Frequency-domain Electromagnetic Data Research Articles

Trial-and-error modeling of ground-airborne electromagnetic data in the Yishu Faulting Basin, China

Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.

Assessing soil moisture variability in a vineyard via frequency domain electromagnetic induction data

IntroductionIn agriculture, accurate hydrological information is crucial to infer water requirements for hydrological modeling, as well as for appropriate water management.MethodsTo achieve this purpose, geophysical frequency domain electromagnetic induction (FDEM) measurements are increasingly used for integration with traditional point-scale measurements to provide effective soil moisture estimations over large areas. The conversion of electromagnetic properties to soil moisture requires specific tools that must take into account the spatial variability of the two measurements and the data and model uncertainties. In a vineyard of about 4.5 ha located in Southern Italy, we tested an innovative assessment approach that uses a freeware code licensed from USGS, MoisturEC, to integrate electromagnetic data, collected with a CMD Mini-Explorer electromagnetic sensor, and point-scale soil moisture data.ResultsAbout 30,000 data measurements of apparent electrical conductivity (sa) allowed us to build a 3D inverted electromagnetic model obtained via an inversion process. Soil properties at different depths were inferred from the FDEM model and confirmed through the ground truth sampling.DiscussionThe data analysis tool allowed a more accurate estimation of the moisture distribution of the investigated area by combining the accuracy of the point-scale soil moisture measurements and the spatial coverage of the electrical conductivity (EC) data. The results confirmed the capability of the electromagnetic data to accurately map the moisture content of agricultural soils and, at the same time, the need to employ integrated analysis tools able to update such quantitative estimations in order to optimize soil and water management.

Frequency-Domain Electromagnetic and VLF Data with An Application to Modeling of Basement Structures: A Case Study Within, Ibadan Area, Southwestern Nigeria

The study area, the church camp is within Ibadan which fall within southwestern basement complex of Nigeria and it is mainly by migmatite-gneiss. The hard rocks are usually characterized by basement fracture which may serve as a conduit for groundwater passage. This has necessitated detail geological and geophysical investigation to accurately and precisely delineate this structure. Geological mapping alongside very low frequency Electromagnetic (VLF-EM) techniques were adopted in which conductivity data were acquired along Fifteen (15) VLF profiles using ABEM WADI. Thirteen (13) of these profiles were in the East-West (E-W) direction which is the dip direction of the structural elements, while the remaining two profiles were in North-South (N-S) direction. The data obtained from the field were processed, filtered and presented inform of profiles and Karous-Hjelt (K-H) filtered pseudo-section to visualise conductivity in (2-D). Pockets of conductive structures were delineated and were interpreted as fractures of various dimension and orientation. Some are single fracture and others are closely-spaced double fractures which some of them joined or fused together at depth with some suspected to contain water while some are dry base on their conductivity/resistivity signature exhibited. In conclusion, VLF-EM has proven to possess the capability to characterize and model the basement fractures and define their dimension, axial orientation and indicates their parallelism which also signifies their origin as regard stress regime that produces the fracture.

Interpretation of Frequency-Domain Airborne Electromagnetic Data Based on the Deep Neural Network Incorporating Topographic Information

Deep neural networks (DNNs) have recently been used to interpret frequency-domain electromagnetic (EM) data, therefore vast amounts of information can be rapidly interpreted. However, in airborne surveys that include mountainous regions, severe topographic changes distort the EM data and they can lead to unreasonable interpretations. DNN-based inversions that reduce the effects of topographic changes have not yet been proposed whereas some conventional inversion techniques can correct topographic distortions. Since DNN-based interpretation needs various training dataset and its performance depends on the characteristics of training dataset, it is important to generate various AEM data and resistivity model pairs. However, it is almost impossible to derive the EM responses of two-dimensional (2D) or 3D resistivity models incorporating diverse topographic patterns because such models require many variables and computation costs are limited. Therefore, we suggested the pseudo 1D interpretation of EM data which can consider the topographic changes using EM responses and topographic information at three neighboring data points. We include topographic information in DNN training as slope angle between the data points. Compared to conventional inversion, our trained DNN model recovers the synthetic resistivity models more reasonably. In addition, we used the trained DNN model to evaluate a real AEM dataset from south-central Alaska. The trained DNN provided reasonable interpretation results despite that the data were acquired at mountainous area with rough topography. Since the trained DNN model can provide predictions rapidly, the interpretation time was drastically reduced. We believe that this research can increase the practical field applicability of AEM survey.

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.

A Random Forest approach to predict geology from geophysics in the Pontiac subprovince, Canada

Current “visual” integration approaches to map geology from geophysics are challenging, biased toward user assumptions, and time consuming. Applying a supervised machine learning Random Forest method to airborne geophysical data sets is an alternative solution to quickly generate interpretative maps, which can later be validated through targeted and therefore more efficient field campaigns. The approach is tested in the Malartic–Cadillac area of the Pontiac subprovince, Canada. Available airborne geophysical data sets include magnetic, frequency-domain electromagnetic, and radiometric data. Unlike the western part of the area, many studies have been done in the eastern part where the world-class Canadian Malartic gold deposit is located. The eastern part has abundant field observations and well-documented geology that are used as training data for Random Forest learning. A predicted map is built after preprocessing the data, gridding the area into a mesh of nodes and training using known geological knowledge. The results provide new information about the spatial distribution of lithological units, including diabase dikes, felsic-intermediate igneous rocks, mafic–ultramafic igneous rocks, and sedimentary rocks. Comparison between the predicted map and previous geological maps could suggest locations where future field mapping efforts should focus in the Malartic–Cadillac area.

Application of Semi-Airborne Frequency Domain Electromagnetic Data Based on Improved Ant-Colony-Optimized Wavelet Threshold Denoising Method

The semi-airborne frequency domain electromagnetic detection method is a popular electromagnetic detection method in which the transmission system is excited on the ground and the reception system coil is in the air to measure the vertical magnetic field signals in the frequency domain. In the field exploration, the semi-airborne frequency domain electromagnetic detection system is vulnerable to low-frequency motion noise, power-line interference, industrial noise, astronomical noise, and many other interferences, which leads to a very low signal-to-noise ratio of the detection data. Owing to the overlap between the effective signal and several types of noise in the time-frequency domain in the semi-airborne frequency domain of the detected signal, the conventional denoising methods are limited and cannot meet practical needs. Therefore, an integrated denoising method based on the improved ant-colony-optimized wavelet threshold is proposed. The method first separates the low-frequency noise of motion using the wavelet’s high-scale component for noise reduction. Other interference noise types, such as power-line interference, industrial noise and astronomical noise, are also reduced using the improved ant-colony-optimized wavelet threshold method. The experimental results show that the proposed integrated denoising method exhibits good suppression effects on several types of noise, can effectively improve the signal-to-noise ratio of the data, and improve the field data inversion accuracy.

A variational non-linear constrained model for the inversion of FDEM data* *Dedicated to Lothar Reichel on the occasion of his 70th birthday.

Reconstructing the structure of the soil using non-invasive techniques is a very relevant problem in many scientific fields, like geophysics and archaeology. This can be done, for instance, with the aid of frequency domain electromagnetic (FDEM) induction devices. Inverting FDEM data is a very challenging inverse problem, as the problem is extremely ill-posed, i.e. sensible to the presence of noise in the measured data, and non-linear. Regularization methods substitute the original ill-posed problem with a well-posed one whose solution is an accurate approximation of the desired one. In this paper we develop a regularization method to invert FDEM data. We propose to determine the electrical conductivity of the ground by solving a variational problem. The minimized functional is made up by the sum of two term: the data fitting term ensures that the recovered solution fits the measured data, while the regularization term enforces sparsity on the Laplacian of the solution. The trade-off between the two terms is determined by the regularization parameter. This is achieved by minimizing an ℓ 2 − ℓ q functional with 0 < q ⩽ 2. Since the functional we wish to minimize is non-convex, we show that the variational problem admits a solution. Moreover, we prove that, if the regularization parameter is tuned accordingly to the amount of noise present in the data, this model induces a regularization method. Some selected numerical examples on synthetic and real data show the good performances of our proposal.

Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness

Frequency-domain electromagnetic (FDEM) data are commonly inverted to characterize subsurface geoelectrical properties using smoothness constraints in 1D inversion schemes assuming a layered medium. Smoothness constraints are suitable for imaging gradual transitions of subsurface geoelectrical properties caused, for example, by varying sand, clay, or fluid content. However, such inversion approaches are limited in characterizing sharp interfaces. Alternative regularizations based on the minimum gradient support (MGS) stabilizers can, instead, be used to promote results with different levels of smoothness/sharpness selected by simply acting on the so-called focusing parameter. The MGS regularization has been implemented for different kinds of geophysical data inversion strategies. However, concerning FDEM data, the MGS regularization has only been implemented for vertically constrained inversion (VCI) approaches but not for laterally constrained inversion (LCI) approaches.We present a novel LCI approach for FDEM data using the MGS regularization for the vertical and lateral direction. Using synthetic and field data examples, we demonstrate that our approach can efficiently and automatically provide a set of model solutions characterized by different levels of sharpness and variable lateral consistencies. In terms of data misfit, the obtained set of solutions contains equivalent models allowing us also to investigate the non-uniqueness of FDEM data inversion.

Evaluation of three approaches for simulating 3-D time-domain electromagnetic data

SUMMARY We implemented and compared the implicit Euler time-stepping approach, the inverse Fourier transform-based approach and the Rational Arnoldi method for simulating 3-D transient electromagnetic data. We utilize the finite-element method with unstructured tetrahedral meshes for the spatial discretization supporting irregular survey geometries and anisotropic material parameters. Both, switch-on and switch-off current waveforms, can be used in combination with direct current solutions of Poisson problems as initial conditions. Moreover, we address important topics such as the incorporation of source currents and opportunities to simulate impulse as well as step response magnetic field data with all approaches for supporting a great variety of applications. Three examples ranging from simple to complex real-world geometries and validations against external codes provide insight into the numerical accuracy, computational performance and unique characteristics of the three applied methods. We further present an application of logarithmic Fourier transforms to convert transient data into the frequency domain. We made all approaches available in the open-source Python toolbox custEM, which previously supported only frequency-domain electromagnetic data. The object-oriented software implementation is suited for further elaboration on distinct modelling topics and the presented examples can serve for benchmarking other codes.

Inversion of electromagnetic induction data using a novel wavelet-based and scale-dependent regularization term

SUMMARY The inversion of electromagnetic induction data to a conductivity profile is an ill-posed problem. Regularization improves the stability of the inversion and a smoothing constraint is typically used. However, the conductivity profiles are not always expected to be smooth. Here, we develop a new inversion scheme in which we transform the model to the wavelet space and impose a sparsity constraint. This sparsity constrained inversion scheme will minimize an objective function with a least-squares data misfit and a sparsity measure of the model in the wavelet domain. A model transform to the wavelet domain allows to investigate the temporal resolution (periodicities at different frequencies) and spatial resolution (location of the peaks) characteristics of the model, and penalizing small-scale coefficients effectively reduces the complexity of the model. The novel scale-dependent regularization term can be used to favour either blocky or smooth structures, as well as high-amplitude models in globally smooth structures in the inversion. Depending on the expected conductivity profile, a suitable wavelet basis function can be chosen. The scheme supports multiple types of regularization with the same algorithm and is thus flexible. Finally, we apply this new scheme on a frequency domain electromagnetic sounding data set, but the scheme could equally apply to any other 1-D geophysical method.

2D Inversion of Frequency-domain Electromagnetic Data Generated by Line Current Source

The inversion of electromagnetic data is an inherently non-unique and unstable problem due to the data noises and incomplete data coverage, which means different geo-electrical models could fit the observed data at the same level. A stable solution of an ill-posed inverse problem can be obtained by the regularization methods in which some desired structure are imposed to stabilize the inverse problem. We have developed an algorithm to invert frequency-domain electromagnetic data generated by a line current source for a 2D model of the earth. To stabilize the inversion, we adopt a smoothness-constraint approach for the model parameters and adjust the regularization parameter objectively using an adaptive regularization parameter technique. For calculating the sensitivities using any of the accurate methods will be very time-consuming, we applied the adjoint-equation approximate method. By means of the numerical examples using synthetic data, we have demonstrated that the inversion method can be effectively to reconstruct the subsurface resistivity structure.

Multi-Line 1D Inversion of Frequency-Domain Helicopter-Borne Electromagnetic Data with Weighted 3D Smoothness Regularization: A Case Study from Northern Iran

An efficient pseudo-3D Occam’s inversion scheme is proposed here to stabilize the traditional 1D inversion for the frequency-domain helicopter-borne electromagnetic (FHEM) data. In this scheme, multiple flight lines are inverted simultaneously for layered 1D models minimizing a common objective function with lateral, vertical, and cross-line in the model regularization function. Applying the lateral, vertical, and cross-line weighting factors into the regularization matrix yields a more stable solution and produces geologically more realistic results. In addition, we investigate how the errors of height measurements obscure the FHEM response and affect recovered resistivity models. In this inversion, attempt is made to recover a correct altitude that deals with distortions caused by the presence of measurement height errors in the reconstructed resistivity models. The comparison among 1D, pseudo-2D, and pseudo-3D Occam’s inversions is made through the analysis of data from two different 3D synthetic models and one field dataset acquired from the north of Iran. The results indicate that pseudo-3D Occam’s inversion provides fewer inversion artifacts, better model recognition, and smoother and more continuous models, while, reduces the effects of data noise in an effective manner.

Inversion of magnetic and frequency-domain electromagnetic data for investigating lithologies associated with gold mineralization in the Canadian Malartic area, Québec, Canada

Canadian Malartic is an Archean low-grade bulk tonnage native gold deposit. The deposit is mostly located in altered clastic metasedimentary rocks, mafic–ultramafic dykes, and monzodioritic porphyry intrusions. Airborne magnetic and frequency-domain electromagnetic (EM) data were inverted to reconstruct the geological units associated with the mineralization, especially the intrusive masses. The 3-D inversion of magnetic data, which used a tetrahedral mesh to a depth of 2.4 km, shows that mafic volcanic rocks and iron formation rocks extend to depth in the area, more so than diabase dykes. The magnetic inversion also shows that the diorite and monzodiorite rocks of the Lac Fournière A pluton are dipping toward the south on its northern edge at the contact with the metasedimentary rocks. The 1-D inversion of the frequency-domain EM data, for both electrical conductivity and magnetic susceptibility, is able to reconstruct geological structures to a depth of approximately 100 m, providing more details and information about these features. The intrusive masses such as diabase dykes, diorite and monzodiorite rocks, and mafic volcanic rocks are reconstructed as electrically conductive structures in the inversion results. The metasedimentary rocks are resistive, and the overburden is conductive in most of the area. The geophysical data and inversion results suggest the presence of some features (such as diabase dykes and monzodiorite rocks) that are not yet present on some parts of the geology map. A comparison of the EM-derived susceptibility and the magnetic-derived susceptibility over the iron formations can reveal the effect of remanent magnetization.

Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data

Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies.

Characterizing the diverse hydrogeology underlying rivers and estuaries using new floating transient electromagnetic methodology

The hydrogeology below large surface water features such as rivers and estuaries is universally under-informed at the long reach to basin scales (tens of km+). This challenge inhibits the accurate modeling of fresh/saline groundwater interfaces and groundwater/surface water exchange patterns at management-relevant spatial extents. Here we introduce a towed, floating transient electromagnetic (TEM) system (i.e. FloaTEM) for rapid (up to 15 km/h) high resolution electrical mapping of the subsurface below large water bodies to depths often a factor of 10 greater than other towed instruments. The novel FloaTEM system is demonstrated at a range of diverse 4th through 6th-order riverine settings across the United States including 1) the Farmington River, near Hartford, Connecticut; 2) the Upper Delaware River near Barryville, New York; 3) the Tallahatchie River near Shellmound, Mississippi; and, 4) the Eel River estuary, on Cape Cod, near Falmouth, Massachusetts. Airborne frequency-domain electromagnetic and land-based towed TEM data are also compared at the Tallahatchie River site, and streambed geologic scenarios are explored with forward modeling. A range of geologic structures and pore water salinity interfaces were identified. Process-based interpretation of the case study data indicated FloaTEM can resolve varied sediment-water interface materials, such as the accumulation of fines at the bottom of a reservoir and permeable sand/gravel riverbed sediments that focus groundwater discharge. Bedrock layers were mapped at several sites, and aquifer confining units were defined at comparable resolution to airborne methods. Terrestrial fresh groundwater discharge with flowpaths extending hundreds of meters from shore was also imaged below the Eel River estuary, improving on previous hydrogeological characterizations of that nutrient-rich coastal exchange zone. In summary, the novel FloaTEM system fills a critical gap in our ability to characterize the hydrogeology below surface water features and will support more accurate prediction of groundwater/surface water exchange dynamics and fresh-saline groundwater interfaces.

Modeling groundwater total dissolved solid from derived electromagnetic data using multiple linear regression analysis: a case study of groundwater contamination

The high concentration of total dissolved solids (TDS) and other physicochemical parameters in groundwater around dumpsites have been used to implicate contamination from decomposed waste materials. A simple multiple linear regression (MLR) TDS model that integrates the TDS data derived from boreholes and hand-dug wells to the geophysical parameters obtained from the frequency-domain electromagnetic (EM) data was developed in this research. This is with a view to efficiently monitor groundwater resources and exploration around the Olusosun dumpsite and its communities. With the aid of the MLR equation, the observed TDS concentration of water samples collected from boreholes and hand-dug wells, and the corresponding estimated ground conductivity data in the vertical dipole mode (VD 40) and horizontal dipole modes (HD 40 and HD 20), obtained from geophysical surveys were regressed in Microsoft Excel software to generate a MLR TDS model. The integrity of the derived TDS model was appraised to examine the possibility of deploying it to investigate the TDS content of groundwater around the study area. The EM data and the resistivity models obtained around the study area confirmed contamination going on around the dumpsite. The developed TDS model can be put to use with high confidence, for groundwater TDS prediction around the study area where there are only terrain conductivity data but with no boreholes parameters. Also, terrain conductivity data alone can be applied to the model to predict the concentration of TDS in groundwater where there are no boreholes and hand-dug wells, therefore reducing the cost and time of determining and monitoring both parameters independently. With the aid of the ArcGIS software, the TDS model was used to generate TDS estimate map for the area. The knowledge of the TDS variability in such a map could give a clue about the integrity of the underground water around the site.

3D interpretation of helicopter-borne frequency-domain electromagnetic (HEM) data from Rams\xe5 Basin and adjacent areas at And\xf8ya, Norway

The Ramså Basin is situated on the strandflat on the northeastern side of Andøya between Ramså and Skarstein. It is the only known Mesozoic sedimentary basin onshore Norway and therefore represents an easily accessible analogue to study the setting and development of the offshore Mesozoic basins along the Norwegian continental shelf. Basement rocks (100–10,000 Ωm) and sedimentary rocks (5–1000 Ωm) have distinct ranges of the electrical resistivity. Resistivity of the subsurface can be investigated by electric and electromagnetic (EM) methods to delineate the extent of the sedimentary basin. This paper presents the interpretation of helicopter-borne frequency-domain electromagnetic (HEM) data and their correlation with data obtained from borehole logging and two-dimensional (2D) electrical resistivity tomography (ERT). Resistivity data obtained from these methods show a good agreement. Spatially constrained inversion (SCI) of HEM presents a three-dimensional (3D) subsurface resistivity image of a larger area in comparison to what could have been drawn from few ERT lines and borehole logging. HEM interpretation together with other ERT and borehole resistivity data indicates the extent of Ramså Basin beyond the presently mapped boundary. This extension is also supported by interpretation of earlier collected gravity, magnetic and seismic data. The Ramså sedimentary basin shows a wide range of electrical resistivity between ca. 10 and 600 Ωm at different depths. Two highly conductive areas are newly identified by HEM data interpretation. In one of these areas, outcropping graphite and sulfide minerals were found by field observations.

Integrated geophysical investigation techniques for mapping cassava effluent leachate contamination plume, at a dumpsite in Ilero, southwestern Nigeria

The subsurface illuminating strength of integrating Electrical Resistivity (ER) and Frequency Domain Electromagnetic (FDEM) geophysical investigation techniques to evaluate the level of subsurface leachate contamination plume generated by indiscriminate dumping of cassava product processing effluents and wastes around a dumpsite near cassava processing mill at Ilero southwestern Nigeria, has been demonstrated in this study. The lateral spread as well as depth extent of the cassava waste effluent leachate plume was determined from the subsurface resistivity and conductivity models generated from combined ER and FDEM geophysical surveys carried out through eight (8) Electrical Resistivity Tomography (ERT), eight (8) FDEM and (2) Vertical Electrical Sounding (VES) measurement profiles and stations purposively established across the dumpsite.Quality checked, inverted and depth matched ER and FDEM data generated 1D, 2D, and 3D resistivity and conductivity distribution in the form of subsurface logs, sections, constant depth maps and earth model employed to characterize the study area. Electrical resistivity result presents resistivity distribution that range in value between 21 Ωm to 213 Ωm from the topsoil to depths beyond 7.5 m. The FDEM sections and depth maps show conductivity distribution range between 4 mS/m – 286 mS/m across the dump site and provided information up to a depth of 60 m. The VES results generally delineate three (3) georesistivity layers, which include topsoil, weathered and fractured basement layers, occurring at depth between 0.4 – 10.0 m with characteristic resistivity values from 33.5 Ωm – 37.9 Ωm. The 3D resistivity and conductivity models generated by integrating the various results indicate anomalously low resistivity and relatively high conductivity (21 – 30 Ωm; 11 mS/m – 40 mS/m) zone which defines contamination plume in the central part of the cassava effluents and waste dumpsite.This study has through integrated electrical resistivity and frequency domain electromagnetic geophysical methods of investigation highlighted the subsurface contaminating potential of indiscriminating dumping of agricultural processing effluent and wastes, especially of cassava effluent, which has toxic cyanogenicglucosides constituent that is deleterious to the environment.

Probabilistic 1-D Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator

Frequency-domain electromagnetic (FDEM) data of the subsurface are determined by electrical conductivity and magnetic susceptibility. We apply a Kalman ensemble generator (KEG) to 1-D probabilistic multilayer inversion of the FDEM data to simultaneously derive conductivity and susceptibility. The KEG provides an efficient alternative to an exhaustive Bayesian framework for FDEM inversion, including a measure for the uncertainty of the inversion result. In addition, the method provides a measure for the depth below which the measurement is insensitive to the parameters of the subsurface. This so-called depth of investigation is derived from ensemble covariances. Synthetic and field data examples reveal how the KEG approach can be applied to FDEM data and how FDEM calibration data and prior beliefs can be combined in the inversion procedure. For the field data set, many inversions for 1-D subsurface models are performed at neighboring measurement locations. Assuming identical prior models for these inversions, we save computational time by reusing the initial KEG ensemble across all measurement locations.