Electromagnetic Instrument Research Articles

Development of a crop water use monitoring system using electromagnetic induction survey

Monitoring soil water use at the plot level is essential for studying the plant’s response to drought or irrigation. However, the current soil monitoring system using moisture sensors is impractical. Soil electromagnetic induction (EMI) surveys can be a rapid, convenient, and non-invasive solution for monitoring soil moisture changes at the plot level. This study developed a crop water use monitoring system for the plot level using an EMI instrument that collects measurements at 0, 20, 40, 60, and 80 cm above the ground surface. The EMI data were converted to bulk soil electrical conductivity (σ) to the depth 1.5 m via an inversion technique and correlated with measured volumetric moisture contents. The EMI system was first tested for variation in ambient temperature. Within the temperature range of 25.5–34 °C, the standard deviation varied between 0.84 and 3.45 mS m−1 with coefficient of variations 1.54–9.26%. The system was then tested on a field trial at Narrabri, New South Wales, Australia, to measure the water use of chickpea genotypes grown over a season. The total water use of chickpeas obtained from EMI surveys correlated highly with values obtained from the Neutron Moisture Meter (NMM) (R2 = 0.73). Time-series of water content, as measured by the EMI survey, could differentiate the water use of different genotypes. In conclusion, the EMI survey is an efficient technique for monitoring crop water use.

Technical note: Efficient imaging of hydrological units below lakes and fjords with a floating, transient electromagnetic (FloaTEM) system

Abstract. Imaging geological layers beneath lakes, rivers, and shallow seawater provides detailed information critical for hydrological modeling, geologic studies, contaminant mapping, and more. However, significant engineering and interpretation challenges have limited the applications, preventing widespread adoption in aquatic environments. We have developed a towed transient electromagnetic (tTEM) system for a new, easily configurable floating, transient electromagnetic instrument (FloaTEM) capable of imaging the subsurface beneath both freshwater and saltwater. Based on the terrestrial tTEM instrument, the FloaTEM system utilizes a similar philosophy of a lightweight towed transmitter with a trailing offset receiver pulled by a small boat. The FloaTEM system is tailored to the specific freshwater or saltwater application as necessary, allowing investigations down to 100 m in freshwater environments and up to 20 m on saline waters. Through synthetic analysis, we show how the depth of investigation of the FloaTEM system greatly depends on the resistivity and thickness of the water column. The system has been successfully deployed in Denmark for a variety of hydrologic investigations, improving the ability to understand and model processes beneath water bodies. We present two freshwater applications and a saltwater application. Imaging results reveal significant heterogeneities in the sediment types below the freshwater lakes. The saline water example demonstrates that the system is capable of identifying and distinguishing clay and sand layers below the saline water column.

Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems.

Data measured using electromagnetic induction (EMI) systems are known to be susceptible to measurement influences associated with time-varying external ambient factors. Temperature variation is one of the most prominent factors causing drift in EMI data, leading to non-reproducible measurement results. Typical approaches to mitigate drift effects in EMI instruments rely on a temperature drift calibration, where the instrument is heated up to specific temperatures in a controlled environment and the observed drift is determined to derive a static thermal apparent electrical conductivity (ECa) drift correction. In this study, a novel correction method is presented that models the dynamic characteristics of drift using a low-pass filter (LPF) and uses it for correction. The method is developed and tested using a customized EMI device with an intercoil spacing of 1.2 m, optimized for low drift and equipped with ten temperature sensors that simultaneously measure the internal ambient temperature across the device. The device is used to perform outdoor calibration measurements over a period of 16 days for a wide range of temperatures. The measured temperature-dependent ECa drift of the system without corrections is approximately 2.27 mSm−1K−1, with a standard deviation (std) of only 30 μSm−1K−1 for a temperature variation of around 30 K. The use of the novel correction method reduces the overall root mean square error (RMSE) for all datasets from 15.7 mSm−1 to a value of only 0.48 mSm−1. In comparison, a method using a purely static characterization of drift could only reduce the error to an RMSE of 1.97 mSm−1. The results show that modeling the dynamic thermal characteristics of the drift helps to improve the accuracy by a factor of four compared to a purely static characterization. It is concluded that the modeling of the dynamic thermal characteristics of EMI systems is relevant for improved drift correction.

Electromagnetic Waves Can Help Improve the Rate of Increase of Milk Feeds Per Day in Premature Infants With Necrotizing Enterocolitis: A Pilot Trial.

ObjectiveTo evaluate the effects of electromagnetic waves generated by a commercial medical electromagnetic instrument (trade name, TDP, the Chinese phonetic abbreviation of “Te-ding Dian-ci-bo Pu”) as an adjuvant to improve the rate of increase of milk feeds per day by premature infants with necrotizing enterocolitis (NEC).MethodsThis study was a prospective randomized clinical trial. A total of 103 premature infants were diagnosed with NEC II, but there was no need for surgery. The infants were randomly divided into the TDP intervention group and the control group by a randomized method using SPSS 24.0. The patients in the TDP intervention group were treated with TDP irradiation and routine interventions; those in the control group were treated with routine interventions. The rate of increase of milk feeds per day, the time to achieve total gastrointestinal nutrition, the velocity of weight gain, and the complication incidence rate were recorded and compared.ResultsThe rate of increase of milk feeds per day in the TDP intervention group was significantly greater than that in the control group [14.51 (11.58~22.11) ml/kg/d vs. 10.15 (6.15~15.87) ml/kg/d, P = 0.002]. Compared to the control group, the time to achieve total gastrointestinal nutrition (21.45 ± 1.87 d vs. 36.43 ± 2.585 d, P = 0.000) and the velocity of weight gain (19.65 ± 15.27% vs. 13.68 ± 7.15%, P = 0.013) in the TDP intervention group were substantially better than those in the control group. The complication incidence rate was not significantly different between the two groups (P > 0.05).ConclusionTreatment with TDP-generated electromagnetic waves improved the volume of milk consumed per day in premature infants with NEC II and were conducive to improving their clinical outcomes.

Standoff High-Frequency Electromagnetic Induction Response of Unsaturated Sands: A Tank-Scale Feasibility Study

Standoff electromagnetic induction (EMI) measurements of complex conductivity and complex permittivity for engineering soil properties have the potential to revolutionize the way the US Army handles route planning and infrastructure assessment. An unmanned aerial system (UAS) based EM platform for soil interrogation would have wide reaching impact in a variety of applications including: civil infrastructure inspection, in-theater ingress and egress routing, reduction of false positives in IED detection, and permafrost mapping, among many others. Traditional frequency domain EMI instruments assess conductivity at low-frequencies, generally in the range of 1–20 kHz; however, recent advancements have resulted in instrumentation targeting a broadband range of frequencies, from 10 kHz through 20 MHz. This advancement, known as high-frequency electromagnetic induction (HFEMI) allows the potential to evaluate frequency domain relaxation effects in soils by acquiring both the in phase and quadrature response of the secondary field from the soil. Relaxation phenomena such as induced polarization and dielectric permittivity are related to important soil properties that can potentially be exploited using this HFEMI system. While conductivity measurements using the quadrature component of the EMI response are well established in EMI instrumentation, understanding of the relationship between direct electrical measurements and standoff HFEMI measurements is lacking. In an effort to illuminate this relationship between various electrical and electromagnetic methods at a scale suitable for soil property estimation, we perform side-by-side measurements using galvanic geoelectrical methods (ERT, IP), electromagnetics, time-domain reflectometry (TDR) and ground penetrating radar (GPR). We compare HFEMI obtained quadrature and in-phase responses to ERT, IP, TDR and GPR measurements. A tank-scale test cell was developed for comparison of the above methods and allowed assessment of sand at varying saturation levels. Further, the HFEMI response at varying heights above the sand surface was also assessed. Qualitative observations are reported in an initial attempt to relate the HFEMI response to important soil parameters.

Delineation of Nitrate Reduction Hotspots in Artificially Drained Areas through Assessment of Small-Scale Spatial Variability of Electrical Conductivity Data.

Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by utilizing electrical conductivity (EC) values derived by the inversion of apparent electrical conductivity data measured by an electromagnetic induction instrument. The NRH areas were derived from the subzones generated from clustering the EC values via two methods, unsupervised ISODATA clustering and the Optimized Hot Spot Analysis, that highly complement each other. The clustering of EC values generated three classes, wherein the classes with high EC values correspond to NRH areas as indicated by their low redox potential values and nitrate (NO3−) concentrations. Nitrate concentrations in the NRH were equal to 13 to 17% of the concentrations in non-NRH areas and occupied 26% of the total area of the drainage catchments in the study. It is likely that, with the identification of NRH areas, the degree of nitrogen reduction in the vadose zone may be higher than initially estimated at the subcatchment scale.

Using machine learning to predict optimal electromagnetic induction instrument configurations for characterizing the shallow subsurface

Abstract. Electromagnetic induction (EMI) is used widely for hydrological and other environmental studies. The apparent electrical conductivity (ECa), which can be mapped efficiently with EMI, correlates with a variety of important soil attributes. EMI instruments exist with several configurations of coil spacing, orientation, and height. There are general, rule-of-thumb guides to choose an optimal instrument configuration for a specific survey. The goal of this study was to provide a robust and efficient way to design this optimization task. In this investigation, we used machine learning (ML) as an efficient tool for interpolating among the results of many forward model runs. Specifically, we generated an ensemble of 100 000 EMI forward models representing the responses of many EMI configurations to a range of three-layer subsurface models. We split the results into training and testing subsets and trained a decision tree (DT) with gradient boosting (GB) to predict the subsurface properties (layer thicknesses and EC values). We further examined the value of prior knowledge that could limit the ranges of some of the soil model parameters. We made use of the intrinsic feature importance measures of machine learning algorithms to identify optimal EMI designs for specific subsurface parameters. The optimal designs identified using this approach agreed with those that are generally recognized as optimal by informed experts for standard survey goals, giving confidence in the ML-based approach. The approach also offered insight that would be difficult, if not impossible, to offer based on rule-of-thumb optimization. We contend that such ML-informed design approaches could be applied broadly to other survey design challenges.

Realization of Multi-Frequency Resonance for a Portable Near-Surface Frequency-Domain Electromagnetic Transmitting System

Portable near-surface frequency-domain electromagnetic (FDEM) instruments are effective tools for detecting underground electrical abnormal targets. However, due to the inductive reactance of the transmitting coil, conventional instruments cannot transmit high amplitude current at multiple frequencies at the same time in the high frequency range (>1000 Hz). This would lead to low detection efficiency and poor signal-to-noise ratio of the system and would ultimately affect the detection accuracy. In view of the above problems, a multi-frequency resonant strategy is proposed to simultaneously increase the transmitting current amplitude at multiple target frequencies. First, the multi-frequency resonance theory is analyzed and the circuit parameter solving method is improved. The validity of the multi-frequency resonance theory and the solution method is verified via circuit simulation. Further, the influence of the component parameter deviation on the circuit resonance is analyzed, and the selection principle of the resonance parameters based on the quality factor Q is presented. Considering the actual deviation, a five-frequency or seven-frequency resonant circuit may achieve a better resonance effect than the nine-frequency resonant circuit in practice. Finally, the strategy is validated with an actual circuit. The results show that the constructed five-frequency resonant circuit can effectively increase the amplitude at the target frequency. The maximum increase is 10.50 times, and the transmitting energy is 11.4 times higher. This study provides a feasible implementation method for multi-frequency resonance at five frequencies and above, which is helpful for improving the detection efficiency and signal-to-noise ratio of a portable near-surface FDEM system.

Soil moisture and electrical conductivity relationships under typical Loess Plateau land covers

AbstractVegetation changes that are driven by soil conservation measures significantly affect subsurface water flow patterns and soil water status. Much research on water consumption and sustainability of newly introduced vegetation types at the plot scale has been done in the Loess Plateau of China (LPC), typically using local scale measurements of soil water content (SWC). However, information collected at the plot scale cannot readily be up‐scaled. Geophysical methods such as electromagnetic induction (EMI) offer large spatial coverage and, therefore, could bridge between the scales. A noninvasive, multicoil, frequency domain, EMI instrument was used to measure the apparent soil electrical conductivity (σa) from six effective depths under four typical land‐covers (shrub, pasture, natural fallow, and crop) in the north of the LPC. Concurrently, SWC was monitored to a depth of 4 m using an array of 44 neutron probes distributed along the plots. The measurements of σa for six effective depths and the integrated SWC over these depths, show consistent behavior. High variability of σa under shrub cover, in particular, is consistent with long term variability of SWC, highlighting the potential unsustainability of this land cover. Linear relationships between SWC and σa were established using cumulative sensitivity forward models. The conductivity–SWC model parameters show clear variation with depth despite lack of appreciable textural variation. This is likely related to the combined effect of elevated pore water conductivity as was illustrated by the simulations obtained with water flow and solute transport models. The results of the study highlight the potential for the implementation of the EMI method for investigations of water distribution in the vadose zone of the LPC, and in particular for qualitative mapping of the vulnerability to excessive vegetation demands and hence, unsustainable land cover.

Digital soil mapping of soil salinity using EM38 and quasi‐3d modelling software (EM4Soil)

AbstractLand development in the form of irrigation has led to increased agricultural productivity, but natural stores of connate salt have led to salinization. To manage salinity, baseline information about the electrical conductivity of a saturated soil paste extract (ECe – dS/m) is necessary. To value add to the limited ECe that can be collected, proximal sensed data from electromagnetic (EM) induction instruments (e.g. EM38) are increasingly being used because the measured apparent soil electrical conductivity (ECa – mS/m) can be correlated with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m), subsoil (0.6–0.9 m) and deep subsoil (0.9–1.2 m) ECe. However, errors may be introduced in prediction, given an EM38 measures ECa at depths of 0–1.5 m in vertical (EM38v) and 0–0.75 m in horizontal (EM38h) mode. To overcome this, we develop a linear regression (LR) between estimates of electrical conductivity (σ – mS/m), inverted from EM38v and EM38h using a quasi‐3d algorithm and measured ECe at the same depths. First, the LR was established (using R2) between estimates of σ inverted from ECa at heights of 0.5 (EM38v0.5 and EM38h0.5) and 0 m (EM38v0 and EM38h0), either alone or in combination, as well as in vertical mode (i.e. EM38v0.5 and EM38v0). ECa were also degraded (100%, 80%, 60% and 40%) to compare loss of prediction agreement (Lin's concordance) and accuracy (root mean square error). We use Normalized Difference Vegetation Index (NDVI) to qualitatively indicate crop growth. Moderate coefficient of determination (R2) was achieved between σ and ECe when we use the EM38v0.5 and EM38h0.5 (0.65) and EM38v0.5 and EM38v0 (0.69), but strong R2 was achieved using EM38v0 and EM38h0 (0.78) and in combination with the EM38v0.5 and EM38h0.5 (0.71). However, while good agreement (Lin's > 0.8) was achieved, during a leave‐one‐out cross‐validation for most EM38 combinations, the best result was achieved using EM38v0 and EM38h0 (Lin's = 0.87). There was also loss in prediction agreement and accuracy using any of the degraded ECa data sets, however. The final 3d map of ECe, as well as NDVI, showed where highly saline (>8 dS/m) areas in the west of most fields resulted as a function of leakage from the Kham‐rean Canal and topography (i.e. lower lying areas). We conclude the approach has broad application to map, potentially manage and monitor large areas of north‐east Thailand.

Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.

Using depth specific electrical conductivity estimates to improve hydrological simulations in a heterogeneous tile-drained field

In agricultural fields, tile drains represent potential pathways for the migration of solutes, such as nitrates, in groundwater and surface water bodies. Tile drain flow is controlled by the temporal and spatial dynamics of the shallow groundwater table, which results from complex interactions between climate, topography and soil heterogeneity. Studies on the effect of topsoil heterogeneity on shallow water and drainage dynamics by fully 3D surface water and groundwater flow modeling are limited. The objective of our study is to demonstrate the use of depth specific electrical conductivity (EC) estimates to improve hydrological simulations in a tile-drained field. The model was applied to a field site in Denmark where times series of drainage discharge and water table elevations are available. Clay-rich soil zones were identified in a tile-drained field using depth specific electrical conductivity estimates generated by the inversion of apparent electrical conductivity data measured using an electromagnetic induction instrument. One model that included the low-permeability clayey zones in the soil layers down to a depth of 1.2 m was compared to a simpler model that assumed homogeneous soil layers. Both models simulate drainage discharge that compares well to the observations. However, including the clayey zones improves the simulation of hydraulic heads, and water table fluctuations, and generates flooded areas that are more representative of those observed during the wet seasons. Our results suggest that the simulation of water table fluctuations can be improved when the soil heterogeneity determined from depth specific EC estimates is included in integrated hydrological models. A better representation of the subsurface flow dynamics will also improve subsequent simulations of the transport and fate of agrochemical substances leaching from fields such as nitrate, which may deteriorate the quality of groundwater and surface water bodies.

Characterization of soil magnetic susceptibility: a review of fundamental concepts, instrumentation, and applications

The characterization of magnetic susceptibility (MS) has become an accepted technique in soil science. This review examines the concept of volume and mass-specific MS, magnetism, frequency dependence, and thermal behavior of MS, as they pertain to soil material. A comparison is presented of the two types of instrumentation for measuring soil MS, based on magnetic field and electromagnetic induction (EMI). These are discussed with respect to applications including magnetic granulometry, detection of pollutants, identification of organic matter, the delineation of drainage class, paleo-environmental studies, archaeology, as well as soil erosion and degradation. Instruments that use magnetic fields can precisely measure the MS of small amounts of soil, thinly deposited layers or soil exposures, but cannot effectively measure materials at distances ≥10 cm from the sensor. EMI instruments, instead, are capable of quickly measuring apparent MS of a finite volume of the soil, and are utilized in mapping of soil MS in agricultural and archaeological investigations; however, the measured apparent MS values need to be further processed to give the real volume MS values of soil layers/segments. Although both kinds of instruments are widely used in soil science, their measured data are not interchangeable. Future work should be conducted to increase the understanding of the comparability of these instruments to find better utility among soil scientists.

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.

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver–pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver–pickup, also termed as transmitter–receiver (T-R) sensor is designed with a small mean radius of 1 mm. In addition, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution, with a limit of 0.2 μm and selected as 0.25 mm. Experiments were conducted for imaging of an aluminium sheet with seven machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10% for tilt angles up to 60 degrees.

The effects of uniform magnetic field on the mechanical and microstructural properties of concrete incorporating steel fibers

This study investigated the effects of applying a uniform magnetic field (UMF) of flux density 500 mili Tesla (mT) to fresh and hardened concrete specimens with steel fibers at volume ratios of 1 and 1.5% on mechanical and microstructural properties. To attain these objectives, compressive and splitting tensile strengths tests were carried out on the specimens with steel fiber-reinforced concrete (SFRC) at 28 days. Furthermore, the microstructure of SFRC, subjected to the UMF, was assessed via scanning electron microscopy (SEM) images. An electromagnetic instrument, capable of generating a density of 500 mT, was used to produce UMF. Finally, a model equation was proposed to predict the splitting tensile strength of SFRC subjected to the UMF as a function of its compressive strength. The application of UMF to SFRC specimens incorporating 1.5% steel fibers revealed an increase in both compressive and splitting tensile strengths up to about 18.2% and 9.5%, respectively. The SEM analysis indicated that the UMF enhanced the cement hydration process, which is responsible for the higher mechanical strength development of SFRC compared to the control specimen.

Ambient temperature and relative humidity–based drift correction in frequency domain electromagnetics using machine learning

ABSTRACTElectromagnetic instrument responses suffer from signal drift that results in a variable response at a given location over time. If left uncorrected, spatiotemporal aliasing can manifest and global trends or abrupt changes might be observed in the data, which are independent of subsurface electromagnetic variations. By performing static ground measurements, we characterized drift patterns of different electromagnetic instruments. Next, we performed static measurements at an elevated height, approximately 4 metre above ground level, to collect a data set that forms the basis of a new absolute calibration methodology. By additionally logging ambient temperature variations, battery voltage and relative humidity, a relation between signal drift and these parameters was modelled using a machine learning (ML) approach. The results show that it was possible to mitigate the effects of signal drift; however, it was not possible to completely eliminate them. The reason is three‐fold: (1) the ML algorithm is not yet sufficiently adapted for accurate prediction; (2) signal instability is not explained sufficiently by ambient temperature, relative humidity and battery voltage; and (3) the black‐box internal (factory) calibration impeded direct access to raw data, which prevents accurate evaluation of the proposed methodology. However, the results suggest that these challenges are not insurmountable and that ML can form a viable approach in tackling the drift problem instrument specific in the near future.

Time‐lapse imaging of soil moisture using electromagnetic conductivity imaging: Wetting phase

AbstractThe Vertosols of the lower Namoi valley in New South Wales, Australia, have been extensively developed for irrigated cotton (Gossypium hirsutum L.) production. However, there are competing demands for water from mining, environmental needs, and agriculture, with climate change forecasts suggesting reduced rainfall and hotter climates. This means irrigators need to improve water efficiency, which requires methods to monitor the soil volumetric water content (θ, m3 m–3). The aim of this study is to assess if a multicoil electromagnetic instrument (DUALEM‐421), which measures soil apparent electrical conductivity (ECa, mS m–1), can be used to value add to limited measurements of θ and allow extrapolation along a uniformly heavy clay irrigation furrow. Specifically, we seek to invert ECa using EM4Soil software to generate electromagnetic conductivity images, whereby the estimates of soil true electrical conductivity (σ, mS m–1) in the topsoil, subsurface, and subsoil are correlated with θ measured by Decagon GS3 sensors installed at the same depths. We also compared the usefulness of inversion of different combinations of DUALEM ECa: DUALEM‐1, DUALEM‐2, and DUALEM‐21. We found estimated σ from inversion of DUALEM‐421 ECa had good agreement (i.e., Lin's concordance = .84) with measured soil bulk electrical conductivity (σb, mS m–1). We explored the relationship between DUALEM‐421 estimated σ and θ using an artificial neural network. The predictions showed the model had good coefficient of determination (R2 = .74) with good accuracy (RMSE = 0.04 m3 m–3) and demonstrated good agreement between measured and predicted θ (Lin's = .84).

Research on the Application of Intelligent Computer Equipment in Higher Vocational Physics Experiment Teaching

With the rapid development of computer, intelligent computer equipment has been gradually applied to the field of teaching, which not only strengthens the authenticity of teaching, but also improves the students’ interest in learning. At present, intelligent computer equipment management applied to higher vocational physics experiment teaching, which has begun to replace the traditional physical experimental instruments, such as simple pointer type, electromagnetic instrument. With the intelligent experimental teaching instruments, the teaching methods, teaching means and teaching quality of physical experiment in higher vocational colleges have been greatly improved. Through the introduction of intelligent experimental platform, higher vocational colleges have realized real-time measurement technology, which is based on computer, sensor and traditional instrument. Through the measurement, processing, storage and output of physical experiments, students can observe and learn physical phenomena more clearly, which also provides a practical and effective teaching environment for experimental teaching. First of all, this paper analyzes the importance of introducing intelligent computer equipment. Then, this paper analyzes the implementation principle of the intelligent experimental platform. Finally, some examples are given.

Effects of salinity and water content on apparent conductivity in an alluvial setting in the Canadian Prairies

Electromagnetic induction surveys are commonly used to assess soil salinity. In this study, a DualEM 1S instrument was used to survey an area in southern Manitoba to characterize sulphate-dominated salinity in clay-rich alluvial soils. The efficacy of predicting apparent conductivity (corrected for departure from low induction number responses) was determined by regression analysis of parameters including saturated-paste salinity, pore-water salinity, volumetric water content, porosity and combinations of these terms determined from 542 soil samples at 65 sampling sites. Predictors were depth-weighted using the V–H mode (exploration depth of 0.5 m) and the V–V mode (exploration depth of 1.5 m) of the electromagnetic instrument. The analysis yielded strong correlations of apparent conductivity (ECa) with saturated-paste or pore water conductivity, with Pearson r2 correlation coefficients exceeding 0.75 for the V–H mode and 0.86 for the V–V mode. The strong correlations of ECa with salinity are explained by moisture conditions being close to field capacity during the survey. These results are similar to those for chloride-dominated salinity. The study yielded less commonly observed results related to the clay-rich soils, including a threshold value of ~ 10% below which volumetric water content does not affect ECa, and an improved prediction of ECa by dividing the porosity data based on clay content. The study results demonstrate the efficacy of using ECa to parameterize sulphate-dominated salinity in clay-rich soils. They also show that salinity can be estimated relatively accurately from saturated paste conductivity at near-field capacity without considering moisture content.