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Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO2 saturation. The network is trained on pairs of CO2 label models and multiphysics data so that it can directly image CO2 saturation. We use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO2 saturation images. Using realistic CO2 label models and multiphysics data derived from the Kimberlina CO2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

Quantitative interpretation of the data from controlled‐source electromagnetic methods, whether via forward modelling or inversion, requires solving a considerable number of forward problems, and multigrid methods are often employed to accelerate the solving process. In this study, a new extrapolation cascadic multigrid method is employed to solve the large sparse complex linear system arising from the finite element approximation of Maxwell's equations using secondary potentials. The equations using secondary potentials are discretized by the classic nodal finite element method on nonuniform rectilinear grids. The resulting linear systems are solved by the extrapolation cascadic multigrid method with a new prolongation operator and preconditioned Stabilized bi‐conjugate gradient method smoother. High‐order interpolation and global extrapolation formulas are utilized to construct the multigrid prolongation operator. The extrapolation cascadic multigrid method with the new prolongation operator is easier to implement and more flexible in application than the original one. Finally, several synthetic examples including layered models, models with anisotropic anomalous bodies or layers, are used to validate the accuracy and efficiency of the proposed method. Numerical results show that the extrapolation cascadic multigrid method improves the efficiency of 3D controlled‐source electromagnetic forward modelling a lot, compared with traditional iterative solvers and some state‐of‐the‐art methods or software (e.g., preconditioned flexible generalized minimal residual method, emg3d) in the considered models and grid settings. The efficiency benefit is more evident as the number of unknowns increases, and the proposed method is more efficient at low frequencies. The extrapolation cascadic multigrid method can also be used to solve systems of equations arising from related applications, such as induction logging, airborne electromagnetic, etc. [ABSTRACT FROM AUTHOR]

Recent advances in metadevices, featuring complex subwavelength metastructures, have dramatically transformed the control and manipulation of electromagnetic waves. However, the inherently narrow operational bandwidth of these devices, stemming from their wavelength‐specific meta‐atoms, restricts their application in rapidly advancing fields such as the Internet of Things and advanced intelligent systems. Here, a novel hyperband synergistic metadevice is introduced, realized through a comprehensive multi‐scale meta‐atom architecture. The complementary metal‐oxide‐semiconductor (CMOS)‐compatible prototype integrates the distinct properties of double‐walled carbon nanotubes with advanced interlayer and intralayer coupling mechanisms, coherently combining nanoscale, microscale, and macroscale meta‐atoms. This prototype is thus adept at operating across a wide electromagnetic spectrum, spanning from the centimeter‐wavelength microwave band to the hundred nanometer‐wavelength visible and infrared optical band. Significantly, this singular device synergistically delivers three critical functionalities: selective microwave absorption, efficient terahertz beam steering, and enhanced optical transparency. These result signifies a breakthrough in hyperband electromagnetic device engineering, leading to compact, versatile, intelligent electromagnetic platforms. [ABSTRACT FROM AUTHOR]

In transient electromagnetic surveys, the collected data inevitably contain noise originating from both natural and cultural sources. This noise has the potential to mask transient electromagnetic responses linked to geological features, thereby posing challenges in accurately interpreting subsurface structures. Hence, the implementation of effective noise reduction techniques is crucial in ensuring the accuracy and reliability of inversion outcomes in transient electromagnetic surveys. This study introduces a novel approach that merges k‐means clustering with locally weighted linear regression to denoise transient electromagnetic data. The results from synthetic examples illustrate that the k‐means locally weighted linear regression method can predict transient electromagnetic data closely resembling true values, similar to the long short‐term memory autoencoder. Occam's inversion results derived from denoised data using both the k‐means locally weighted linear regression and long short‐term memory–autoencoder methods can well reflect the true model. Notably, a key advantage of the k‐means locally weighted linear regression method is its independence from labelled data as the sample set. The k‐means locally weighted linear regression method was applied to field data collected at the Narenbaolige coalfield in Inner Mongolia, China. Occam's inversion models generated from the denoised field data delineate the boundary between the basaltic body and sedimentary rocks, aligning with drilling data. The inversion models derived from the noisy field data also can capture this boundary, but deep section views reveal the presence of numerous intricate high‐resistivity anomalous bodies. These observations highlight the effectiveness of the k‐means locally weighted linear regression method in denoising transient electromagnetic data. [ABSTRACT FROM AUTHOR]

This paper explores the application of machine learning techniques, specifically deep learning, to the inverse problem of marine controlled‐source electromagnetic data. A novel approach is proposed that combines the convolutional neural network and recurrent neural network architectures to reconstruct layered electrical resistivity variation beneath the seafloor from marine controlled‐source electromagnetic data. The approach leverages the strengths of both convolutional neural network and recurrent neural network, where convolutional neural network is used for recognizing and classifying features in the data, and recurrent neural network is used to capture the contextual information in the sequential data. We have built a large synthetic dataset based on one‐dimensional forward modelling of a large number of resistivity models with different levels of electromagnetic structural complexity. The combined learning of convolutional neural network and recurrent neural network is used to construct the mapping relationship between the marine controlled‐source electromagnetic data and the resistivity model. The trained network is then used to predict the distribution of resistivity in the model by feeding it with marine controlled‐source electromagnetic responses. The accuracy of the proposed approach is examined using several synthetic scenarios and applied to a field dataset. We explore the sensitivity of deep learning inversion to different electromagnetic responses produced by resistive targets distributed at different depths and with varying levels of noise. Results from both numerical simulations and field data processing consistently demonstrate that deep learning inversions reliably reconstruct the subsurface resistivity structures. Moreover, the proposed method significantly improves the efficiency of electromagnetic inversion and offers significant performance advantages over traditional electromagnetic inversion methods. [ABSTRACT FROM AUTHOR]

Holographic multiple-input multiple-output (HMIMO) systems constitute a promising technology in support of next-generation wireless communications, thus paving the way for a smart programmable radio environment. However, despite their significant potentials, further fundamental issues remain to be addressed, such as the acquisition of accurate channel information. Indeed, the conventional angular-domain channel representation is no longer adequate for characterizing the sparsity inherent in HMIMO channels. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of research conducted on the Method of Auxiliary Sources (MAS) over a period of the last 22 years, i.e., since the last up-to-date survey was published. MAS is a very attractive numerical technique due to its simple algorithmic structure and the generally low computational cost it requires in terms of memory and CPU time; this is why it has been applied to a vast variety of cases, as concluded by the long citations list included. After a short introduction summarizing the fundamental concepts of the method, references since 2002 are categorized, briefly described, and commented on. This work is intended to assist every researcher who is involved in MAS computations, providing an exhaustive, to the best of the authors' knowledge, list of related publications. [ABSTRACT FROM AUTHOR]

We present waveguide cavity measurements used to evaluate several thin materials for use in radomes. In addition to the data on the materials, we show how these measurements can be performed with common laboratory equipment and simple calculations. We sought an approach that allowed candidate materials to be readily evaluated to deal with formerly selected materials becoming unavailable or cost-prohibitive. We used lengths of standard waveguide (WR650 and WR137 here) with readily manufactured irises and a vector network analyzer (Keysight N5225B here). To select the iris size and determine the limits of the simplifications in the equations used, we employed a full-wave 3D electromagnetic simulator (CST Microwave Studio). The equations required to calculate the dielectric properties of samples and their contribution to the equivalent system noise temperature from unloaded and loaded resonant frequencies and Q factors are shown. While these formulations can be found elsewhere, we did not find these assembled as conveniently in other studies in the literature. We also show that orienting the sample down the length of the cavity allows for higher-order modes to be fully utilized. We did not find this straightforward adaptation of the common cross-guide orientation in other works. Overall, the results allowed us to recommend three fabrics for use at the frequencies tested (1.7 and 5.6 GHz). The complete process is outlined to assist others in performing these measurements themselves. [ABSTRACT FROM AUTHOR]

Quantification of data misfits and model structures is an important step in the non‐linear iterative inverse scheme, allowing medium parameters to be iteratively refined through minimization. This study developed a new three‐dimensional controlled‐source electromagnetic inversion algorithm that allows general measures to be made selectively available for this evaluation. We adopt ℓ2$\ell _2$, ℓ1$\ell _1$, Huber, hybrid ℓ1$\ell _1$/ℓ2$\ell _2$, Sech, Cauchy, biweight and ℓ0$\ell _0$ norms as general measures. The inversion implementation is based on a regularized Gauss–Newton method, and non‐quadratic measures are incorporated via the use of an iteratively reweighted least‐squares scheme. To exploit current computing power, forward solutions are computed on an edge finite‐element discretization using a parallel version of a direct sparse solver, while dense matrix operations in inversion are optimized using the LAPACK library. The behaviours of general measures for evaluating data misfits and model structures are examined in synthetic inversion experiments, focusing on elucidating weighting mechanisms and setting user‐defined parameters. A preliminary demonstration is presented, showcasing simultaneous regularization in imaging a toy model containing both sharp and smooth property changes, alongside a field data application for imaging subsurface artificial structures. Our findings highlight the seamless integration of general measures, contributing to improved robustness against data outliers and enhanced spatial properties provided in output models. [ABSTRACT FROM AUTHOR]

For linear motion applications, particularly in aerospace, this study outlines the creation of an essential Quadrupole Electromagnetic System (QES) as a substitute for a four-track electromagnetic launcher. The QES design is compared with a four-track system to address concerns regarding rail-armature contact sliding. In a QES, four coils provide a homogeneous electromagnetic field, resulting in a Lorentz force on the slider. The QES was designed using the three-dimensional modeling capabilities of the ANSYS software. The results of the magnetic properties show a high potential for scaling this model to various levels. Additionally, the QES power circuit was simulated using ANSYS Simplorer. The circuit uses silicon-controlled rectifiers (SCR) and a pulse-width modulation (PWM) pulse generator. A force of 4kN was achieved, and this paper presents the current and force plots in detail. The study includes finite element analysis, electromagnetic and current characteristics simulation, and monitoring of the skin and proximity effects. Performance is increased by optimizing the QES design parameters using Particle Swarm Optimization (PSO). The simulation results demonstrate the feasibility and scalability of the QES design.

The construction of (hierarchical) curl‐conforming basis functions has been a hot topic in the last decades in the finite element community. Especially, functions applied to simplices have been quite popular after the work by Nédélec in 1980. Many mixed‐order and full‐order families have been provided in the last years, but sometimes, it is difficult to assess if they belong to the original space proposed by Nédélec (especially when orthogonalization procedures are applied). Here, a tool to determine if a family of basis functions belongs to the Nédélec space is provided. Since affine coordinates are the most frequent choice for simplices, particularities about its use with this kind of coordinates are detailed. A detailed survey of existing families is provided, and the practical application of the tool to a representative set of these families is discussed. The tool is also available for the community in a public repository. [ABSTRACT FROM AUTHOR]

We develop a two‐dimensional controlled‐source electromagnetic inversion algorithm employing a space domain forward modelling algorithm. The space domain forward modelling algorithm is devised by imposing boundary conditions on the plane perpendicular to the strike direction that passes through the source position. The boundary conditions for various source types are derived using the symmetric/antisymmetric character of the electric and magnetic fields. The benchmarking analysis reveals that roughly eight grids are sufficient for discretizing space in the strike directions for accurate forward response computations. For inverse modelling, the inexact Gauss–Newton optimization technique is utilized. Numerical inversion experiments of synthetic and real‐field data clearly demonstrate the versatility and robustness of the developed algorithm. The inversion experimentations also concur with the forward response benchmarking analysis and suggest that only a few grids (around eight) are adequate to discretize space in the strike direction. The developed algorithm is more than one order efficient compared to a wavenumber domain code. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of traditional scattering evaluation methods in near-field scattering prediction, a novel fast scattering calculation algorithm suitable for a near-field region is proposed in this paper. Based on physical optics, combined with locally modified approximate Green's function, the integral expressions of near-field electromagnetic scattering radiated by a plane wave and a dipole source are derived in a frequency domain. The simulation results show that the modified algorithm not only retains the simplicity and efficiency of the traditional code, but also can be directly used for near-field scattering prediction. [ABSTRACT FROM AUTHOR]

Controlled‐source electromagnetic methods are applied to survey the electrical resistivity distribution of the subsurface. This work compares normalized electromagnetic field components and transfer functions such as impedance tensors and vertical magnetic transfer functions generated by two independent source polarizations as input data for three‐dimensional inversion. As most other available inversion codes allow for inverting only one of the mentioned input data types, it is unclear which data type is preferable for controlled‐source electromagnetic inversion. Our three‐dimensional non‐linear conjugate gradient inversion code can handle both input data types, facilitating a comparison of normalized electromagnetic field components and transfer functions inversion. Examining inversion results for a three‐dimensional synthetic model with two anomalies, we infer that the transfer functions inversion is favourable for recovering the overall resistivity distribution below the receiver sites in fewer iterations. The inversion of normalized electromagnetic field components produces a sharper image of the anomalies and may be capable of detecting the resistivity distribution below the extended sources, which comes at the price of introducing a more heterogeneous background resistivity in the model. [ABSTRACT FROM AUTHOR]

The high precision identification of coal–rock layers is a significant challenge in intelligent mining. There is a large amount of electromagnetic noise and metal reflector signals in the full space detection environment of mining roadway, which makes it hard to distinguish the reflected waves at interface from a set of echo signals generated by the interface due to the similar amplitudes among them. So the method of identifying layers solely based on amplitude characteristics has poor stability and accuracy in coal mining environments. This paper proposes a method for identifying coal–rock layers based on Hilbert transform and tracking–scanning–stacking technology. There are two steps to achieve the recognition of air–coal–rock interfaces. First, by analysing the instantaneous amplitude spectrum obtained from the Hilbert transform, the first extreme point that is always the maximum value within a wavelength range is determined as the rough position of the air–coal interface. To solve the problem of recognition errors caused by noise and energy dispersion, the density difference method is used to remove discrete points. Second, the precise position of the air–coal interface is determined by tracking the extreme points within the 1.5 wavelength range around the rough position, and using the amplitude stacking method to quantitatively analyse and compare the degree of energy concentration. The data between zero time and the reflected waves at the air–coal interface is removed to avoid the impact of them on the recognition of the coal–rock interface. Results of physical model experiments and actual coal mine experiments show that this method yields better results and has high stability compared to conventional recognition method. Moreover, the average relative thickness errors are 4.5% for air layer and 4.2% for coal layer. [ABSTRACT FROM AUTHOR]

The potential application of conducting Scholte‐wave analysis using electroseismic pressure fields excited by an electric current source due to the electrokinetic effect in fluid‐saturated porous seabed sediments is investigated. First, we develop a numerical modelling algorithm by combining the Luco–Apsel–Chen generalized reflection and transmission method with the peak‐trough averaging method to simulate the electroseismic wave fields in stratified fluid/porous media. The modelling results show that the electroseismic pressure signals recorded on the seafloor are mainly composed of evanescent electroseismic waves, and Scholte waves are the dominant wave pattern. Their amplitudes are generally within the order of magnitudes capable of being detected by current seismic instruments. Then, the modified frequency–Bessel transform method is extended to extract the Scholte‐wave dispersion curves from electroseismic pressure fields. Results demonstrate that Scholte‐wave dispersion curves extracted from electroseismic records are superior to those extracted from conventional seismic wave fields excited by an airgun source under the same source–receiver geometry because they contain many overtones and are almost free from the interferences of dispersive guided waves. Furthermore, the Scholte‐wave dispersion inversion results obtained by employing the Levenberg–Marquardt method show that the shear‐wave velocity model inverted by Scholte‐wave dispersion curves extracted from the electroseismic pressure field is more accurate than those obtained by dispersion curves extracted from the seismic wave fields with the guided‐wave removal. The above results indicate that the electroseismic Scholte‐wave analysis method has the potential to evaluate the shear‐wave velocities of shallow‐water seabed sediments. [ABSTRACT FROM AUTHOR]

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Electromagnetics has been shown to be a viable tool to monitor CO2 plumes embedded in saline reservoirs. However, the majority of studies focus on measuring the electric field, which requires cumbersome equipment in the field and it is difficult to install permanent measurement stations. Magnetic field receivers offer an opportunity to reduce the form factor of the survey and increase the mobility by utilizing upcoming technologies, such as drones. We explore the use of frequency-domain electric dipole sources, and time-domain loops with a focus on measuring the secondary magnetic field at the surface for a conceptual injection scenario based on the Kemper CarbonSAFE site. We find that electric dipole sources give a response above the sensitivity of current sensor technology and, therefore, be a viable tool for CO2 monitoring. The time-domain loop source does provide fields that are useful for determining the location of the CO2 plume, however the field magnitude is below the sensitivity of the current generation of instruments. To explore the use of a potential borehole receiver we generate a map of the magnetic field at depth to explore potential borehole placement for monitoring efforts. Finally, we limit the spatial extent of the electric dipole survey to a single parcel of land to help understand how the fields change with survey geometry. We find that the shape of the secondary fields change slightly with the small transmitter, but are still measurable provided that the cultural noise at the site is low. Thus, we conclude that at the Kemper site a frequency-domain cross-dipole source with magnetometer receivers is suitable to monitor the expansion of the CO2 plume in the saline reservoir, even with a limited transmitter footprint on the surface.

Diabetes is a chronic disease that affects millions of humans worldwide. This review article provides an analysis of the recent advancements in non-invasive blood glucose monitoring, detailing methods and techniques, with a special focus on Electromagnetic wave microwave glucose sensors. While optical, thermal, and electromagnetic techniques have been discussed, the primary emphasis is focussed on microwave frequency sensors due to their distinct advantages. Microwave sensors exhibit rapid response times, require minimal user intervention, and hold potential for continuous monitoring, renders them extremely potential for real-world applications. Additionally, their reduced susceptibility to physiological interferences further enhances their appeal. This review critically assesses the performance of microwave glucose sensors by considering factors such as accuracy, sensitivity, specificity, and user comfort. Moreover, it sheds light on the challenges and upcoming directions in the growth of microwave sensors, including the need for reduction and integration with wearable platforms. By concentrating on microwave sensors within the broader context of non-invasive glucose monitoring, this article aims to offer significant enlightenment that may drive further innovation in diabetes care.

Many studies have highlighted the superior performance of iterative solvers employing the auxiliary‐space Maxwell solver preconditioner in controlled‐source electromagnetic induction problems featuring isotropic conductivity. The importance of considering the presence of electrical anisotropy in controlled‐source electromagnetic data has been well recognized. However, considering anisotropic conductivity will impose difficulty in robustly solving the final system of linear equations as the electrical anisotropy may significantly increase its condition number and degrade the performances of iterative solvers. Whether or not iterative solvers using the auxiliary‐space Maxwell solver preconditioner have similar superior performances in the case of arbitrary electrical anisotropy is still an issue to be discussed. In this study, within the framework of finite element simulation employing unstructured tetrahedral meshes, we conduct a comprehensive examination to evaluate the performance of the flexible generalized minimum residual solver with the auxiliary‐space Maxwell solver preconditioner for three‐dimensional controlled‐source electromagnetic forward modelling problems involving arbitrary anisotropic media. Tests on synthetic one‐ and three‐dimensional models show that our iterative scheme performs better than widely used iterative or direct solvers for controlled‐source electromagnetic anisotropy forward problems. Its convergence rate is nearly independent of working frequencies, anisotropy ratio and problem size. Finally, we applied the newly developed parallel iterative scheme to the Bay du Nord reservoir in a complicated real‐life offshore hydrocarbon exploration scenario characterized by anisotropic conductivity, in which our iterative scheme with an auxiliary‐space Maxwell solver preconditioner has good robustness. Furthermore, we investigated how data responses at different frequencies are sensitive to the actual hydrocarbon reservoir. Our sensitivity analysis revealed that data at large measuring offsets are considerably more sensitive to the reservoir than data at shorter measuring offsets. We also assessed the impact of neglecting anisotropy in data analysis for the realistic example and found that ignoring anisotropy can lead to noticeable changes in the data. This suggests that considering anisotropy in the interpretation of the observed data is essential to guarantee the precision of controlled‐source electromagnetic field surveys. [ABSTRACT FROM AUTHOR]

Fractional derivative operators are finding applications in a wide variety of fields with their ability to better model certain phenomena exhibiting spatial and temporal nonlocality. One area in which these operators are applicable is in the field of electromagnetism, thereby modelling transient wave propagation in complex media. To apply fractional derivative operators to electromagnetic problems, the operator must adhere to certain principles, like the trigonometric functions invariance property. The Grünwald–Letnikov and Marchaud fractional derivative operators comply with these principles and therefore could be applied. The fractional derivative arises when modelling frequency-dispersive dielectric media. The time-domain convolution integral in the relation between the electric displacement and the polarisation density, containing an empirical extension of the Debye model, is approximated directly. A common approach is to recursively update the convolution integral by approximating the time series by a truncated sum of decaying exponentials, with the coefficients found through means of optimisation or fitting. The finite-difference time-domain schemes using this approach have shown to be more computationally efficient compared to other approaches using auxiliary differential equation methods. [ABSTRACT FROM AUTHOR]

Interpreting transient electromagnetic (TEM) anomalies in terms of conductive rectangular plates is effective in many situations. However, not all conductors are thin and planar. Triaxial ellipsoid conductors are an attractive alternative: geometrically simple (corner-free), mathematically tractable at early and late time limits, and able to encompass shapes ranging from discs to elongate lenses to equi-dimensional pods. Accordingly a fast magnetostatic algorithm has been developed to compute the resistive limit (RL) response of a ellipsoidal conductor, which may also be permeable. The algorithm has been validated against new analytic resistive limit solutions for spherical and spheroidal conductors and against 3D multigrid finite difference modelling for a triaxial ellipsoidal conductor. A uniformly conductive ellipsoid supports three fundamental current modes in the resistive limit, an independent mode for excitation parallel to each of the principal axes. The RL current density increases linearly with radial distance from the ellipsoid centre. A formula for the time constant of an oblate spheroid has been derived for excitation parallel to its rotational axis, namely $ {\tau _3} \approx \sigma {\mu _0}bc/(4 + 6c/b) $ τ 3 ≈ σ μ 0 b c / (4 + 6 c / b) , where $ \sigma $ σ is the conductivity and $ c $ c and $ b $ b are respectively the minor and major radii. [ABSTRACT FROM AUTHOR]

This paper presents a fast antenna phase optimization scheme to enable microwave power focusing for breast cancer hyperthermia. The power focusing is achieved through the maximization of the deposited electric field on the target malignant tumor tissue. To do so, a malignant breast tumor, the surrounding breast medium, and the skin of the breast are modeled as a cylindrical structure composed of eccentric cylinders, and electric field distribution is computed analytically in terms of cylindrical harmonics. This approach minimized the computational cost and simplified the breast medium model. To ensure applicability across various breast types, the dielectric properties (DPs) of the main cylinder, symbolizing healthy breast tissue, are averaged from pixel DPs of digital breast phantoms. The hyperthermia problem is posed as maximizing the total electric field at the central point of the tumor which is simply represented as a vector sum of complex quantities in terms of antenna phases. That is, one antenna phase is selected as a reference and the remaining ones are adjusted accordingly. The approach is validated using a circular applicator with eight TMz polarized line sources surrounding realistic digital breast phantoms. Testing involved eight scenarios using two distinct breast media. In the optimal scenario, the peak temperature is achieved 1.3 mm away from the focus point. [ABSTRACT FROM AUTHOR]

Many different magnetotelluric response functions such as the impedance and tipper which are converted from measured electromagnetic fields to remove the effects of natural sources have been proposed and studied. To obtain good inversion results, it is important to know that the inversion results depend on the sensitivity patterns of the magnetotelluric response functions used as the input, indicating parts to which the magnetotelluric responses are sensitive. We introduce how to use the sensitivity patterns to determine optimal input magnetotelluric response functions for interpreting field data. For magnetotelluric field data, we use the data acquired at the Utah Frontier Observatory for Research in Geothermal Energy survey area. The target structure in the Utah Frontier Observatory for Research in Geothermal Energy survey area is associated with geothermal energy, and most of the sites are located to the left of the target structure. Examining the sensitivity patterns of the major magnetotelluric response functions, that is the components of the impedance tensor and tipper vector, for the target anomaly, the pattern of y‐component of the tipper vector (Ty${T_y}$) covers the areas where most of the sites are distributed. This means information about the target structure is mainly contained in Ty${T_y}$. To investigate the impact of Ty${T_y}$ on inversion of the target anomalous body, we compare the inversion results for four cases that (1) the xy‐ and yx‐components of the impedance tensor (Zxy${Z_{xy}}$ and Zyx${Z_{yx}}$) are used; (2) Zxy${Z_{xy}}$, Zyx${Z_{yx}}$ and Ty${T_y}$ are used; (3) Zxy${Z_{xy}}$, Zyx${Z_{yx}}$ and x‐component of the tipper vector (Tx${T_x}$) are applied; and (4) the all components of the impedance tensor (Zxx${Z_{xx}}$, Zxy${Z_{xy}}$, Zyx${Z_{yx}}$ and Zyy${Z_{yy}}$) are used as the input. This comparison shows that Ty${T_y}$ selected as the optimal input from the sensitivity patterns for this case study contributes to retrieving the target anomaly. Through the case study for the Utah Frontier Observatory for Research in Geothermal Energy site, we show how to design a strategy of selecting optimal magnetotelluric response functions based on the sensitivity patterns to effectively image target structures. Our experiment supports that the sensitivity patterns can be used to increase the reliability of magnetotelluric inversion. [ABSTRACT FROM AUTHOR]

Subsurface energy resources are often found in three‐dimensional and non‐spatially continuous rock formations that exhibit electrical anisotropy. Deep‐sensing tri‐axial borehole electromagnetic measurements are currently being used to detect three‐dimensional fluid‐bearing subsurface formations, but borehole environmental, geometrical and instrument‐design factors, together with measurement noise, constrain their practical range of detection and spatial resolution. By understanding the interplay of the above factors on the detectability and sensitivity of borehole deep electromagnetic measurements, one can potentially quantify the uncertainty of both target spatial location (relative to the well trajectory) and electrical resistivity contrast, thereby improving the certainty of three‐dimensional well navigation in real time. We implement a finite‐volume method to numerically solve Maxwell's equations for three‐dimensional electrically anisotropic heterogeneous rock formations in the calculation of magnetic fields measured with deep‐sensing tri‐axial borehole electromagnetic instruments. The calculated magnetic fields at measurement locations are described as the per cent difference between measurements acquired in rock formations with and without conductive or resistive three‐dimensional targets. We quantify the maximum radial distance of detection from the well trajectory and the spatial sensitivity of a commercially available deep‐sensing electromagnetic instrument with respect to environmental factors and measurement acquisition parameters by assuming that the borehole electromagnetic instrument can reliably detect offset three‐dimensional targets if the corresponding per cent measurement difference exceeds the threshold for measurement noise. Results indicate that commercially available tri‐axial deep‐sensing borehole electromagnetic instruments can achieve maximum detection distances between a quarter of and a full transmitter–receiver spacing. In addition, we show that radial detection distances vary from 0.3 to 2 skin depths depending on the geological environment, that is, target conductivity contrast with respect to the embedding background, electrical anisotropy of the background formation and targets and measurement acquisition parameters, that is, frequency and transmitter–receiver spacings. The above findings are not only important for instrument design and measurement‐acquisition planning but also for the effective implementation of real‐time inversion‐based measurement interpretation procedures. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are considered a key enabler of highly energy-efficient 6G and beyond networks. This property arises from the absence of power amplifiers in the structure, in contrast to active nodes, such as small cells and relays. However, a certain amount of power is still required for RIS operation. To improve their energy efficiency further, we propose the notion of perpetual RISs, which secure the power needed to supply their functionalities through wireless energy harvesting (EH) of impinging transmitted electromagnetic (EM) signals. Toward this, we initially explain the rationale behind such RIS capability and proceed with a presentation of the main RIS controller architecture that can realize this vision under an in-band EH consideration. Furthermore, we present a typical EH architecture, followed by two harvesting protocols. Subsequently, we study the performance of the two protocols under a typical communications scenario. Finally, we elaborate on the main research challenges governing the realization of large-scale networks with perpetual RISs. [ABSTRACT FROM AUTHOR]

Magnetorheological fluids (MRFs) are widely used for various kinds of controllable devices since their properties can be controlled by an external magnetic field. Despite many benefits of MRFs, such as fast response time, the sedimentation arisen due to the density mismatch of the compositions between iron particles and carrier oil is still one of bottlenecks to be resolved. Many studies on the sedimentation problem of MR fluids have been carried out considering appropriate additives, nanoparticles, and several carrier oils with different densities. However, a study on the effect of current magnitudes and profiles on the sedimentation is considerably rare. Therefore, this study experimentally investigates sedimentation behaviors due to different current magnitudes and different magnitude profiles such as square and sine waves in different diameters. The evaluation was performed by visual observation to obtain the sedimentation rate. It was found that the average sedimentation rate of the square type of current is slower compared to the sinusoidal type. It has also been identified that the higher intensity of the applied current results in a stronger electromagnetic field, which could slow down the sedimentation. The results achieved in this work can be effectively used to reduce particle sedimentation in the controller design of various application systems utilizing MRFs in which the controller generates a different magnitude and different profile of the external magnetic field. [ABSTRACT FROM AUTHOR]

This review paper addresses the development of numerical modeling of electromagnetic fields in geophysics with a focus on recent finite element simulation. It discusses ways of estimating errors of our solutions for a perfectly matched modeling domain and the problems that arise from its insufficient representation. After a brief outline of early methods and modeling approaches, the paper mainly discusses the capabilities of the finite element method formulated on unstructured grids and the advantages of local h-refinement allowing for both a flexible and largely accurate representation of the geometries of the multi-scale geomaterial and an accurate evaluation of the underlying functions representing the physical fields. In summary, the accuracy of the solution depends on the geometric mapping, the choice of the mathematical model, and the spatial discretization. Although the available error estimators do not necessarily provide reliable error bounds for our complex geomodels, they are still useful to guide grid refinement. Therefore, an overview of the most common a posteriori error estimators is given. It will be shown that the sensitivity is the most important function in both guiding the geometric mapping and the local refinement. [ABSTRACT FROM AUTHOR]

The Bakreswar geothermal province represents a medium enthalpy geothermal system with its Bakreswar and Tantloie hot springs. It lies within the Chotanagpur Granite Gneissic Complex in the eastern part of the Indian Peninsula. The province has a high heat flow and a high geothermal gradient of 90°C/km. Magnetotelluric data from 95 sites in a frequency range of 10 kHz–10 Hz were acquired over the Bakreswar geothermal province to obtain an electrical conductivity model and map the geothermal reservoir with its fluid pathways and related geological structures. Subsurface conductivity models obtained from three‐dimensional inversions of the Magnetotelluric data exhibit several prominent anomalies, which are supplemented by gravity results. The conductivity model maps three features which act as a conduit (a) a northwest–southeast trending feature, (b) an east–west trending feature to the south of the northwest–southeast trending feature (which lies 1 km north of the Oil and Natural Gas Corporation fault marked by previous studies) and (c) shallow conducting features close to Bakreswar hot spring. The northwest–southeast trending feature coincides with the boundary of the high‐density intrusive block. This northwest–southeast trending feature provides the pathway for the meteoric water to reach a maximum depth of 2.7 km, where it gets heated by interacting with deep‐seated structures and then it rises towards the surface. The radiogenic process occurring within the granites of Chotanagpur Granite Gneissic Complex provides the heat responsible for heating the meteoric water. The northwest–southeast and east–west trending features are responsible for the transport of meteoric water to deeper depths and then towards the shallow regions of the Earth. The near surface features close to the Bakreswar hot spring are responsible for carrying the water further towards the hot spring. The resistivity of these structures plotted as a function of salinity and temperatures for saline crustal fluids suggests the involvement of meteoric water. Further, applying Archie's law to this resistivity suggests that the conduit path has a porosity greater than 10%. This study successfully maps the anomalous structures which might foster the migration of geothermal fluid in Bakreswar geothermal province. [ABSTRACT FROM AUTHOR]

Data acquisition using equal spacing has been a standard practice in geophysics. The dense uniform sampling derived from Nyquist–Shannon sampling includes redundant samples, and it is sufficient but not necessary to adequately record target signals. We propose an ergodic sampling, which avoids the redundant samples in the dense uniform sampling and possesses the ability to capture the sufficiently similar information content as does Nyquist sampling. Ergodicity means that a key part of the system can represent the average performance of the entire system. Our ergodic samples are a critical subset of dense uniform samples and can represent the full uniform Nyquist samples. To find such a critical subset, we first examine the properties of different sampling patterns, including the sampling interval distribution, sampling angle distribution, areal sample density, and resolution in the spectral domain. We define the information sampling ability of sampling patterns based on these properties. The concept of information sampling ability that we have proposed serves as the criterion to compare different sampling patterns and assess their sampling performances. The sampling patterns with the same information sampling ability have the same capability to gather information, even though the appearance of sampling patterns may be different. We formulate an optimization problem to find this critical subset of sample locations, which has the fewest number of samples but has a similar information sampling ability as that of the desired dense uniform samples. This critical subset is irregularly located, has the optimized properties and forms the ergodic sampling pattern. We define this process of sampling design and associated understanding as the ergodic sampling. Ergodic sampling can be applied to gain two major benefits in practice. First, this approach can save a significant number of samples. We demonstrate ergodic sampling using one‐dimensional synthetic data and a two‐dimensional field geophysical dataset. The simulations confirm that, compared with other sampling strategies, ergodic sampling can use fewer samples to acquire the same amount of information, so that we can save cost. Alternatively, with the same budget, we can use the same number of samples through ergodic sampling to acquire more information. The new ergodic sampling can lead to a new generation of economic and efficient geophysical data acquisition, which could assist in increasing the discovery rate in resource exploration, tackling more earth science problems with a limited budget and can also benefit the environment in the process by reducing the invasiveness in potentially sensitive regions. [ABSTRACT FROM AUTHOR]

Numerical methods based on finite differences for solving the wave equation are prone to various errors, a challenge that also applies to the semi-discrete case of a recently proposed quantum algorithm that preserves continuous time while discretizing space into a quantum state vector. The inherent limitations of quantum processing, which affect the generality and extensibility of the quantum algorithm compared to classical numerical solvers, are offset by the logarithmic scaling of the number of qubits with the number of grid points. The higher grid density in combination with the higher-order approximation of the continuous Laplacian operator improves the numerical accuracy. In contrast to the Dirichlet boundary condition, however, the Neumann boundary condition in the quantum algorithm cannot exceed a second-order Laplacian approximation. Moreover, the anisotropy error is inherent to the lattice geometry. In this work, we propose a quantum wave equation solver on a hexagonal grid, which exhibits intrinsic grid dispersion comparable to that of a fourth-order accurate method on a rectangular grid and provides a quadratic improvement in the anisotropy measures. We show that the graph Laplacian on a hexagonal grid embedding the Neumann boundary condition is a bidirected graph. To make it suitable for use as a system Hamiltonian, a symmetrization and factorization based on the asymmetric adjacency matrix is proposed. Dispersion relations for quantum algorithms solving the wave equation on different lattices are derived and compared. The algorithms are analyzed as a limiting case of Yee’s finite-difference time-domain method in the context of electromagnetic wave propagation.

This study investigates the use of deep learning techniques for building a generalized surrogate model that can accurately and very efficiently predict antenna performance parameters. Notably, we focus on applications where a substantial amount of simulation time is required and prior data is available for deep learning use. Specifically, for these applications, we introduce deep learning models that efficiently and reliably model the near-field of the antenna. These models, in turn, accurately predict far-field properties and essential antenna metrics, such as the reflection coefficient. To demonstrate the efficiency of our method, the widely used rectangular patch antenna is considered, encompassing variations in several important geometrical parameters, dielectric constant, and frequency. Based on our results, the proposed model, once trained, is over 200 times faster than conventional full-wave simulations with a nominal average root mean square error (RMSE) of 0.0174 in predicting all the necessary antenna parameters, such as resonant frequency, radiation pattern, and directivity.

This article investigates the extraction of complex refractive indices from the amplitude and phase of the transmitted electric field. In the first step, an incident plane wave has been assumed and the amplitude and phase of the transmitted plane wave is calculated analytically. In this calculation, different values of the complex refractive index have been assumed for the non-magnetic material under test. In fact, the real part and imaginary part of the refractive index are assumed in the range of [1–10] and [0–1], respectively. Furthermore, a general study is made by an assumption of the material thickness to simulation wavelength ratio in the range of [0.01–20]. Due to examining the measurement noise, noisy data are produced for different values of signal-to-noise ratio in the range of [25–40] dB. Due to the difficulties of estimating the refractive index confronted in the theoretical or iterative methods, a Long short-term memory (LSTM) network is proposed and used for the estimation of complex refractive index based on the amplitude and phase of the transmitted electric field. It is shown that the estimation accuracy of about 97% can be achieved in the trained network. Furthermore, the estimation accuracy as a function of thickness-to-wavelength ratio, signal-to-noise ratio, and the values of real and imaginary parts of the refractive index are studied in detail and shown that higher estimation accuracy can be achieved. The simulated results have been confirmed by the measurement for the thickness-to-wavelength ratio below 0.1 and a good agreement has been found. Therefore, the proposed method can replace analytical or repetitive methods as an optimal and more accurate method.

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade‐off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real‐time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO2 saturation. The network is trained on pairs of CO2 label models and multiphysics data so that it can directly image CO2 saturation. We use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO2 saturation images. Using realistic CO2 label models and multiphysics data derived from the Kimberlina CO2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single‐physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO2 saturation images. Our results also suggest that the deep learning multiphysics network for the non‐seismic data (i.e., electromagnetic and gravity) can be used as an effective low‐cost monitoring tool in between regular seismic monitoring. [ABSTRACT FROM AUTHOR]

Quantum sensing provides advanced technologies which significantly improve sensitivity and accuracy for sensing changes of motion, gravity, electric and magnetic field. Therein, quantum sensors for the detection of magnetic fields, so-called quantum magnetometers, are one of the most promising technological realizations. We firstly will provide a brief overview on methods in geophysical exploration benefitting from quantum magnetometers with resolution at the physical and technical limit. We will introduce recent developments on SQUID and OPM based sensors as specific implementations of a quantum magnetometer systems and application examples. [ABSTRACT FROM AUTHOR]

Model order reduction usually consists of two stages: the offline stage and the online stage. The offline stage is the expensive part that sometimes takes hours till the final reduced‐order model is derived, especially when the original model is very large or complex. Once the reduced‐order model is obtained, the online stage of querying the reduced‐order model for simulation is very fast and often real‐time capable. This work concerns a strategy to speed up the offline stage of model order reduction for large and complex systems. In particular, it is successful in accelerating the greedy algorithm that is often used in the offline stage for reduced‐order model construction. We propose to replace the high‐fidelity error estimator in the greedy algorithm with multi‐fidelity error estimation. Consequently, the computational complexity of the greedy algorithm is reduced and the algorithm converges more than two times faster without incurring noticeable accuracy loss. [ABSTRACT FROM AUTHOR]

The concealed microcracks in shield tunnel lining present the characteristics of being of small size, unknown shape, and are difficult to detect. Based on the finite-difference time domain (FDTD) approach, this study proposed a new construction method of a refined grid accommodating and combining the variable shapes of microcracks, and capable of designing cross type, mesh type, and wave type microcrack models. The proposed new method also configured steel bars in the models to simulate actual engineering conditions, and characteristic response images of the models under different working conditions were obtained using ground penetrating radar (GPR) technology, which were then compared and analyzed to identify the imaging characteristics and differences of microcracks with variable geometric shapes. The waveform, amplitude, and time span of the characteristic single channel signal were furthermore studied. The results showed that the new method could successfully simulate the GPR characteristic response images of 0.5 mm microcracks of diverse geometric shapes. When the microcracks were wavy, their real shape could only be determined after signal pre-processing; the density and quantity of steel bars directly affected the appearance of microcrack characteristic signals; the greater the density and quantity of steel bars, the greater the interference on the waveform, amplitude, and time-frequency range of electromagnetic wave signals; a special correlation existed between the maximum mean root square value of the amplitude and the single channel signal of the cracks. Moreover, the finding that the extension in time and distance in the GPR time distance profile intersected with the cracks was deemed potentially to provide fresh insights into identifying the characteristic points of the cracks in the GPR images. The new method proposed in this study successfully obtained the GPR numerical simulation images and characteristic signals of microcracks with variable geometric shapes. Through the processing and analysis of the characteristic response signals of microcracks, the conclusions obtained were considered to provide an interpretation basis for the detection of microcracks in practical engineering. [ABSTRACT FROM AUTHOR]

In this paper, we consider the spectral theory of linear differential-algebraic equations (DAEs) for periodic DAEs in canonical form, i.e., J df dt + H f = λ W f , where J is a constant skew-Hermitian n × n matrix that is not invertible, both H = H (t) and W = W (t) are d-periodic Hermitian n × n -matrices with Lebesgue measurable functions as entries, and W(t) is positive semidefinite and invertible for a.e. t ∈ R (i.e., Lebesgue almost everywhere). Under some additional hypotheses on H and W, called the local index-1 hypotheses, we study the maximal and the minimal operators L and L 0 ′ , respectively, associated with the differential-algebraic operator L = W - 1 (J d dt + H) , both treated as an unbounded operators in a Hilbert space L 2 (R ; W) of weighted square-integrable vector-valued functions. We prove the following: (i) the minimal operator L 0 ′ is a densely defined and closable operator; (ii) the maximal operator L is the closure of L 0 ′ ; (iii) L is a self-adjoint operator on L 2 (R ; W) with no eigenvalues of finite multiplicity, but may have eigenvalues of infinite multiplicity. Finally, we show that for 1D photonic crystals with passive lossless media, Maxwell’s equations for the electromagnetic fields become, under separation of variables, periodic DAEs in canonical form satisfying our hypotheses so that our spectral theory applies to them. [ABSTRACT FROM AUTHOR]

The energy efficiency of a communication system using pulse position modulation (PPM) can be increased by reducing the duration of the pulses transmitted over the communication channel to several tens of picoseconds. The employment of an event timer device as a time-to-digital converter (TDC) for demodulation allows the use of PPM with many pulse positions and achieves competitive data transfer speeds. However, along with several-picosecond accuracy of modern event timers, they require a pulse duration of several hundred picoseconds for precise detection. This research is devoted to developing passive techniques for precise pulse expansion from tens of picoseconds to hundreds of picoseconds. We propose two methods: the electrical method, which employs a microstrip low-pass filter (LPF), and the optical method, which uses fiber Bragg grating (FBG). This research offers a detailed analysis of distortion-free pulse expansion requirements, the design of prototypes meeting these requirements, and experimental design verification. Theoretical background, mathematical models, and results of experimental validation of the proposed pulse expansion methods within the laboratory transmitted reference pulse-position modulation (TR-PPM) communication system are provided. [ABSTRACT FROM AUTHOR]

We introduce two a posteriori error estimators for Nédélec finite element discretizations of the curl-curl problem. These estimators pertain to a new Prager–Synge identity and an associated equilibration procedure. They are reliable and efficient, and the error estimates are polynomial-degree-robust. In addition, when the domain is convex, the reliability constants are fully computable. The proposed error estimators are also cheap and easy to implement, as they are computed by solving divergence-constrained minimization problems over edge patches. Numerical examples highlight our key findings, and show that both estimators are suited to drive adaptive refinement algorithms. Besides, these examples seem to indicate that guaranteed upper bounds can be achieved even in non-convex domains. [ABSTRACT FROM AUTHOR]

Summary: Even though the bioeffects of electromagnetic radiation (EMR) have been extensively investigated during the past several decades, our understandings of the bioeffects of EMR and the mechanisms of the interactions between the biological systems and the EMRs are still far from satisfactory. In this article, we introduce and summarize the consensus, controversy, limitations, and unsolved issues. The published works have investigated the EMR effects on different biological systems including humans, animals, cells, and biochemical reactions. Alternative methodologies also include dielectric spectroscopy, detection of bioelectromagnetic emissions, and theoretical predictions. In many studies, the thermal effects of the EMR are not properly controlled or considered. The frequency of the EMR investigated is limited to the commonly used bands, particularly the frequencies of the power line and the wireless communications; far fewer studies were performed for other EMR frequencies. In addition, the bioeffects of the complex EM environment were rarely discussed. In summary, our understanding of the bioeffects of the EMR is quite restrictive and further investigations are needed to answer the unsolved questions.

In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially tested MOGNDO on standard multi-objective benchmark functions, where it showed strong performance. When applied to the BLDC motor design with the objectives of either maximizing operational efficiency or minimizing motor mass, the MOGNDO algorithm consistently outperformed other techniques like Ant Lion Optimizer (ALO), Ion Motion Optimization (IMO), and Sine Cosine Algorithm (SCA). Specifically, MOGNDO yielded the most optimal values across efficiency and mass metrics, providing practical solutions for real-world BLDC motor design. The MOGNDO source code is available at: https://github.com/kanak02/MOGNDO.

The Multiphysics Object-Oriented Simulation Environment (MOOSE) electromagnetics module has been developed to increase MOOSE physics module capabilities, enabling standalone and coupled computational electromagnetics within the MOOSE multiphysics ecosystem. The module is actively being utilized in the areas of plasma physics and advanced manufacturing, and it currently provides initial demonstrated capability in multi-dimensional, complex-valued electromagnetic wave propagation, electrostatic contact, reflection and transmission, and electromagnetic eigenvalue problems. Two-dimensional wave propagation and one-dimensional wave reflection and transmission are showcased as examples in this work. The modularity, parallelism, and plug-in infrastructure for custom future development is inherited from MOOSE itself, and the module can be used with both MOOSE-based and external codes, giving great flexibility.

Wellbore integrity is of paramount importance to subsurface resource extraction, energy storage 1and waste disposal. After installation,well casing and cement are subject to mechanical stress due to near-well pressure changes and fluid induced corrosion. This isexacerbated for geothermal wells where produced fluid is at high temperature and corrosive. The current state-of-the-art technologiesfor wellbore integrity assessments are an array of cased hole logging tools. Wireline deployed acoustic, electromagnetic and mechanicaltools are all available to inspect steel casing corrosion and casing-cement bond and these tools can provide high-resolution assessmentof borehole conditions. They are intrusive, however, in terms of borehole preparation and interruption to the normal operation of thewells, and not suitable for high temperature or highly deviated well deployments. In addition, these measurements are performedinfrequently due to high cost, and are therefore incapable of providing frequent data to better predict borehole degradation trajectory,which can help provide early warning of potential borehole failures.For this project we are developing a suite of novel, non-invasive, casing based tools for wellbore integrity assessment, combiningfast/low cost screening with higher-precision investigation. Our approach is based on monitoring the response of the casing whenenergized at the wellhead, thereby interrogating the casing without well intervention. Lab, field and numerical approaches are used inour study. During the early stage of the research, we focus on numerical simulations, which have shown the sensitivity of the lowfrequency electromagnetic (EM) signals to changes in borehole depths and have successfully tested the concept at a field site withdifferent length well casings. Initial seismic modeling efforts have also demonstrated our capability to simulate seismic tube wave andseismic field alterations due to borehole breakage and associated fluid leakage. Further numerical, laboratory and field experiments areunderway for additional technology sensitivity analysis, particularly the transient EM/Seismic reflectometry methods, data acquisitionoptimization, and numerical simulation improvements.