Frequency-domain Electromagnetic Research Articles

Feasibility of using the frequency-domain electromagnetic method to detect pipeline blockages: Experimental study

The normal function of modern society requires a vast number of buried pipelines with differing specifications, which could become blocked to varying degrees during routine operation. Consequently, a major technical municipal engineering problem is how best to accurately, nondestructively, and efficiently locate blockages in underground pipelines. The frequency-domain electromagnetic (FDEM) method has been widely used in geophysical exploration because of its features of nondestructive examination and high efficiency. Considering the application of FDEM to the problem of locating blockages in underground pipelines, numerical simulations and physical experiments are conducted on different pipeline types with different blocked states, different blockage substances, and different working configurations. Results demonstrate that FDEM is most applicable for the detection of blockages in insulated pipelines, especially when the single-ended charging method is used or when the blockage has reasonable impermeability because the induction signal exhibits a particularly obvious abrupt drop at the blockage position. However, owing to the low resistivity of metals and the current propagating to the surrounding ground, which makes it easier to form more conductive paths than blockages, the FDEM displays no obvious change in signal characteristics at the position of a blockage in a metal pipeline, which means that this method has certain challenges for detecting such blockages.

A Broadband Multifrequency Resonance Compensator for Frequency-Domain Electromagnetic Prospecting Transmitting System

A Broadband Multifrequency Resonance Compensator for Frequency-Domain Electromagnetic Prospecting Transmitting System

Apparent Resistivity Variation Imaging Method Based on Magnetic Field Gradient by NGO-LSSVM for the Ground-Airborne Frequency-Domain Electromagnetic Method

Apparent resistivity imaging is a typical rapid imaging method in the ground-airborne frequency-domain electromagnetic method. At present, the apparent resistivity is typically calculated by the measured magnetic field, however, this imaging method exhibits limited capability in recognizing the center of three-dimensional anomalies. Therefore, this paper proposed the calculation of apparent resistivity using magnetic field gradients. To solve the problem of random artificial anomalies that existed during the calculated process, this paper presents a hybrid least square support vector machine (LSSVM) and Northern Goshawk optimization (NGO) to establish the mapping relationship between the magnetic field gradient and apparent resistivity variation. This approach enables accurate prediction of apparent resistivity variations and effectively resolves the challenge of correcting background resistivity. Furthermore, three typical theoretical models and field examples are used to predict the apparent resistivity variation, the imaging results demonstrate that the proposed NGO-LSSVM algorithm is a feasible and efficient tool for predicting the apparent resistivity variation with high accuracy. This study provides a novel and efficient imaging method, which facilitates the application of ground-airborne frequency-domain electromagnetics for high-resolution detection requirements, such as mineral exploration.

Three-dimensional magnetotelluric modeling with nontrivial anisotropy by a regularization approach

The regularization approach has been successfully applied to remove spurious solutions in the magnetotelluric (MT) forward problems of isotropic Earth media. However, spurious modes are more likely to occur in numerical solutions of anisotropic media, as electrical anisotropy introduces many more complications to electromagnetic (EM) induction in such media. This study focuses on developing the regularization approach to 3D MT forward problems of anisotropic media, especially those of nontrivial anisotropy. The governing equation is now derived with a conductivity tensor, and an accordingly adapted form of a scaled grad-div term is augmented to regularize the solutions and constrain the divergence-free condition. A new scaling scheme is proposed to cope with the complicated distribution of current densities in nontrivial anisotropy media, and an effective conductivity is approximated by the diagonal elements of the conductivity tensor to formulate the scaling factor. Numerical tests show that, for various models of electrical anisotropy, the regularization approach can effectively enforce the divergence condition and successfully suppress spurious solutions. Therefore, for nontrivial anisotropy media, this approach can also improve the efficiency of the iterative solvers while retaining the accuracy of the solutions. The derivation of the governing equation is based on the MT method. However, this strategy should be generally applicable to other frequency-domain EM methods.

Hybrid CPU-GPU solution to regularized divergence-free curl-curl equations for electromagnetic inversion problems

The Curl-Curl equation is the foundation of time-harmonic electromagnetic (EM) problems in geophysics. The efficiency of its solution is key to EM simulations, accounting for over 95% of the computation cost in geophysical inversions for magnetotelluric or controlled-source EM problems. However, most published EM inversion codes are still central processing unit (CPU)-based and cannot utilize recent computational developments on the graphic processing units (GPUs). Based on a previously proposed divergence-free algorithm developed on CPUs, this study demonstrates the current limits of the CPU-based inversion procedure. To exploit the high throughput capability of GPUs, we propose a hybrid CPU-GPU framework to solve forward and adjoint problems required for EM inversions. The large sparse linear systems arising from the staggered-grid finite difference approximation of the Curl-Curl equation are solved with a mixed-precision Krylov subspace solver implemented on a GPU. The algorithm is then tested in EM forward and adjoint calculations, with real-world three-dimensional numerical examples. Test results show promising 30× kernel-level speed-ups over the conventional CPU algorithm. This approach may further take the complex frequency domain EM inversions onto the next, practical stage on small affordable GPU platforms.

A surface-tunnel frequency domain electromagnetic method for mineral exploration in Tajikistan area

For old mines, numerous mining tunnels exist, which can potentially bring us closer to the deep ore-bearing structures. Therefore, we propose a surface-tunnel frequency domain electromagnetic (EM) method to utilize these mining tunnels for geophysical exploration. In this method, the transmitter is placed on the Earth’s surface, while the receiver is positioned within the tunnel space, providing higher resolution due to its proximity to the target body. This paper presents the derived analytical solution for the electric field in the surface-tunnel configuration. We also propose a survey system for our surface-tunnel frequency domain electromagnetic method and provide a field example of lead-zinc deposits in Tajikistan. Synthetic cases demonstrate a significant enhancement of the EM signal when the receiver is moved into the tunnel. The field application validates the practicality of our surface-tunnel frequency domain EM method for deep mineral exploration in active mines.

Research on Frequency‐Domain Electromagnetic Detection Signal Acquisition Technology Based on Multi‐frequency LC Filtering

Frequency‐domain electromagnetic (FDEM) instruments are effective tools for detecting underground electrical abnormal targets. However, due to the influence of various noises in the detection process, would lead to l poor signal‐to‐noise ratio of the system and would ultimately affect the detection accuracy. And FDEM detection systems have a defined measuring range. Excessive noise will lead to saturation of the system acquisition and distortion of the measurement. Because of the above problems, an acquisition technique for the FDEM receiving system is proposed to achieve noise suppression in the detection process. The scheme proposes a multi‐frequency LC filtering circuit to optimize the analog signal acquisition module of the receiver system. The circuit converts the measured voltage signal into a current signal, improving the system's immunity to interference. At the same time, the noise of non‐target frequencies in the measured signal is suppressed, avoiding saturation of the receiving system acquisition, and realizing high signal‐to‐noise acquisition of multiple target frequencies. First, the principle of circuit design is analyzed, and the design flow of circuit parameters is proposed. Further, the validity of the design is verified via simulation results. Finally, the optimization scheme is validated with an actual circuit. The results show that for the measurement environment set in this paper, this optimization scheme can suppress the noise of non‐target frequency in the measured signal, so that the noise amplitude can be reduced by more than 90%, and the signal‐to‐noise ratio of the filtered signal can be improved by 23.35 dB. This study helps to facilitate FDEM instruments to achieve high signal‐to‐noise ratio detection. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Maximizing RDF recovery potential through the integration of electrical resistivity tomography and frequency-domain electromagnetic surveys for waste characterization in open dump mining

Open dumping is a widespread waste management technique used in developing countries. This approach is simple and economical but has numerous environmental and health impacts. Open dump mining (ODM) can reduce waste in both open and controlled dumpsites by converting waste into refuse-derived fuel (RDF) for energy generation; however, before ODM implementation, the waste's characteristics and composition must be assessed. Geophysical surveys are widely used to characterize waste. The electrical resistivity tomography (ERT) method is a particularly common technique for determining waste composition, but it has several limitations. Therefore, a frequency-domain electromagnetic (FDEM) survey was used in this study as a pre-screening tool to evaluate the waste composition and overcome the limitations of ERT surveys. This study aimed to determine the relationship between geophysical data types (i.e., ERT and FDEM) and the optimal waste composition for RDF production and assess the ability of FDEM surveys to identify areas with RDF recovery potential. This study combined ERT and FDEM surveys with waste sample analysis in old/new controlled dumpsites in Thailand. The quantitative and qualitative correlations between the geophysical data (i.e., resistivity, conductivity, waste composition, and moisture content (MC)) were then assessed using linear and multiple linear regression analysis. Integrating geophysical surveys was found to have the potential to locate optimal RDF recovery areas, with low conductivity (0–100 mS/m) and high resistivity (>55 Ω·m) characteristics associated with areas of high RDF content (>40 % by weight). An unexpected finding was that increased waste age resulted in increased resistivity and decreased conductivity. Additionally, the MC depended on factors affecting spatial variability (e.g., precipitation and biodegradation). Overall, this study demonstrates that integrating ERT and FDEM surveys is a suitable pre-screening tool to evaluate waste characteristics and composition, mitigate ERT survey limitations, and improve waste investigation before ODM.

Hydrogeologic controls on barrier island geomorphology: Insights from electromagnetic surveys

Barrier islands provide a first line of defense for coastal communities against storms, hurricanes, and sea-level rise. The geomorphology of barrier islands exerts a major control on storm impact and island recovery. In turn, barrier island geomorphology is affected by subsurface hydrogeologic conditions. In this study, we investigated the relationship between subsurface hydrogeologic conditions and geomorphology of Padre Island, with a focus on the influence of human development. We measured apparent electrical conductivities using frequency-domain electromagnetic (FDEM) surveys and spatially correlated them with the island's morphology. The latter was generated from a 1 m resolution digital elevation model. Four distinct zones were identified from the observed variations in apparent conductivity and elevation, revealing their inverse correlation. The beach area (Zone I) exhibits the highest apparent conductivity (289.7 ± 66.3 mS/m) and the lowest elevations (1.4 ± 0.2 m). These trends are largely due to the proximity of the beach to saline groundwater and maritime floods. Conversely, the foredune area (Zone II) presents the lowest apparent conductivity (19.0 ± 3.4 mS/m) and the highest elevation (4.5 ± 0.4 m) due to a greater distance from saline waters, deeper groundwater levels, and relatively dry soil conditions. Human development has significantly impacted Zones III (east central zone) and IV (west central zone), contributing to an increase in apparent conductivity (Zone III: 40.3 ± 21.8 mS/m; Zone IV: 159.5 ± 83.0 mS/m) and a reduction in elevation (Zone III: 2.1 ± 0.5 m; Zone IV: 1.3 ± 0.4 m). Anthropogenic activities have modified hydrologic patterns, introduced conductive materials, and altered vegetation cover and soil composition. This research elucidates the interplay between subsurface electrical conductivity, surface morphology, and the impact of human development on barrier island geomorphology, providing crucial insights for coastal management and conservation efforts.

A general forward solver for 3D CSEMs with multitype sources and operating environments

To determine the electromagnetic (EM) fields of different three-dimensional (3D) controlled-source electromagnetic methods (CSEMs) using the same parameters of the forward solution, by explicitly considering the commonalities, we present a general 3D forward modeling solver for CSEMs with multitype sources and operating environments. The commonality of the solver is reflected in two aspects. First, the solver is based on a frequency-domain (FD) vector Helmholtz equation for determining the scattered electric field. The different types of sources are imposed on the right-hand term of the equation, expressed as background Green’s function. Second, sources of any CSEM can be composed of electric dipole (ED) or magnetic dipole (MD) superposition. Thus, the focus of the 3D forward modeling of CSEMs is reduced to determining the EM fields of ED or MD sources for the background medium. The quasi-minimal residual (QMR) method is used to solve the large sparse complex linear system. Once the FD EM fields have been calculated, the time-domain (TD) response can be obtained using the cosine/sine transformation. The numerical results show that the relative error is less than 5% between the 3D numerical and analytical solutions, which verifies the accuracy of the solver. We further study the difference between the real (bent) and theoretical (straight) wires. We suggest that the shape of the source must be considered for TD and FD CSEMs with a wire source during data processing and inversion. The last example investigated the characteristics of FD EM fields from a finite-length wire and TD EM fields from a rectangular fixed loop on the same conductive tilted disk model buried in resistive sediments. According to the numerical results, we recommend FD CSEMs with a wire source for detecting deep anomalies.

Combining Ground Penetrating Radar and Frequency Domain Electromagnetic Surveys to Characterize the Structure of the Calderone Glacieret (Gran Sasso d’Italia, Italy)

Ice is a rich reservoir of past climate information, and the well-documented increasing rate of glacier retreat represents a great loss for paleoclimate studies. In this framework, the Ice Memory project aims to extract and analyze ice cores from glacier regions worldwide and store them in Antarctica as a heritage record for future generations of scientists. Ice coring projects usually require a focused geophysical investigation, often based on Ground Penetrating Radar (GPR) prospecting to assess the most suitable drilling positions. As a novel approach in the Calderone Glacieret, we integrated the GPR method with Frequency Domain Electromagnetic (FDEM) surveys, a technique not commonly applied in the glacial environment. We used a separated-coils FDEM instrument to characterize the glacieret structure. The acquired FDEM datasets were inverted and compared to the GPR data and borehole information. The results demonstrated the capability of the FDEM technique to define the structure of the glacieret correctly; therefore, the potential to be applied in frozen subsoil environments. This opens new perspectives for the use of the FDEM technique to characterize periglacial environments, such as rock glaciers, where the coarse-blocky surface hinders data acquisition and enhances the problem of signal scattering.

Controlled-source electromagnetic modeling using a high-order finite-difference time-domain method on a nonuniform grid

Simulation of 3D low-frequency electromagnetic (EM) fields propagating in the earth is computationally expensive. We have developed a fictitious wave-domain high-order finite-difference time-domain (FDTD) modeling method on nonuniform grids to compute frequency-domain 3D controlled-source EM data. The method overcomes the inconsistency issue widely present in the conventional second-order staggered-grid finite-difference scheme over a nonuniform grid, achieving high accuracy with an arbitrarily high-order scheme. The finite-difference coefficients adaptive to the node spacings can be accurately computed by inverting a Vandermonde matrix system using an efficient algorithm. A generic stability condition applicable to nonuniform grids is established, revealing the dependence of the time step and these finite-difference coefficients. A recursion scheme using fixed-point iterations is designed to determine the stretching factor to generate the optimal nonuniform grid. The grid stretching in our method reduces the number of grid points required in the discretization, making it more efficient than the standard high-order FDTD with a densely sampled uniform grid. Instead of stretching in vertical and horizontal directions, better accuracy of our method is observed when the grid is stretched along the depth without horizontal stretching. The efficiency and accuracy of our method are demonstrated by numerical examples.

Field evaluation of semi-automated moisture estimation from geophysics using machine learning.

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025-0.046 cm3/cm3). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

Solving Electromagnetic Inverse Problem Using Adaptive Gradient Descent Algorithm

The nonlinear conjugate gradient (NLCG) algorithm is one of the popular linearized methods used to solve the frequency-domain electromagnetic (EM) geophysical inverse problem. During NLCG iterations, the model gradient guides the searching direction while the line-search algorithm determines the step length of each iteration. Normally, the line search requires solving the corresponding forward problem a few times. Since line search is usually computationally inefficient, we introduce the adaptive gradient descent (AGD) algorithm to accelerate solving the frequency-domain EM inverse problem within the linearized framework. The AGD algorithm is a variant of the classical gradient descent method and has been well-developed and widely used in deep learning. Rather than the time-consuming line search, its core idea is to algebraically manipulate the cumulative gradients and updates of the model from previous iterations to estimate the model parameter variables at the current iteration. For the inversion of magnetotelluric (MT) data, we here designed and implemented a framework using the AGD algorithm combined with the cool-down scheme to tune the regularization parameter. To improve the convergence performance of the AGD algorithm [specifying to Adam and root-mean-square propagation (RMSProp)], we proposed a tolerance strategy which has been tested numerically. To optimize the global learning rate, we carried out some comparative trials in the proposed inversion framework. The inverted results of synthetic and real-world data showed that both the AGD algorithms (Adam and RMSProp) can recover comparable results and save more than a third of CPU time compared with the NLCG algorithm.

Drone-Borne Electromagnetic (DR-EM) Surveying in The Netherlands: Lab and Field Validation Results

In the past decade, drones have become available and affordable for civil applications, including mapping and monitoring the Earth with geophysical sensors. In 2017 and 2019, the feasibility of executing frequency domain electromagnetic (FDEM) surveys using an off-the-shelf drone was investigated at Deltares Institute. This paper reports firstly the preparatory tests executed to determine the optimal instrumental configuration, flight path, data processing and inversion schemes and secondly the three field validation tests executed to demonstrate the feasibility of the drone-borne electromagnetic survey in real-scale applications. At several test sites, the optimal configuration of the drone and electromagnetic instruments, such as the mounting device and distance of the electromagnetic (EM) sensor with respect to the drone, the flight altitude, the coil separation and frequency of the EM source, efficiency and safety, and the assemblage of instrument and drone data were investigated. This has resulted in a robust method to acquire accurate and repeatable in-phase, quadrature and apparent resistivity data, and a workflow for data correction, processing and inversion scheme was developed. During those tests, three EM instruments were tested. The drone-borne electromagnetic (DR-EM) system has the ability and efficacy to fly over inaccessible areas and surface water. Compared to helicopter-borne electromagnetic surveys, the spatial resolution is much higher, which allows very detailed 3D mapping of subsurface targets, and the survey costs are relatively low. Repeated drone-borne electromagnetic (DR-EM) surveys allow low-cost monitoring of local changes in water saturation and salinity.

An approach to solving Maxwell's equations in time domain

In this paper, we propose a new direct approach to solving Maxwell's equations in time domain. Completely different from the classic one, this approach is derived and analysed based on a strategy which is independent of the well-known Fourier (or Laplace) transformation and the corresponding inverse transformation. The analysis first decouples Maxwell's equations on the basis of a potential theory and then uses an operator-variation-of-constants formula (the Duhamel Principle) to achieve the expression of exact solutions of Maxwell's equations. We further illustrate the simplicity and multi-purpose applicability of the new approach in linear, homogeneous, isotropic and lossless (bounded or unbounded and source-free or source) medium (in rectangular, cylindrical or spherical coordinates). Moreover, we make a study on impulse electromagnetics. With this new approach, this paper bridges the gap between the time domain electromagnetics and the frequency domain electromagnetics.

Optimization of multi-frequency electromagnetic surveying for investigating waste characteristics in an open dumpsite

ABSTRACT Multi-frequency electromagnetic (EM) surveying is a type of frequency-domain electromagnetic (FDEM) survey, a new geophysical exploration approach used as a scanning tool to investigate waste characteristics within a final disposal site before electrical resistivity tomography (ERT) surveying. EM surveys provide information about shallow subsurface conditions; however, to date, no studies on waste bodies or waste characteristics in open dumpsites have assessed the optimal frequency for use in multi-frequency EM surveys. This research aims to determine the optimal frequency set for multi-frequency EM surveying for initial waste quality assessment in open dumpsites. These initial waste quality data are critical to assess the feasibility of open dump mining projects. First, a multi-frequency EM survey was performed in this study, which was measured at the exact electrode positions of the three ERT profiles. This FDEM survey used 15 frequencies ranging from 1,000–⁠15,000 Hz, which were grouped into frequency sets for inversion modeling of the electrical conductivity. Second, an ERT survey was carried out along all three profiles, each of which was around 144 m long. Subsequently, synthetic electrical resistivity data were generated by processing the electrical conductivity model. Data from each frequency set were then generated at the same depth, with interpolation techniques used to analyze the linear correlation between synthetic electrical resistivity and real electrical resistivity data and compare the Pearson’s correlation coefficient (R) values to determine the optimum frequency set for FDEM surveys in the open dumpsite. The results showed that the E4 frequency set, consisting of 5,000, 11,000, and 15,000 Hz values, typically had the highest positive correlations (R = 0.74–0.91) across all profiles and provided waste characteristics information close to the true conditions. Thus, these frequencies represent the optimal set for multi-frequency EM surveys for the primary assessment of waste characteristics in open dumpsites. Implications: Open dump mining (ODM) is now applied as a sustainable approach to combat improper waste disposal and reduce municipal solid waste (MSW) in the open dumpsite. To implement ODM for producing RDF, business developers must know the amount and composition of waste that can be converted into RDF before mining. This study used multi-frequency EM surveys with frequencies of 5,000, 11,000, and 15,000 Hz. This multi-frequency method effectively determined the waste composition and identified potential excavation points in the open dumpsite prior to ODM. This method can mitigate the limitations of traditional surveying, due to its improved mobility, lower time consumption, and reduced labor needs.

A comparison between Kalman ensemble generator and geostatistical frequency-domain electromagnetic inversion: The impacts on near-surface characterization

The spatial distribution of the physical properties of the first meters beneath the earth’s surface is often complex due to its highly dynamic nature and small-scale heterogeneities resulting from natural and anthropogenic processes. Therefore, obtaining numerical 3D models that accurately describe the spatial distribution of these properties is often challenging yet essential for different fields such as environmental assessment and remediation, geoarchaeological conservation, and precision agriculture. Frequency-domain electromagnetic (FDEM) induction methods have proven their potential to image these properties in high (spatial) detail because FDEM measurements are sensitive to two key soil properties: electrical conductivity and magnetic susceptibility. Predicting subsurface properties from FDEM data requires solving an ill-posed and nonlinear inverse problem with multiple solutions. Recently, there has been a rapid growth of FDEM inversion methods, which may be broadly divided into probabilistic and deterministic methods. We compare two stochastic FDEM inversion approaches: the Kalman ensemble generator (KEG) and another one formulated as an iterative geostatistical FDEM inversion. Both methods are applied to a synthetic data set with spatially heterogeneous physical properties of interest, mimicking a real landfill mining site. The predicted models are compared with the reference models in terms of histogram and variogram models reproduction and in their ability to quantify spatial uncertainty. The results indicate the ability of both methods to predict the reference values. Although the KEG is computationally efficient, it struggles to reproduce the extreme values. In contrast, the geostatistical inversion approach ensures the reproduction of the imposed histograms and variogram models in the predicted models. As the prior information is included in both inversion methods in different ways, the pointwise variance models computed from all of the posterior models have different information. The synthetic data set is available to the community, so it can be used as a benchmark for other FDEM inversion methods.

Developing a low-cost frequency-domain electromagnetic induction instrument

Abstract. Recent advancements and the widespread availability of low-cost microcontrollers and electronic components have created new opportunities for developing and using low-cost, open-source instrumentation for near-surface geophysical investigations. Geophysical methods that do not require ground contact, such as frequency-domain electromagnetics, allow one or two users to quickly acquire significant amounts of ground resistivity data in a cost-effective manner. The Colorado School of Mines electromagnetic system (CSM-EM) is a proof-of-concept instrument capable of sensing conductive objects in near-surface environments, and is similar in concept to commercial-grade equipment while costing under USD 400 to build. We tested the functionality of the CSM-EM system in a controlled laboratory setting during the design phase and validated it over a conductive target in an outdoor environment. The transmitter antenna can generate a current of over 2.5 A, and emit signals that are detectable by a receiver antenna at offsets of up to 25 m. The system requires minor refitting to change the functioning frequency, and has been operationally validated at 0.4 and 1.6 kHz. The receiver signal can be measured by off-the-shelf digital multimeters. Future directions will focus on improving the electronic and mechanical stability of the CSM-EM with the goal of using acquired data to make quantitative measurements of subsurface resistivity.

Frequency domain electromagnetic induction imaging: An effective method to see inside a capped landfill

The frequency-domain electromagnetic (FDEM) methods are a powerful tool for evaluating the impact caused on natural environments by anthropic facilities such as landfills. Noninvasive FDEM rapidly investigates large areas with no impact on the system. This is essential in case of capped landfills, as the impermeable liner represents a strong limitation for the use of all others direct and indirect investigation methods. This technique allows the propagation of the EM fields and collection of subsurface response below the liner thus representing the only effective solution both for static imaging and time-lapse monitoring of the processes that take place into the waste deposits. Traditionally, electromagnetic data are visualized as apparent electrical conductivity (ECa) maps that give practically no information about the variation of the conductivity with depth because ECa is only the equivalent conductivity of a homogeneous soil that would give the same measured response along depth. More recent approaches allow for an inversion of data thus providing clear information on the thickness of the investigated subsurface layers. The need for building a 3D electromagnetic model is crucial in the context of the urban waste landfill characterization, where leachate induces strong anomalies in electrical conductivity, which in turn causes a nonlinear model of the EMI response. A rigorous EMI inversion approach has been tested at a closed landfill in Southern Italy. The inverted model provided detailed information unattainable with other methods, by corroborating the assumption that electromagnetic measurements represent the best technique to characterize closed systems such as capped landfills.