo Electromagnetic Satellite (CSES-01). First, the logarithm of VLF frequency band data was calculated and rotated counterclockwise to create power spectrum images and label them to form a sample image dataset; then, images were enhanced (which involved resizing, scaling, rotation, gaussian denoising, etc.) to solve the problems of the model overfitting and sample imbalance. Finally, the U-net network model based on the ResNet50 encoder was trained to obtain the optimal kernel anomaly detection model ResNet50_Unet. Comparative experiments with various semantic segmentation algorithms show that the ResNet50_Unet model has the best performance. Applying this model to detect the electric field power spectrum images from November 2021 to February 2022, a total of 101 nuclear-shaped anomaly areas were found, distributed between 45° and 70° of the north–south latitude. This model can quickly detect nuclear-shaped anomaly regions from massive data, providing reference significance for the detection of other types of ionospheric spatial disturbances. At the same time, it has important scientific significance and practical value for understanding the ionosphere and space communication.