Electromagnetic Induction Measurements Research Articles

Characterization of soil drainage using electromagnetic induction measurement of soil magnetic susceptibility

As a physical property of soil, magnetic susceptibility (MS) has been widely used to delineate soil drainage conditions over the past two decades, where all MS values were derived from laboratory measurements. In this paper, the ability of an electromagnetic induction (EMI) instrument, Geonics EM38, in in-field measurements of apparent magnetic susceptibility (MSa or κa) of topsoil was tested on six selected soil profiles having different drainage conditions. The MSa data, measured in both vertical and horizontal coil configurations of EM38, were then inverted using an iterative 1-D inversion procedure to calculate real volume MS of soil layers up to 1 m depth. Moreover, volume and mass-specific MS of the soil profiles were directly measured in laboratory by employing Bartington MS2C and MS2B sensors applied on core and dried soil samples, respectively, collected at the soil profiles. Comparing the results revealed that volume MS profiles derived from inversion of MSa data were positively correlated with MS profiles measured in laboratory, especially at topsoil. It was also found that MS values in poorly drained soil profile were lower than those of a well-drained profile. So, considering that EMI methods are fast, precise and cost-efficient, they would be preferred over conventional methods for delineating soil drainage conditions if using soil MS assessment is considered as soil drainage criteria.

The role of electromagnetic induction in development planning: the case of the Grands Philambins enigma

ABSTRACTSpatial planning aims at constantly improving the distribution of people and activities within available space, especially near cities on ancient agricultural or industrial areas. However, a proper schedule of the development work necessitates the assessment, as precisely as possible, of the difficulties that would be encountered in terms of geotechnical abilities of the terrain and hazards resulting from previous use. In the Grands Philambins site, three different geophysical techniques were combined: multi‐depth resistivity; magnetic gradiometry; and electromagnetic induction. The aim of this study was to map, in a few days and with high measurement densities, all the electromagnetic properties of the terrain and to evidence metallic features. The acquired data were compared with prior documentation, mainly the aerial photos acquired since the middle of the 20th century. The terrain structure is complex, at the junction of geological formations composed of limestones and marls in different proportions, which were characterized by the geophysical survey. It also exhibits metallic features, among which an enigmatic 70 m diameter radially striped disc object, which was identified as non‐ferrous and corresponding to an ancient antenna earthed socket. This case study illustrates the relevance of the use of electromagnetic induction measurements in land planning studies as this method can map three independent underground properties and identify metallic features.

Sensitivity of soil electromagnetic induction measurements to salinity, water content, clay, organic matter and bulk density

Observed variations in the ground electrical conductivity (σb*) measurements obtained with EMI instruments depend on several soil proxies that influence crop growth and development such as salinity (σe), contents of water (θw), clay (wc) and organic matter (wom) and bulk density (ρb). However, the relative contributions of all these to σb* are unknown. This knowledge is needed to improve the planning and interpretation of σb* data for precision agriculture applications. Recently, a semi-empirical model has been developed to relate σb* measurements taken with an EM38 device to σe, θw, wc, wom and ρb and also soil temperature (t). In this work this model was subjected to a global sensitivity analysis (GSA) based on the soil data obtained during two surveys carried out, one in summer and the other in autumn, in an ample irrigated area in SE Spain. On the basis of the multiple linear regression meta-models developed for the σb* measurements, these were found to linearly respond to the soil properties (R2 between 0.92 and 0.96) and thus, the GSA could be based on their standardised regression coefficients. According to these, the soil characteristics explain the following percentages of variance in σb* (PV): 30–34 (σe), 8–20 (θw), 32–47 (wc), 0.6–2.6 (wom), 5.7–7.5 (ρb) and 0.3–0.4 (t) with changes from the summer to the autumn season of − 4, − 12 and + 15 in the PV explained by the most influential properties, respectively, σe, θw and wc. The results of the GSA will help the planning and interpretation of σb* measurements for improving crop management.

Downscaling digital soil maps using electromagnetic induction and aerial imagery

Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping intra-field variability. At the same time, sensor data, such as electromagnetic induction measurements and aerial imagery, can be highly useful for mapping soil properties that correlate with electrical conductivity or soil color. However, maps based on these data nearly always require calibration with local samples, as multiple factors can affect the sensor measurements. In this study, we present a downscaling method, which combines coarse-resolution, large extent soil maps with sensor data in order to improve predictions of soil properties. The method modifies values from coarse-resolution soil maps to predict soil properties at a location, using relationships between soil properties and sensor data from other locations. We test this method for predicting clay and soil organic matter contents at five agricultural fields located in Denmark. We test the method for one field at a time, using soil samples from the four other fields to predict soil properties. The maps produced with the method are generally more accurate than the coarse-resolution soil maps, especially for soil organic matter. The method generally overestimates prediction uncertainties, a disadvantage, which will require improvements. Overall, the method is a simple, promising tool for giving a quantitative estimate of soil properties, when no local soil samples are available.

A semi‐empirical model to predict the EM38 electromagnetic induction measurements of soils from basic ground properties

AbstractElectromagnetic induction (EMI) measurements (σb*) are widely used for the survey of several soil attributes, among which basic properties such as salinity (σe), water content (θw), clay (wc), organic matter (wom) and bulk density (ρb) stand out. In usual practice, purely empirical models relating one of these properties to σb* are calibrated at selected sites. However, this calibration is site and time specific and has to be repeated time and again. In order to understand where the variability of the EMI empirical models comes from, it is necessary to know how the different soil properties contribute to them and, with this aim, a more physically based relationship between σb* and, at least, σe, θw, wc, wom and ρb was developed in this work, additionally including soil temperature (t). It was calibrated and cross‐validated with the data from one survey carried out in a wide agricultural irrigation area in SE Spain, taking σb* measurements with the Geonics EM38 in the horizontal and vertical dipole modes and at various heights over the ground. Then, it was externally validated with the data from a second survey carried out 4 years later in the same area but in a different season. In the calibration, R2 and root mean square error (RMSE) were 0.84 and 0.18 dS m−1 (41%), respectively, for the vertical dipole orientation and 0.90 and 0.11 dS m−1 (39%) for the horizontal one. In the external validation, R2 and RMSE were 0.80 and 0.24 dS m−1 (44%), respectively, for the vertical dipole orientation and 0.90 and 0.13 dS m−1 (38%) for the horizontal one. Therefore, because the performance of the model barely worsened as time passed by, it can be considered to represent the underlying physical process and, therefore, to increase our understanding of how the soil EMI signals are generated, with potential benefits for the planning and comparability of EMI soil measurements, specifically with the EM38, among different areas.Highlights A semi‐empirical model was developed to predict soil EMI measurements from basic ground properties. Salinity, water content, clay, organic matter, bulk density and temperature were used as predictors. The model was able to explain between 80 and 90% of the variance in EMI measurements in the validation. This model helps us understand how the basic soil properties contribute to the EMI measurements.

Remote Sensing, Archaeological, and Geophysical Data to Study the Terramare Settlements: The Case Study of Fondo Paviani (Northern Italy)

During the Middle and Recent Bronze Age, the Po Plain and, more broadly Northern Italy were populated by the so-called “Terramare”, embanked settlements, surrounded by a moat. The buried remains of these archaeological settlements are characterized by the presence of a system of palaeo-environments and a consequent natural gradient in soil moisture content. These differences in the soil are often firstly detectable on the surface during the seasonal variations, with aerial, satellite, and Laser Imaging Detection and Ranging (LIDAR) images, without any information on the lateral and in-depth extension of the related buried structures. The variation in the moisture content of soils is directly related to their differences in electrical conductivity. Electrical resistivity tomography (ERT) and frequency domain electromagnetic (FDEM), also known as electromagnetic induction (EMI) measurements, provide non-direct measurements of electrical conductivity in the soils, helping in the reconstruction of the geometry of different buried structures. This study presents the results of the multidisciplinary approach adopted to the study of the Terramare settlement of Fondo Paviani in Northern Italy. Remote sensing and archaeological data, collected over about 10 years, combined with more recent ERT and FDEM measurements, contributed to the analysis of this particular, not yet wholly investigated, archaeological site. The results obtained by the integrated multidisciplinary study here adopted, provide new useful, interesting information for the archaeologists also suggesting future strategies for new studies still to be conducted around this important settlement.

A multi-technique approach to determine temporal and spatial variability of groundwater-stream water exchange

Characterizing the spatio-temporal distribution of groundwater-surface water exchange fluxes is of paramount importance in understanding catchment behavior. A wide range of field-based techniques are available for such characterization. The objective of this study is to quantify the spatio-temporal distribution of the exchange fluxes along the Cakit stream (Nigde, Turkey) through coupling a set of geophysical techniques and in-stream measurements in a hierarchical manner. First, geological and water quality information were combined at the catchment scale to determine key areas for reach-scale focus. Second, electromagnetic induction (EMI) surveys were conducted along the reach to pinpoint potential groundwater upwelling locations. EMI anomalies guided our focus to a 665 meter-long reach of the stream. Along this selected reach, a fiber-optic distributed temperature sensing (FO-DTS) system was utilized to investigate streambed-temperature profiles at fine spatial and temporal scales. Furthermore, vertical hydraulic gradients and exchange fluxes were investigated using nested piezometers and vertical temperature profiles, respectively, at two potential upwelling locations and a potential downwelling location identified by previous surveys. The results of the study reveal heterogeneity of vertical water-flow components with seasonal variability. The EMI survey was successful in identifying a localized groundwater upwelling location. FO-DTS measurements revealed a warm temperature anomaly during cold air temperature and low streamflow conditions at the same upwelling site. Our point-based methods, namely vertical temperature profiles and vertical hydraulic gradient estimates, however, did not always provide consistent results with each other and with EMI and DTS measurements. This study, therefore, highlights the opportunities and challenges in incorporating multi-scale observations in a hierarchical manner in characterization of the groundwater-surface water exchange processes that are known to be highly heterogeneous in time and space. Overall, a combination of different methods helps to overcome the limitations of each single method and increases confidence in the obtained results.

Mapping depth to the argillic horizon on historically farmed soil currently under forests

The Piedmont region of the southeastern United States experienced a period of accelerated erosion in the 1800s. Clear-cutting of the forests coupled with soil tilling and inadequate erosion control practices led to substantial soil redistribution and loss. This redistribution exposed the subsoil clay (argillic) horizon in many locations and altered hydrologic processes (i.e., infiltration or interflow) across the landscape. Describing the current hydrologic and biogeochemical processes in this landscape requires a spatially explicit mapping of soil re-distribution and the current depth to the argillic horizon. To achieve this mapping, we measured the depth to the argillic horizon in highly eroded (historically farmed) and undisturbed hillslopes (reference areas) by direct measures—soil augur and tile push probe—and by electromagnetic induction (EMI) combined with ancillary data (i.e., terrain features). Direct measures indicated that the depth to the argillic horizon in the historically farmed watersheds ranged from 26 ± 2 to 87 ± 12 cm in ridge-top and toe-slope, respectively, while in the reference hillslopes depths from ridge-top to toe-slope only ranged from 30 ± 3 to 48 ± 12 cm. Using remotely-sensed data, the best predictive models of the depth to the argillic horizon were based on measurements of the site topographic characteristics (percent slope and hillslope position), along with the following EMI data products: the 2 m horizontal coplanar (HCP) EMI measurement and some of its derivatives (i.e., 2 m HCP – 2 m perpendicular, 2 m HCP – 1 m HCP). Our data suggest that in this previously farmed and reforested landscape, geophysical sensing is an efficient means of predicting depth-to-clay when combined with landscape feature characteristics (R2 = 0.69).

Effect of fertilizers and irrigation on multi‐configuration electromagnetic induction measurements

AbstractElectromagnetic induction (EMI) data are often used to investigate spatial and temporal patterns of soil texture, soil water content and soil salinity. We hypothesized that the EMI methodology might thus also offer potential to detect agricultural legacy effects originating from fertilizer application and irrigation of different fields. Therefore, we performed EMI measurements on two long‐term field experiments (LTFE) at Thyrow near Berlin (Germany) that differed in agricultural management with regard to long‐term irrigation in combination with mineral (NPK and lime) and organic amendments (straw and farmyard manure). Two different rigid‐boom multi‐coil EMI instruments were used to measure simultaneously the apparent electrical conductivity (ECa) over nine different depth ranges to study the entire soil profile from topsoil to deep subsoil. Additionally, soil samples were taken from the different treatments to ground‐truth the measurements and disentangle the nutrient application or irrigation effects from natural soil heterogeneity. The soil samples indicated a rather homogenous soil and the correlation between soil parameters or states were not significant. However, the treatments showed significant differences in measured ECa values. In general, ECa values were largest on regularly irrigated as well as on mineral and organic fertilized plots, with regular irrigation exhibiting the largest impact on EMI records even though the last application was months before the EMI measurement. Overall, this study reveals that EMI data can support the classical in situ assessment of agricultural management effects within LTFE, while offering new potentials in detecting and understanding legacy effects of agricultural management on spatial soil properties at farm level.

Performance Analysis of Parameter Estimation in Electromagnetic Induction Data

We derive the Cramer–Rao lower bound (CRB) for target tensor amplitudes and target location parameters that can be estimated from electromagnetic induction (EMI) measurements of a target. In deriving the bound, no restrictions are placed on the target type, target orientation, quantity, or location of the measurement positions, nor the geometry of the EMI sensor or sensor array. The analysis is applicable to both a scanned sensor and a stationary array, as well as both time- and frequency-domain sensors. We show how the bound varies as a function of target type, orientation, depth, and signal-to-noise ratio. In addition, we illustrate the ways to use the CRB and the Fisher information matrix to analyze the relationships between the tensor amplitudes and location parameters. We show results of the CRB analysis applied to an experimental frequency-domain Georgia Tech sensor for a few target types for a nominal 2-D scan geometry as well as the Time-domain Electromagnetic Multi-sensor Towed Array Detection System. We also provide an algorithm for estimating the target location and tensor that achieves the CRB.

Pattern Extraction of Topsoil and Subsoil Heterogeneity and Soil‐Crop Interaction Using Unsupervised Bayesian Machine Learning: An Application to Satellite‐Derived NDVI Time Series and Electromagnetic Induction Measurements

AbstractThe link between remotely sensed surface vegetation performances with the heterogeneity of subsurface physical properties is investigated by means of a Bayesian unsupervised learning approach. This question has considerable relevance and practical implications for precision agriculture as visible spatial differences in crop development and yield are often directly related to horizontal and vertical variations in soil texture caused by, for example, complex deposition/erosion processes. In addition, active and relict geomorphological settings, such as floodplains and buried paleochannels, can cast significant complexity into surface hydrology and crop modeling. This also requires a better approach to detect, quantify, and analyze topsoil and subsoil heterogeneity and soil‐crop interaction. In this work, we introduce a novel unsupervised Bayesian pattern recognition framework to address the extraction of these complex patterns. The proposed approach is first validated using two synthetic data sets and then applied to real‐world data sets of three test fields, which consists of satellite‐derived normalized difference vegetation index (NDVI) time series and proximal soil measurement data acquired by a multireceiver electromagnetic induction geophysical system. We show, for the first time, how the similarity and joint spatial patterns between crop NDVI time series and soil electromagnetic induction information can be extracted in a statistically rigorous means, and the associated heterogeneity and correlation can be analyzed in a quantitative manner. Some preliminary results from this study improve our understanding the link of above surface crop performance with the heterogeneous subsurface. Additional investigations have been planned for further testing the validity and generalization of these findings.

Geometrical characterization of urban fill by integrating the multi‐receiver electromagnetic induction method and electrical resistivity tomography: A case study in Poitiers, France

A geophysical survey including electromagnetic induction (EMI) and electrical resistivity tomography (ERT) methods was applied and assessed with a 40‐trench sampling grid to delineate the geometry of an urban fill layer. Classical investigation techniques, such as excavation, offer localized information and suffer from time and budget constraints for environmental assessments. Near‐surface geophysics can provide the required spatial sampling to evaluate the coverage of anthropogenic soils in a time‐effective and quasi‐continuous manner. Fast‐acquisition and high‐spatial‐coverage EMI mapping and high‐vertical‐resolution ERT were implemented to delineate a suspicious urban fill on a 5‐ha site in a suburban region, which will host the buildings of a commercial complex. The ERT data and 40 trench excavations revealed an urban fill thickness ranging from 0.4 to 3.6 m overlying a calcareous substratum. After ERT–EMI calibration, a two‐layer model was introduced into the one‐dimensional (1‐D) inversion of the EMI data for estimating the geometry of the urban fill layer across the study site. The EMI 1‐D inversion results indicated that the predicted urban fill thicknesses were consistent with 70% of the measured values (27 out of 40 excavation sites). Resistive ground, large 3‐D structures and the heterogeneity of urban fill affected the EMI and ERT measurements and increased the difficulty of estimating its spatial distribution. In this paper, we present a measurement protocol that can guide land‐use development and be reproduced to investigate brownfield sites.Highlights Urban soils are poorly characterized by localized investigations for environmental assessments. Spatial EMI data from an urban fill context were calibrated by ERT for thickness inversion. The ERT–EMI inversion reconstructed the urban fill thickness for 70% of the excavation points. Geophysical techniques can reduce uncertainties for site rehabilitation and land‐use development.

Large-scale soil mapping using multi-configuration EMI and supervised image classification

Reliable and high-resolution subsurface characterization beyond the field scale is of great interest for precision agriculture and agro-ecological modelling because the shallow soil (~1–2 m depth) is responsible for the storage of moisture and nutrients that are accessible to crops. This can potentially be achieved with a combination of direct sampling and Electromagnetic Induction (EMI) measurements, which have shown great potential for soil characterization due to their non-invasive nature and high mobility. However, only a few studies have used EMI beyond the field scale because of the challenges associated with a consistent interpretation of EMI data from multiple fields and acquisition days. In this study, we performed a detailed EMI survey of an area of 1 km2 divided in 51 agricultural fields where previous studies showed a clear connection between crop performance and soil properties. In total, nine apparent electrical conductivity (ECa) values were measured at each location with a depth of investigation ranging between 0–0.2 to 0–2.7 m. Based on the combination of ECa maps and available soil maps, an a priori interpretation was performed and four sub-areas with characteristic sediments and ECa were identified. Then, a supervised classification methodology was used to divide the ECa maps into areas with similar soil properties. In a next step, soil profile descriptions to a depth of 2 m were obtained at 100 sampling locations and 552 samples were analyzed for textural characteristics. The combination of the classified map and ground truth data resulted in a 1 m resolution soil map with eighteen units with a typical soil profile and texture information. It was found that the soil profile descriptions and texture of the EMI-based soil classes were significantly different when compared using a two-tailed t-test. Moreover, the high-resolution soil map corresponded well with patterns in crop health obtained from satellite imagery. It was concluded that this novel EMI data processing approach provides a reliable and cost-effective tool to obtain high-resolution soil maps to support precision agriculture and agro-ecological modelling.

A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture

Application of Precision Agriculture requires an accurate assessment of fine-resolution spatial variation. At present, advances in proximal sensing and spatial data analysis are available to characterize soil systems and detect changes in physical or chemical properties useful to understand and manage the variation within fields in a site-specific way. The objective of this work was to verify the suitability of geostatistical techniques to fuse data measured with different geophysical sensors for delineating homogeneous within-field zones for Precision Agriculture. A geophysical survey, using electromagnetic induction (EMI) and ground penetrating radar (GPR), was carried out at Montecorvino Rovella in the southern Apennines (Salerno, Italy). Both sensors (EMI and GPR) enabled the assessment of variation of soil dielectric properties both laterally and vertically. The study area is a 5 ha terraced olive grove under organic cropping. The sensor surveys were carried out along the terraces and over the entire field. The multi-sensor data were analyzed using geostatistical techniques to estimate synthetic scale-dependent regionalized factors. The results allowed the division of the study area into smaller areas, characterized by different properties that could impact agronomic management. In particular, a large area was delineated in the northern part of the grove, where apparent soil electrical conductivity and radar attenuation were greater. Through soil profiling it was shown that soils of the northern macro-area refer to deep, well developed, clayey Luvic Phaezem, whereas soils of the southern macro-area are shallower and less developed, sandy loam Leptic Calcisol. The proposed geostatistical approach effectively combined the complementary 2D EMI and 3D GPR measurements, to delineate areas characterized by different soil horizontal and vertical conditions. This within-olive grove partition might be advantageously used for site-specific tillage and fertilization.

Interpretation of deep directional resistivity measurements acquired in high-angle and horizontal wells using 3-D inversion

The interpretation of resistivity measurements acquired in high-angle and horizontal wells is a critical technical problem in formation evaluation. We develop an efficient parallel 3-D inversion method to estimate the spatial distribution of electrical resistivity in the neighbourhood of a well from deep directional electromagnetic induction measurements. The methodology places no restriction on the spatial distribution of the electrical resistivity around arbitrary well trajectories. The fast forward modelling of triaxial induction measurements performed with multiple transmitter–receiver configurations employs a parallel direct solver. The inversion uses a pre-conditioned gradient-based method whose accuracy is improved using the Wolfe conditions to estimate optimal step lengths at each iteration. The large transmitter–receiver offsets, used in the latest generation of commercial directional resistivity tools, improve the depth of investigation to over 30 m from the wellbore. Several challenging synthetic examples confirm the feasibility of the full 3-D inversion-based interpretations for these distances, hence enabling the integration of resistivity measurements with seismic amplitude data to improve the forecast of the petrophysical and fluid properties. Employing parallel direct solvers for the triaxial induction problems allows for large reductions in computational effort, thereby opening the possibility to invert multiposition 3-D data in practical CPU times.

Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo

Abstract. A substantial interpretation of electromagnetic induction (EMI) measurements requires quantifying optimal model parameters and uncertainty of a nonlinear inverse problem. For this purpose, an adaptive Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to assess multi-orientation and multi-offset EMI measurements in an agriculture field with non-saline and saline soil. In MCMC the posterior distribution is computed using Bayes' rule. The electromagnetic forward model based on the full solution of Maxwell's equations was used to simulate the apparent electrical conductivity measured with the configurations of EMI instrument, the CMD Mini-Explorer. Uncertainty in the parameters for the three-layered earth model are investigated by using synthetic data. Our results show that in the scenario of non-saline soil, the parameters of layer thickness as compared to layers electrical conductivity are not very informative and are therefore difficult to resolve. Application of the proposed MCMC-based inversion to field measurements in a drip irrigation system demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provides useful insight about parameter uncertainty for the assessment of the model outputs.

Spatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data

Abstract Monitoring spatiotemporal variations of soil water content (θ) is important across a range of research fields, including agricultural engineering, hydrology, meteorology and climatology. Low frequency electromagnetic induction (EMI) systems have proven to be useful tools in mapping soil apparent electrical conductivity (σa) and soil moisture. However, obtaining depth profile water content is an area that has not been fully explored using EMI. To examine this, we performed time-lapse EMI measurements using a CMD mini-Explorer sensor along a 10 m transect of a maize field over a 6 day period. Reference data were measured at the end of the profile via an excavated pit using 5TE capacitance sensors. In order to derive a time-lapse, depth-specific subsurface image of electrical conductivity (σ), we applied a probabilistic sampling approach, DREAM ( Z S ) , on the measured EMI data. The inversely estimated σ values were subsequently converted to θ using the Rhoades et al. (1976) petrophysical relationship. The uncertainties in measured σa, as well as inaccuracies in the inverted data, introduced some discrepancies between estimated σ and reference values in time and space. Moreover, the disparity between the measurement footprints of the 5TE and CMD Mini-Explorer sensors also led to differences. The obtained θ permitted an accurate monitoring of the spatiotemporal distribution and variation of soil water content due to root water uptake and evaporation. The proposed EMI measurement and modeling technique also allowed for detecting temporal root zone soil moisture variations. The time-lapse θ monitoring approach developed using DREAM ( Z S ) thus appears to be a useful technique to understand spatiotemporal patterns of soil water content and provide insights into linked soil moisture vegetation processes and the dynamics of soil moisture/infiltration processes.

Mapping Soil Texture Using Geostatistical Interpolation Combined With Electromagnetic Induction Measurements

ABSTRACTSoil texture influences many physical and chemical properties that affect fertility and productivity. Assessing the spatial distribution of soil texture is necessary to implement management practices that avoid soil degradation. The objective of this study was to evaluate the usefulness of s

Electrical resistivity dynamics beneath a fractured sedimentary bedrock riverbed in response to temperature and groundwater–surface water exchange

Abstract. Bedrock rivers occur where surface water flows along an exposed rock surface. Fractured sedimentary bedrock can exhibit variable groundwater residence times, anisotropic flow paths, and heterogeneity, along with diffusive exchange between fractures and rock matrix. These properties of the rock will affect thermal transients in the riverbed and groundwater–surface water exchange. In this study, surface electrical methods were used as a non-invasive technique to assess the scale and temporal variability of riverbed temperature and groundwater–surface water interaction beneath a sedimentary bedrock riverbed. Conditions were monitored at a semi-daily to semi-weekly interval over a full annual period that included a seasonal freeze–thaw cycle. Surface electromagnetic induction (EMI) and electrical resistivity tomography (ERT) methods captured conditions beneath the riverbed along a pool–riffle sequence of the Eramosa River in Canada. Geophysical datasets were accompanied by continuous measurements of aqueous specific conductance, temperature, and river stage. Time-lapse vertical temperature trolling within a lined borehole adjacent to the river revealed active groundwater flow zones along fracture networks within the upper 10 m of rock. EMI measurements collected during cooler high-flow and warmer low-flow periods identified a spatiotemporal riverbed response that was largely dependent upon riverbed morphology and seasonal groundwater temperature. Time-lapse ERT profiles across the pool and riffle sequence identified seasonal transients within the upper 2 and 3 m of rock, respectively, with spatial variations controlled by riverbed morphology (pool versus riffle) and dominant surficial rock properties (competent versus weathered rock rubble surface). While the pool and riffle both exhibited a dynamic resistivity through seasonal cooling and warming cycles, conditions beneath the pool were more variable, largely due to the formation of river ice during the winter season. We show that surface electrical resistivity methods have the capacity to detect and resolve electrical resistivity transience beneath a fractured bedrock riverbed in response to porewater temperature and specific conductance fluctuations over a complete annual cycle.

Dispersive and directional electrical conductivity and dielectric permittivity of conductive-mineral-bearing samples derived from multifrequency tensor electromagnetic induction measurements

Organic-rich mudrocks, hydrocarbon-bearing conventional formations, and source rocks generally contain pyrite, rutile, graphite, graphitic precursors, and other electrically conductive minerals in the form of veins, laminations, flakes, and grains. Under redox-inactive subsurface conditions, when an external electromagnetic (EM) field is applied to geomaterials containing conductive mineral inclusions, ions in pore-filling brine and charge carriers (electrons and holes) in electrically conductive mineral inclusions migrate, accumulate/deplete, and diffuse around impermeable host-inclusion interfaces. These EM-field-induced phenomena are referred to as perfectly polarized interfacial polarization (PPIP) phenomena, and they alter the effective electrical conductivity [Formula: see text] and effective relative dielectric permittivity [Formula: see text] of geomaterials. In addition, the relaxation process associated with such polarization phenomena and the time required to fully develop and dissipate the EM-field-induced polarization gives rise to frequency dispersion of [Formula: see text] and [Formula: see text] of geomaterials containing conductive mineral inclusions. A laboratory-based EM apparatus, referred to as a whole-core EM induction tool, was used to measure the directional, multifrequency EM response of brine-saturated 4 in diameter (10.16 cm diameter), 2 ft long (0.61 m long), glass-bead packs containing uniformly distributed pyrite and graphite inclusions. We then implemented a semianalytic (SA) EM forward model, referred to as the SA model, to compute the [Formula: see text] and [Formula: see text] of these conductive-mineral-bearing glass-bead packs. The estimated [Formula: see text] and [Formula: see text] of conductive-mineral-bearing packs exhibit directional and frequency dispersive characteristics, which can be explained using the theory of PPIP phenomena. Relative variations in [Formula: see text] and [Formula: see text] due to frequency dispersion were as large as [Formula: see text] and [Formula: see text], respectively, between the values estimated at 20 and 260 kHz. Computed values of [Formula: see text] of conductive-mineral-bearing packs were unusually large in the range of 103–106, whereas the corresponding values of [Formula: see text] exhibited strong dependence on volume content, size, and metallic nature of conductive mineral inclusions, brine salinity, and frequency. Furthermore, packs containing uniformly distributed pyrite and graphite inclusions exhibited conductivity and permittivity anisotropy in the range of one to two.