Resistivity Imaging Technique Research Articles

A GUI-BASED DATA ACQUISITION SYSTEM FOR EIT USING TWO CURRENT INJECTION METHODS

The current injection method played an essential role in the data quality of impedance measurement using the electrical impedance tomography (EIT) technique. Previous studies have largely utilized only a single current injection method with limited interface access, which restricts the effectiveness of EIT systems. In this paper, a graphical user interface (GUI)-based data acquisition system was designed to improve user interaction and optimize the control system. The system was equipped with multiple current injection methods, allowing researchers to choose which method to use in the measurement process. The designed hardware comprises a V-to-I converter circuit, multiplexer, electrodes, peak detector, and filter circuit. Testing results indicate that the V to I converter circuit has an average peak difference between voltage and current of 4.86% and an average error of 0.69% in the peak detector circuit. The multiplexer circuit switches quickly and consistently, while the filter circuit remains stable at the 50 kHz frequency used in this study. These results demonstrate that the designed circuits perform adequately and effectively, ensuring reliable and accurate measurements. Additionally, the data acquisition software, presented as a GUI, effectively facilitates the selection of current injection methods and the display of boundary data simulation (BDS) on the object. This is demonstrated by the spatial inhomogeneity patterns visible through BDS in both the adjacent and opposite methods

Enhancing electrical resistance tomography reconstruction from conductive fabric: a comparative study of CycleGAN and pix2pixGAN

Abstract Monitoring fetal health is crucial in prenatal care, and existing techniques for assessing fetal movements are often expensive and limited to clinical environments. This research investigates the potential of utilizing electrical resistance tomography (ERT) with a conductive fabric to create a cost-effective and non-invasive imaging solution for fetal monitoring. The fusion of ERT with wearable e-textile devices facilitates continuous and portable monitoring. To improve the quality of ERT-generated images, we propose the application of CycleGAN and pix2pixGAN, both machine learning models based on generative adversarial networks. These models learn to map reconstructed images to target images, thereby enhancing reconstruction precision and image quality. The outcomes of this research highlight the effectiveness of the suggested method in managing noisy data and achieving superior image generation. This work presents a promising approach to fetal monitoring using ERT and deep learning techniques, opening possibilities for more affordable and accessible prenatal care.

Geophysical and Sedimentological Characterization of Oil Sand Deposit in Part of Eastern Dahomey Basin Southwestern Nigeria

Unconventional oil is the group name given to unconventional petroleum deposits such as oil sands and heavy oils. Oil sand, commonly referred to as tar sand or bituminous sand is a sandstone formation impregnated with viscous bitumen. This study utilized the two-dimensional electrical resistivity imaging technique to characterize oil sand and validate its true resistivity value and sedimentological analysis to evaluate the physical properties of the oil sand deposit. The two-dimension electrical resistivity imaging data acquisition was done along seven profiles systematically in a grid format using dipole-dipole electrode configuration and processed using the RES2DINV software. Fresh samples were collected from three points within the study area for grain sizes and textural properties analysis. The result of the 2-D ERI in the study area showed two to three geoelectric layers and the oil sand being the second layer as evident in the 3-D model obtained from the systemic data acquisition procedure. The resistivity obtained for oil sand in the study area range between 2,500 Ωm to 105 Ωm which validates the report of high resistivity value of oil sand in the study area. The sedimentological analysis showed that the sediments in the study area are of medium sand moderately sorted sand grain and leptokurtic to mesokurtic sand suggesting unimodal and bimodal parent source.

A multi-scale attention residual-based U-Net network for stroke electrical impedance tomography.

Electrical impedance tomography (EIT), a non-invasive, radiation-free, and convenient imaging technique, has been widely used in the diagnosis of stroke. However, due to soft-field nonlinearity and the ill-posed inverse problem, EIT images always suffer from low spatial resolution. Therefore, a multi-scale convolutional attention residual-based U-Net (MARU-Net) network is proposed for stroke reconstruction. Based on the U-Net network, a residual module and a multi-scale convolutional attention module are added to the concatenation layer. The multi-scale module extracts feature information of different sizes, the attention module strengthens the useful information, and the residual module improves the performance of the network. Based on the above advantages, the network is used in the EIT system for stroke imaging. Compared with convolutional neural networks and one-dimensional convolutional neural networks, the MARU-Net network has fewer artifacts, and the reconstructed image is clear. At the same time, the reduction of noisy artifacts in the MARU-Net network is verified. The results show that the image correlation coefficient of the reconstructed image with noise is greater than 0.87. Finally, the practicability of the network is verified by a model physics experiment.

Electrical Resistivity - Tomography Studies In Determining Shallow Aquifer Potential Zones a Case Study in Different Terrains

The study documents the effectiveness of 2 D (Two Dimensional Electrical Resistivity Survey (Tomography) to map shallow subsurface geological formations namely recent Alluvium, Gondwana deposits and hard rock deposits. 2 dimensional Electrical Resistivity Survey (or tomography) was conducted at 2 locations at Valarpuram and one location Madurantakam areas. Gondwana deposits mask Valarpuram Thandalam, villages (at Kancheepuram District, Tamilnadu). To the east of Valarpuram alluvium thickness increases at shallow depths. Weathered and hard granitic gneiss and hard charnockite rocks overlie topsoil in Madurantakam areas (Chengalpettu District, Tamilandu). The survey was conducted to 12 m depth to decipher shallow permeable zones. Gondwana formations consisting of clays, siltstones are predominant in Valarpuram Thandalam and surrounding areas. Hard rock formations are found in Madurantakam areas. 2 Dimensional Resistivity Survey results indicate 2-D sections with very low resistivity values in the range of 2 to 4 ohm-m in Valarpuram Thandalam areas indicating predominant clay deposits with poor ground water potential. To the east of Valarpuram Thandalam resistivity data is of moderately higher value indicating sand deposits at shallow depths. These are ideal locations for dug wells. Low to moderate values at shallow depths indicating weathered thickness up to 12 m are observed at Madurantakam location. From 12m depth steep rise in resistivity values are observed in hard indicating presence of massive rock from 12m depth and below areas. Thus, 2 D Resistivity Imaging Technique are helpful in delineating shallow aquifer potential and this in turn helps in deciding the depth of open wells with high precision.

Groundwater Investigation Using Electrical Resistivity Imaging Technique in Western Ramadi, Iraq

The two-dimensional electrical resistivity imaging technique was used to examine groundwater in western Ramadi. The survey was performed with a factor (n) and a-spacing values of 6 and 5 m, respectively. The inverse models clearly show the resistivity variation between the anomalous parts of the water table and all the sediments. The vertical extension of the saturation sediments is between 1.5–9.5 m, which is identical to the groundwater level in the three existing wells located in the University of Anbar, with an area of 7 kilometers and an area of 18 kilometers. The extreme thickness of the water saturation layer was determined to be about 10.5 meters. It was concentrated in gypsum and mudstone soils with secondary gypsum and siltstone, considered the upper components of the Injana Formation at the University of Anbar. At 7 Kilometer area which starts from a depth of 1 m to depths of up to 12 m, it is also considered a groundwater reservoir. While 18 Km area is considered to have dry soil and the groundwater in it is far from the surface at 12 m depth.

Finite element modeling of the electrical impedance tomography technique driven by machine learning

To create a human-like skin for a robotic application, current touch sensor technologies have a few drawbacks. Electrical Impedance Tomography (EIT) is a candidate for this application due to its applicability over complex geometries; nevertheless, it has accuracy concerns. This study employs artificial neural networks (ANNs) to investigate the accuracy and capability of EIT-based touch sensors. A finite element (FE) model is utilized to solve the forward EIT problem while simultaneously determining the system’s comprehensive mechanical response. The FE model is comprised of a polyurethane (PU) foam domain, a conductive spray layer and a set of sixteen electrodes. To replicate the process of touching the sensor body, a punch of varying diameters and touch forces is utilized. The mechanical response of the sensor body is modeled using the hyperfoam material model calibrated through experimental uniaxial and shear test data, while the electric conductivity of the sprayed skin surface is obtained experimentally as function of applied strain. The viscoelastic behavior of the PU foam material is also obtained experimentally. These experimental data were implemented in the FE model through user subroutines to model the mechanical and electrical properties of the sensor in the EIT forward problem. The traditional EIT inverse problem image reconstruction was replaced utilizing ANNs as an alternative to extract mechanics based parameters. The ANNs were created to predict the spatial coordinates of the touch point, and they were proven to be extremely accurate. Using the EIT voltage readings as input, the ANNs were utilized to forecast the system’s mechanical behavior such as contact pressure, contact area, indentation depth, and touching force.

3-D Printed Graphene-Based Piezoresistive Foam Mat for Pressure Detection Through Electrical Resistance Tomography and Machine Learning Classification Techniques

3-D Printed Graphene-Based Piezoresistive Foam Mat for Pressure Detection Through Electrical Resistance Tomography and Machine Learning Classification Techniques

2D Resistivity Imaging for Leachate Migration in Nkwelle-Ezunaka Farm Estate, Southeastern Nigeria

Leachate effluence from wastewater Leachate migration and solid waste are major sources of soil and groundwater pollution. Assessing the impact of leachate is an active area of soil and groundwater research. This study evaluated the extent of leachate migration at Nkwele-Ezunaka Farm Estate in Anambra State of Nigeria using a 2D resistivity imaging technique. The study area lies within longitude 6° 51' 27'' − 6° 59' 37'' E and latitudes 6° 13' 18'' − 6° 20 '27" N and covers an area of about 60 km2. A total of five profile lines were carried out in a resistivity survey using a digital read-out resistivity meter (ABEM SAS 1000). The data obtained were interpreted using RES2DINV software. A contaminant leachate plume was delineated in 2D resistivity sections as low resistivity zones (less than 80.0 Ωm). The maximum depth of penetration of leachate determined was 10.6 m and hence could not penetrate the groundwater in the study area. The groundwater flow in the study area was revealed to be in the West-East direction.

A Fast Magnetic Flux Density Measurement Method With Skip-Echo Acquired Turbo Spin Echo (SATE).

The measurements of magnetic flux density ( Bz) needed in magnetic resonance electrical impedance tomography (MREIT) and magnetic resonance current density imaging (MRCDI) techniques often utilize spin echo (SE)-based sequences for data acquisition. The low imaging speed of SE-based methods significantly hampers the clinical applications of MREIT and MRCDI. Here, we propose a new sequence for substantially accelerating the acquisition of Bz measurements. A skip-echo acquired turbo spin echo (SATE) imaging sequence based on the conventional turbo spin echo (TSE) technique was proposed by adding a skip-echo module in front of the TSE acquisition module. The skip-echo module consisted of a series of refocusing pulses without acquisition. In SATE, amplitude-modulated crusher gradients were used to remove the stimulated echo pathways, and the radiofrequency (RF) pulse shape was specially selected to preserve more signals. In efficiency evaluation experiments performed on a spherical gel phantom, we demonstrated that SATE had improved measurement efficiency compared to the conventional TSE sequence via skipping one echo before acquiring signals. The accuracy of the Bz measurements by SATE was validated against those by the multi-echo injection current nonlinear encoding (ME-ICNE) method, while SATE was able to accelerate the data acquisition up to 10-fold. Volumetric coverage of Bz maps obtained in the phantom, pork, and human calf illustrated that SATE can reliably measure volumetric Bz distributions within clinically acceptable time. The proposed SATE sequence provides a fast and effective approach for volumetric coverage of Bz measurements, greatly facilitating the clinical applications of MREIT and MRCDI techniques.

Prone Positioning in COVID-19 ARDS: Comment.

We read with interest the recent article by Zarantonello et al.1 on the physiologic effects of prone positioning on ventilation and perfusion distribution, oxygenation, and lung mechanics in COVID-19 acute respiratory distress syndrome patients. We would like to first congratulate the authors for the nicely conducted study that once again proved the clinical applicability of electrical impedance tomography for the assessment of ventilation–perfusion matching at the bedside. We acknowledge the clinical significance of the findings, but we wish to point out that several parts of the electrical impedance tomography methodology were incorrectly described in the article.In the Methods section, the authors described how the lung areas that were (1) perfused but not ventilated, (2) ventilated but not perfused, and (3) both ventilated and perfused were “divided by the sum of ventilated and perfused pixels” to calculate the corresponding relative fractions of the lung area in percentages, i.e., (1) “% shunt area,” (2) “% dead space area,” and (3) “% matched area,” respectively. However, the denominator must be the number of pixels that were ventilated or perfused, i.e., using the logical operator “or,” otherwise the calculated relative areas would be incorrect. The authors also determined “the percentage of total ventilated lung area that was located in the dorsal half of the thorax,” which they inaccurately termed as “dorsal ventilation.” According to the consensus electrical impedance tomography terminology and definitions,2 regional ventilation is determined as the sum of all pixel values of tidal impedance variation not as the pixel count, which defines just the ventilated area.The authors further introduced a new electrical impedance tomography parameter, the “homogeneity index,” which they “calculated as 1 – global inhomogeneity index,” based on the assumption of the global inhomogeneity index ranging from 1 to 0. However, the global inhomogeneity index values are commonly higher than 1 in patients with pronounced ventilation inhomogeneity, as shown already in some of the examined mechanically ventilated subjects in the original description of the global inhomogeneity index3 or recently in patients with exacerbated chronic lung disease.4 An example patient case is presented in figure 1, in which a global inhomogeneity index value of 2.59 was noted in the presence of pendelluft, which is a frequent cause of out-of-phase impedance variation both in spontaneously breathing and ventilated patients.5 The “homogeneity index” proposed by the authors would be negative in this case and assume the confusing value of –1.59. We therefore strongly advise against using the “homogeneity index” as a surrogate for the well established global inhomogeneity index.The authors pointed out that electrical impedance tomography perfusion assessment with saline bolus injection had been validated only in two animal studies. In fact, this technique of contrast-enhanced electrical impedance tomography has been studied in several other animal studies as well.6–9 Last, we agree with the authors that electrical impedance tomography images do not cover the whole lungs, but as summarized in the consensus paper on chest electrical impedance tomography,2 an electrical impedance tomography image does not originate from “only the area of the lung surrounded by the belt.”Electrical impedance tomography receives increasing attention due to its noninvasive bedside monitoring features. Correct description and implementation of the electrical impedance tomography technique and analysis procedures are crucial for further development and clinical use.Dr. Zhao receives a consulting fee from Dräger Medical (Lübeck, Germany). Dr. Frerichs reports funding from the European Union’s Framework program for Research and Innovation Horizon 2020 (WELMO, grant No. 825572).

Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection

Wet foundations and walls of buildings significantly increase the energy consumption of buildings, and the drying of walls is one of the priority activities as part of thermal modernization, along with the insulation of the facades. This article discusses the research findings of detecting moisture decomposition within building walls utilizing electrical impedance tomography (EIT) and deep learning techniques. In particular, the focus was on algorithmic models whose task is transforming voltage measurements into spatial EIT images. Two homogeneous deep learning networks were used: CNN (Convolutional Neural Network) and LSTM (Long-Short Term Memory). In addition, a new heterogeneous (hybrid) network was built with LSTM and CNN layers. Based on the reference reconstructions’ simulation data, three separate neural network algorithmic models: CNN, LSTM, and the hybrid model (CNN+LSTM), were trained. Then, based on popular measures such as mean square error or correlation coefficient, the quality of the models was assessed with the reference images. The obtained research results showed that hybrid deep neural networks have great potential for solving the tomographic inverse problem. Furthermore, it has been proven that the proper joining of CNN and LSTM layers can improve the effect of EIT reconstructions.

Using electrical resistivity imaging technique for borehole calibration in selected communities in Asutifi North District, Ghana

This study has investigated the use of Electrical Resistivity (ER) technique for siting suitable groundwater points in selected communities within the Asutifi North District of Ghana where borehole drilling success rate has been low, leading to water shortages in the dry season. The study method employed involved 2D Electrical Resistivity Imaging (2D ERI) survey along existing boreholes in the area using the dipole-dipole array and correlating the outputs with existing 1D Vertical Electrical Sounding (1D VES) data and its corresponding borehole logs of the area to delineate the potential groundwater zones. The results from the 2D ERI survey showed the potential groundwater zones to be at depths of 14.7 to 58.9 m with resistivity values ranging from 2.08 to 156 Ωm while that for the 1D VES were at depths of 20 to 60 m with resistivities ranging from 67.10 to 545.28 Ωm. Also, the delineated potential groundwater zones from the 2D ERI showed a positive linear correlation with the observed aquifer zones from the borehole logs at a correlation coefficient (R2) of 0.72, whereas no correlation was observed when the observed aquifer zones from the borehole logs were compared to potential groundwater zones from the 1D VES data. Additionally, the 2D pseudo-sections from the 2D ERI survey aided in categorising the different potential groundwater zones in the area as good, moderate, fair and poor. Thus, the 2D ERI technique provided more detailed information on the subsurface in the area than the 1D VES approach and would be more reliable for subsequent groundwater exploration projects in the area and similar geological terrains.

Improving tailings dam risk management by 3D characterization from resistivity tomography technique: Case study in São Paulo – Brazil

Tailings dams are traditionally monitored by routine visual inspections combined with the use of instruments such as piezometers, topographical landmarks, and water level indicators. As they are structures that have high potential damage associated, the use of robust investigation methods is essential to ensure the operational safety of these dams. Geoelectrical methods have been successful in mapping humid zones inside the tailings dam structures, using the proper acquisition parameters and data sampling that these methods allow. This is a promising approach to investigating a tailings dam with a heterogeneous embankment made of limestone tailings, that has been in operation for 50 years in São Paulo state, Brazil. Considering its structural complexity, electrical resistivity sections were acquired with a tight profile spacing – of 10 to 15 m, processed, and modeled by applying a specific workflow. The Electrical Resistivity Tomography (ERT) operational workflow applied to the dataset obtained from two field campaigns, conducted in the embankment and the right abutment of the dam, performed well in mapping possible high water content zones inside the structure, defining a relevant element for the risk assessment. For this purpose, compartmentalization of the resistivity ranges, and three-dimensional modeling techniques were used, both constrained by the geotechnical instruments of the structure. The embankment campaign mapped zones of attention in the right abutment driving the decision to perform a follow-up survey dedicated to this region. The right abutment campaign mapped zones of high resistivity, associated with the natural terrain, composed of sandy soils, saprolite, and rock, as well as zones of low resistivity related to high moisture content regions. The geometry of these conductive zones made it possible to infer the depth of the water table in the region between the direct measurements from the instrumentation and to model zones with low resistivity values. The 3D modeling revealed that part of the conductive zones, present in the right abutment and extending to the embankment indicate a possible contribution of moisture from the natural terrain towards the embankment of the dam. Thus, the achieved results demonstrated that 3D modeling applied to the combination of the geotechnical and geophysical datasets contributes to expanding the knowledge of the internal structure of the dam and consequently the safety of the operations, especially where there is no instrumentation, improving the tailings dam risk management.

Landslide investigation using Electrical Resistivity Imaging (ERI) method at Kg. Chuchoh Puteri, Kuala Krai, Kelantan, Malaysia

The possible landslide in Kg. Chuchoh Puteri, Kuala Krai, Kelantan were investigated using the electrical resistivity imaging (ERI) technique. The survey for the collecting of data was conducted along six lines. Each survey line was 200 meters long, with 5 meters between electrodes. ABEM Terrameter LS 1 is used to record all the data and RES2DINV software is used to process it. While conducting the geophysical survey, the relationship between resistivity and conductivity is reciprocal. A pole-dipole array configuration was utilised in survey Lines 1, 2, 4 and 5 and in survey Lines 3 and 6, a Schlumberger array configuration. Survey Lines 2, 3, and 6 are primarily indicated as having a high probability of experiencing a landslide using the pseudosection 2-D profile. The findings reveal varying resistivity at a depth of study between 40 and 80 meters for a survey line length of 200 meters. In general, the resistivity survey's seven pseudosections showed two distinct types of soils: dry residual soil (1–1500 Ωm) and weathered volcanic rocks (>1500 Ωm). Residual soils with varying saturation levels, hard soil and weathered volcanic rock, have dominated the soil profile. These profiles can generally be divided into two (2) zones: thin/thick layers of loose to dense residual soils (10–100 Ωm; Zone A) and thin/thick layers of dense and hard material (> 1000 Ωm).

Imaging the distribution of bitumen contaminants in shallow coastal plain sands in southwestern Nigeria using electrical resistivity

Naturally occurring bitumen seeps migrating within the shallow subsurface contaminates soil and groundwater in communities within the eastern segment of the Dahomey Basin in southwestern Nigeria. Managing these contaminations require an understanding of the distribution of the bitumen seeps to isolate contaminated regions to avoid farming activities or drilling shallow drinking water wells within them. Several studies have assessed the shallow occurrence of bitumen in the region, focusing on their economic potentials but ignores the challenges with soil and groundwater contamination in these communities. This study focuses on using electrical resistivity imaging techniques with limited soil cores to delineate the distribution of bitumen seeps within the shallow subsurface in Imakun-Omi community in southwestern Nigeria. We used both 1D and 2D numerical and field approaches in this study. 13 vertical electrical soundings using a Schlumberger electrode array were first acquired to obtain the bulk resistivities and layer thicknesses. The 1D resistivity alongside well lithologic data served as prior data for a forward modelling study to assess the capability of resolving the bitumen seeps within electrically resistive coastal sands. Results of the forward modelling studies guided the acquisition of five 2D electrical resistivity profiles used to image the distribution of bitumen seeps using a Wenner electrode array with a unit electrode spacing of 2 m. Results of this study show that bitumen seeps with resistivities greater than 3000 Ohm-m are distributed within the top 4 m in the area and extends down to 10 m in some locations. Our numerical studies show that despite difficulties in imaging bitumen seeps within sediments overlain by higher electrical resistivity layers, reliable results can be derived, thus proving the versatility of electrical resistivity in delineating areas contaminated by bitumen seeps within shallow coastal sands.

3-D complex resistivity imaging using controlled source electromagnetic data: a multistage procedure using a second order polynomial parametrization

SUMMARY In some Earth materials, significant induced polarization (IP) phenomena are occurring when an electric perturbation is applied. These mechanisms are described by a frequency-dependent complex resistivity (CR). The study of the CR spectral signature allows to access indirectly to several properties of interest of the subsurface linked to the interaction between the pore space and fluids. CR is usually studied using the electrical method with a direct current approximation, neglecting by the way electromagnetic (EM) induction that can occur in the data. However, EM induction increases with frequency and offset, resulting in limitations at high frequencies or for the investigation of deep target. We implemented a frequency-dependent CR in a 3-D finite-differences (FD) modelling and inversion code for frequency domain controlled-source electromagnetic (CSEM) data to take into consideration IP information contained in EM data or reciprocally. The CSEM methods are resistivity imaging techniques using multifrequency EM fields that fully take into account EM induction with large investigation depth. Following a preliminary sensitivity study, a multistage inversion framework was designed to constrain the multiparameter inverse problem. Furthermore, to manage the increasing number of parameters, a second-order polynomial parametrization is used to describe independently frequency variation of CR norm and phase. We demonstrate the method through 1-D and 3-D synthetic data inversions for a deep-target model. We show that we were able to recover the CR and its frequency variation from CSEM data in the IP/EM coupling domain for 1-D targets. The problem of deep polarizable 3-D targets is more challenging and the resolution of the recovered CR spectrum was impacted. Nevertheless, we retrieved from a model containing several polarizable anomalies some crucial information allowing the discrimination of the targets from the non-polarizable background and from different spectral CR signatures. Our inversion strategy allows thus accessing to IP parameters of the medium in an extended frequency domain by fully taking EM induction information into account.

USE OF GEOPHYSICAL TECHNIQUES FOR ANALYSIS OF TRACE METALS IN A CEMETERY UNSATURATED SOIL ZONE

The purpose of this research was to apply two geophysical methods [Resistivity Imaging Technique (RI) and Inductive Electromagnetic Technique (EM-31)] to verify this tool’s efficiency in locating in subsurface contamination plumes and/or metal ions released by decomposition of corpses and their shells at Necrópole Nossa Senhora Aparecida, Piedade, SP.
 This research was carried out from October 2015 to September 2016. In the first phase, we used the Resistivity Imaging technique and later we applied the electromagnetic technique. This period between one investigation and another was useful to verify if anomalies changed during that period. Results showed that localized anomalies did not move and that results of both methodologies were similar, considering limitations of each technique.

Identifying soil-plant interactions in a mixed-age orange orchard using electrical resistivity imaging

Background and aimsThe ever-increasing demand of fresh water from irrigated agriculture and the water scarcity condition that characterizes the Mediterranean region, encourages the adoption of efficient water saving strategies. In this study, the electrical resistivity imaging (ERI) technique was applied for characterizing the mass exchange mechanisms affecting the continuous soil-plant system of heterogeneous micro-irrigated orchards.MethodsTime-lapse ERI surveys were carried out in a multi-variety and mixed-age orange orchard located in Eastern Sicily (insular Italy). The time-lapse ERI monitoring was coupled with the use of independent soil water content (SWC) measurements.Results and conclusionsA clear relationship between the soil electrical resistivity (ER) and the measured SWC changes was identified in the soil under the mixed-age orchard (with an overall coefficient of determination value of 0.63). Specifically, this study highlights the dependency of SWC dynamics as function of tree age. Overall, an increase in ER was obtained in correspondence to the soil domain where the younger trees were located ( < = 3 years). This behaviour was inferred to result from the higher soil evaporation process active at these locations, due to less vegetation cover of the younger trees and, consequently to their lesser root extension in comparison to the mature trees ( > = 8 years). On the other hand, in mature trees, characterized by denser root biomass, there were more evident decreasing patterns of ER (i.e., greater changes in SWC), due to greater transpiration rates that caused lower initial SWC under these conditions in comparison to the younger trees.

Optimal Implementation Parameters of a Nonlinear Electrical Impedance Tomography Method Using the Complete Electrode Model

This study discusses a nonlinear electrical impedance tomography (EIT) technique under different analysis conditions to propose its optimal implementation parameters. The forward problem for calculating electric potential is defined by the complete electrode model. The inverse problem for reconstructing the target electrical conductivity profile is presented based on a partial-differential-equation-constrained optimization approach. The electrical conductivity profile is iteratively updated by solving the Karush–Kuhn–Tucker optimality conditions and using the conjugate gradient method with an inexact line search. Various analysis conditions such as regularization scheme, number of electrodes, current input patterns, and electrode arrangement were set differently, and the corresponding results were compared. It was found from this study that the proposed EIT method yielded appropriate inversion results with various parameter settings, and the optimal implementation parameters of the EIT method are presented. This study is expected to expand the utility and applicability of EIT for the non-destructive evaluation of structures.