Electrical Resistivity Tomography Data Research Articles

Spatial Analysis of Leachate Penetration at Lemna Dumpsite, Calabar: Implications for Sustainable Waste Management in Cross River State

This study rigorously investigated the spatial analysis of leachate penetration at Lemna dumpsite, located in Calabar, Cross River State, Nigeria. Purposeful soil sampling, performed at specific intervals (5 meters, 25 meters, and 50 meters) along the Electrical Resistivity profile line within the dumpsite, was augmented by water sample collection from five boreholes near Lemna dumpsite. Utilizing Electrical Resistivity Tomography (ERT) and Vertical Electric Sounding (VES) survey techniques, resistivity data were systematically gathered to comprehensively analyze the Leachate Penetration in the Lemna dumpsite. Laboratory analysis of soil and borehole water quality focused on Benzene, Toluene, Ethylbenzene, and Xylene (BTEX), with paired sample t-tests applied for statistical scrutiny. Analyzing the ERT and VES data employed sophisticated techniques embedded in Resistivity Two Dimension Invasion software and Advanced Geosciences Incorporation Earth Imager software. Substantial disparities (p<0.05) emerged in the paired sample t-tests for BTEX in soil compared to National Environmental Standard Regulation and Enforcement Agency (NESREA) limits. Similarly, BTEX in borehole water displayed significant differences (p<0.05) when compared to World Health Organization (WHO) standards, raising alarming concerns about the safety and portability of groundwater in the area. The examination of dumpsite leachate penetration revealed a resistivity anomaly of 8.01Ωm and an inverse depth of 12.4 m, underscoring profound environmental implications and necessitating immediate remediation efforts. Additionally, Vulnerability and Aquifer Protective Capacity Index (VES) results, with a rating of <0.1, indicated severely compromised aquifer protective capacity, emphasizing the vulnerability of groundwater resources to further contamination. Our study advocates for strategic management, remediation, and monitoring measures to prevent contamination and safeguard water quality in the region.

Salinity distribution in agricultural land by geophysical, hydrochemical and geostatistical approaches: a pilot area located in Qelabshowah–Belqas, East Nile Delta region, Egypt

The study area is situated in the Qelabshowah–Belqas region, known for its Quaternary deposits. This research aims to demonstrate the two-dimensional (2D) variation of subsurface layers and salinity distribution using geoelectrical data, hydrochemical analysis, and geostatistical analysis. DC resistivity measurements were taken at fifteen vertical electrical sounding (VES) survey points using a Schlumberger array (AB/2 = 100 m) along three profiles. In addition, an electrical resistivity tomography (ERT) survey was conducted with a dipole–dipole array across one profile. Seven surface water samples were collected in the area. From the 1D and 2D inversion of VES and ERT data sets, three-to-four geoelectric layers were identified, including unconsolidated surface deposits, saturated clayey sand, saturated sand, and a salt-rich layer. The 2D inversion of VES data revealed an ancient salt-rich layer deposited in swampy conditions over a conductive wet sand layer along profile one due to salt mineral infiltration and dissolution. The 2D inversion of ERT data showed accurate lateral geometric accuracy compared to the 2D inversion of VES data, highlighting geological features, such as caves in the second layer and a buried water canal on the ground surface. Surface water samples showed high salinity levels with sodium hazards, indicating an Na–Cl composition. Geoelectric and hydrochemical data sets were geostatistically analyzed using spherical variogram supported ordinary Kriging interpolation. The analysis indicated weak to moderate spatial dependency for true resistivity parameters, while sodium content (SC) and permeability index (PI) showed strong spatial correlation. The 2D spatial distribution resistivity maps based on the 1D inversion of VES data displayed a general decrease in resistivity with depth, likely due to clay minerals or moist soil in the second layer and saline irrigation water infiltration in the third layer. The 2D spatial distribution of SC and PI showed a high concentration zone, posing a potential risk to agricultural crops regardless of soil permeability. It is recommended to use these maps when cultivating plants that can tolerate high sodium levels during the reclamation process.

Assess the Effectiveness of Multichannel Analysis of Surface Waves Method in Mapping Ancient Structures in Ultrashallow Aquatic Environments: The Case of Agioi Theodoroi, Greece

ABSTRACTThe present geophysical research aims to evaluate the applicability of multichannel analysis of surface waves (MASW) on mapping ultrashallow underwater ancient masonry remnants. The work presents the analysis from a single seismic line using MASW and seismic refraction tomography (SRT) methods and its corresponding electrical resistivity tomography (ERT) section surveyed at the submerged prehistoric site of Agioi Theodoroi area located 10 km eastern of Heraklion, Crete, Greece. The 2D MASW velocity model exhibits significant correspondence with the resistivity structure extracted from the ERT data, showing lateral S‐wave velocity (Vs) variations at the positions where the high resistivity anomalies exist. The analysis of synthetic seismic data calculated from a respective model reproduced a comparable S‐wave velocity pseudo‐section with the real data. However, the investigated targets (submerged buried masonry) appear shallower and wider in MASW sections than in the real world and the corresponding synthetic models, due to insufficient vertical and horizontal resolution of this method. Surface waves travelling through the seafloor sediments (Scholte‐waves) demonstrate very low velocity values. This makes them suitable for the detection of shallow and relatively large (&gt; 0.5 m) underwater manmade structures, providing the enhancement of MASW method resolution, by utilizing a high frequency (&gt; 100 Hz) seismic source, recording short Scholte wavelengths (≤ 1 m) and using shorter (≤ 0.5 m) receiver spacing and array length. Consequently, the results of this work demonstrate the potential in employing conventional seismic techniques in the delineation of underwater antiquities and the revealing of the cultural dynamics in very shallow off‐shore archaeological sites.

Improving efficiency and accuracy of levee hazard detection with deep learning

This paper presents a model framework based on a deep learning algorithm using Faster R-CNN to improve the accuracy and efficiency of detecting long-distance levee hazards. The proposed method aims to assist technicians in identifying hazards in resistivity inversion profiles, thereby reducing the manual workload and extracting valuable information from a large volume of data using deep learning techniques. To this end, we constructed a dataset of levee hazards using inversion images of electrical resistivity imaging data obtained from numerical simulations, indoor experiments, and actual levee hazards. Subsequently, we utilized image augmentation techniques to expand the dataset and enhance the model's performance, resulting in a total of 2000 labeled hazard images. The network was then trained from scratch by setting the hyperparameters. the trained weight parameters and network architecture were saved for future use. The model's performance was evaluated using standard evaluation metrics, including precision, recall, F1 score, and average precision (AP). The proposed model demonstrated robustness and adaptability, achieving high precision and accuracy with an AP value of up to 100%. Furthermore, we validated the method using three cases, and the results indicated its superiority over traditional manual identification-based or outlier analysis detection of levee hazards in practical scenarios. This study emphasizes the significant advantages of deep learning techniques over the traditional methods, including improved accuracy, automation, speed, and scalability. It suggests that intelligent detection based on deep learning can accurately identify and locate hazards in levees.

Characterizing Landfill Extent, Composition, and Biogeochemical Activity using Electrical Resistivity Tomography and Induced Polarization under Varying Geomembrane Coverage

Landfill monitoring is essential for sustainable waste management and environmental protection. Geophysical methods can provide quasi-continuous spatial and temporal insights into subsurface physical properties and processes in a non-intrusive manner. The effectiveness of monitoring landfill extent, composition, and degradation under varying geomembrane coverage was evaluated using electrical resistivity tomography (ERT) and induced polarization (IP) methods. Synthetic electrical models for landfill with different geomembrane damage degrees were inverted to assess data reliability. The current conduction channels into the geomembrane during the electrical survey were quantified. Reliable electrical data was obtained when the inverted conduction channel ratio of the geomembrane (representing damage to the geomembrane) was 51.6% or higher. This criterion was validated in a landfill experiencing aeration and anaerobic treatments. ERT and IP data captured construction and domestic waste distribution and identified the landfill boundary. The chargeability of domestic waste proved sensitive to microbial degradation activity, corroborated by characteristic ammonium and nitrate ions and a linear relation between chargeability and subsurface temperature. Temperature variations between the aerobic and anaerobic reaction zones (&gt;20°C and = 12°C) were observed to correlate with high chargeability values (&gt;80.4 mV/V), signifying the presence of biogeochemically active zones. IP excels in characterizing geomembrane-covered landfill boundaries and discerning biogeochemical activity, thereby enhancing landfill monitoring and waste management strategies.

Geophysical characterization of groundwater aquifers in the Western Debrecen area, Hungary: insights from gravity, magnetotelluric, and electrical resistivity tomography

The recent study followed a multi-methodological approach integrating gravity, magnetotelluric (MT), and electrical resistivity tomography (ERT) to investigate the geometry and hydrological characteristics of the main hydrostratigraphical units in the Western Debrecen area, Eastern Hungary. The integration of these methods aims to delineate potential zones for groundwater development and guide effective extraction strategies. In the gravity investigation, the Bouguer anomaly map undergoes spectral analysis for the separation of shallow and deep features, offering a preliminary indication of basement rock depth. Subsequently, gravity data inversion is employed to map variations in basement rock topography, revealing a basin structure, with sediment thicknesses extending up to 2 km. On the other hand, the MT data are modeled using the 1D Occam inversion algorithm to validate the results of the gravity data analysis. This inversion, constrained with lithological logs is further utilized to delineate the main hydrostratigraphical units in the study area. Accordingly, four units are identified, including the Nagyalföld Aquifer, Algyő and Endrődi Aquitards, the Badenian Aquifer, and the Pre-Neogene Aquitard. Consequently, Dar Zarrouk parameters based transmissivity and the hydraulic conductivity of the aquifer units are measured. The Nagyalföld aquifer showed a hydraulic conductivity that ranged between 7.9 and 11.9 m/day, while the Badenian aquifer showed an average hydraulic conductivity of 13.1 m/day. The ERT data are employed to map the spatial distribution of the depth to the water table. The shallow water table is observed in regions characterized by an elevated thickness of sedimentary rocks, attributed to their high specific capacity. Integrating these hydrogeophysical methods provided a comprehensive understanding of the subsurface hydrology and enabled better-informed decision-making for groundwater development.

Integrated Geotechnical and Electrical Resistivity Tomography to Map the Lithological Variability Involved and Breaking Surface Evolution in Landslide Context: A Case Study of the Targa Ouzemour (Béjaia)

The specific lithology of the southern part of Bejaia city represents a major limitation to urban settlement and expansion. This is partly due to landslides that tend to affect this region. To date, one of these landslides in this region has occurred in the Targa Ouzemour area, where the damage extended approximately six hectares. The main purpose of this study is to identify the failure surfaces characterizing the internal structure of this landslide as well as the significant influence of groundwater on slope instability, which manifests as surface cracking and subsidence. We have combined several geotechnical and geophysical methods, including field observations. The exploitation of the collected geotechnical data from the six (06) boreholes drilled in the landslide zone has allowed for knowledge to be gained on the lithological components, as well as the characterizations of physical and mechanical properties on a range of different types of affected rocks, whereas electrical resistivity tomography (ERT) data allowed an in-depth examination, leading us to reconstruct the landslide geometry and particularly to evaluate the hydrological characteristics of the studied site. Moreover, the resistivity contrast patterns provided more clarity to discern between the various lithological formations that are still stable or actively moving within this landslide. All these findings have contributed to the construction of a characteristic geomodel that highlights the failure surfaces over which displacement is still experienced. Finally, with the evidence of rainfall effects on the deformation and stability of the slope, specific landslide remedial measures were accordingly suggested.

Near-surface imaging by joint inversion of ERT and seismic traveltime data with guided FCM clustering

We present a novel joint inversion strategy for electrical resistivity tomography (ERT) and seismic first-arrival traveltime data with guided fuzzy c-means (GFCM) clustering coupling to increase the accuracy of inversion results and improve the characterization of heterogeneous near-surface materials. The physical parameter correlation between the resistivity and seismic velocity is enforced using a GFCM clustering coupling term in the objective function, which also includes misfit and regularization terms. The physical property models estimated by joint inversion are guided by the clustering centers of a priori petrophysical parameters. A limited-memory quasi-Newton approach is used to optimize the objective function. To validate the proposed approach and describe its implementation in detail, tests were first performed using two synthetic models. The stratigraphic structures recovered by joint inversion with GFCM clustering coupling are more consistent with the true model than those inverted by individual inversion, cross-gradient and fuzzy c-means (FCM) joint inversion. Furthermore, the physical parameters estimated using GFCM joint inversion are significantly more accurate than those estimated using individual inversion and the other two joint inversion methods. The joint inversion algorithm was applied to field data from the Liangzhu site in Hangzhou, China. The depth and shape of the stratigraphic interface imaged by GFCM joint inversion are consistent with the drilling results. However, when using individual inversion and the other two joint inversion methods, the recovered stratigraphic structures have some differences with the drilling data. Therefore, GFCM joint inversion is an effective and reliable method for near-surface fine imaging.

Quantifying salinity in heterogeneous coastal aquifers through ERT and IP: Insights from laboratory and field investigations

The lithological and stratigraphical heterogeneity of coastal aquifers has a great influence on saltwater intrusion (SI). This makes it difficult to predict SI pathways and their persistence in time. In this context, electrical resistivity tomography (ERT) and induced polarization (IP) methods are receiving increasing attention regarding the discrimination between saltwater-bearing and clayey sediments. To simplify the interpretation of ERT data, it is commonly assumed that the bulk conductivity mostly depends on the conductivity of pore-filling fluids, while surface conductivity is generally disregarded in the spatial and temporal variability of the aquifers, particularly, once the aquifer is affected by the presence of saltwater. Quantifying salinities based on a simplified petrophysical relationship can lead to misinterpretation in aquifers constituted by clay-rich sediments. In this study, we rely on co-located data from drilled boreholes to formulate petrophysical relationships between bulk and fluid conductivity for clay-bearing and clay-free sediments. First, the sedimentary samples from the drilled wells were classified according to their particle size distribution and analyzed in the lab using spectral IP in controlled salinity conditions to derive their formation factors, surface conductivity, and normalized chargeability. Second, the deduced thresholds are applied on the field to distinguish clay-bearing sediments from brackish sandy sediments. The results are validated with logging data and direct salinity measurements on water samples. We applied the approach along the Luy River catchment and found that the formation factors and surface conductivity of the different unconsolidated sedimentary classifications vary from 4.0 to 8.9 for coarse-grained sand and clay-bearing mixtures, while normalized chargeability above 1.0 mS.m−1 indicates the presence of clay. The clay-bearing sediments are mostly distributed in discontinuous small lenses. The assumption of homogenous geological media is therefore leading to overestimating SI in the heterogeneous clay-bearing aquifers.

3D electrical resistivity survey for reduction of groundwater drilling uncertainties in a clay-rich environment

Drilling for groundwater is expensive and challenging. It is even more challenging to find a location that will result in a high-yield well in heterogeneous environments. To tackle the heterogeneity issue, geophysical surveys can help in mapping the subsurface structure and delineating the drilling trajectory. The current study displays the effectiveness of 3D electrical resistivity tomography (ERT) to locate a permeable groundwater zone within a highly heterogeneous and clayey subsurface. Ground truthing the acquired geophysical data with in-situ sampling helps ensure accuracy in classifying groundwater zones in the final inverted 3D data set while also delineating boundaries between permeable groundwater zones and less permeable clayey structures. In-situ samples of groundwater and soil were used to measure the saturated region's resistivity in the laboratory using a column setup. Clay zones in the data set are classified from the nearby well data at similar depth ranges and from very low resistivity values from ERT data and laboratory measurements. The results display highly differentiating resistivity zones that are attributed to the scattered clay lenses (low resistivity) in conjunction with the freshwater zone (high resistivity). The distinction between clayey and nonclayey bodies is important to better inform drilling locations for optimal groundwater yield. This study concludes that with the aid of low-cost geophysical surveys and minimal in-situ sampling data correlations, permeable groundwater boundaries and clay lens volumes can be identified easily.

ERT data assimilation to characterize aquifer hydraulic conductivity heterogeneity through a heat‐tracing experiment

AbstractGeothermal energy systems, such as heat pumps relying on aquifers, use renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, a heat‐tracing experiment is a typical field test that can help with the characterization of the subsurface. During a heat‐tracing experiment, monitoring with downhole temperature sensors, water‐level pressure transducers and electrical resistivity tomography (ERT) can be used to help characterize the hydrogeological properties. Previous monitoring tools have shortcomings, such as low‐resolution data and over‐smoothing; thus, they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over‐smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time‐lapse cross‐borehole ERT data into a numerical model of groundwater flow and heat transfer, where the groundwater is extracted and heated water is reinjected into an unconfined sandy‐gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modelling (ResIPy) to estimate cross‐borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to an experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduced as the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross‐borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move towards K values inferred with slug tests.

Interpretation of large‐scale, long‐term electrical geophysical monitoring guided by a process simulation

AbstractSurface electrical resistivity tomography (ERT) was used at a waste site to monitor vadose zone changes in electrical properties as a proxy for contaminant flux over a span of 17 years. The BC Cribs and Trenches (BCCT) site at the Hanford site contains 20 disposal trenches and six disposal cribs. Wastes include a large inventory of technetium‐99 and large masses of nitrate and uranium‐238. ERT data were collected along 41 profiles in 2005 to characterize regions of elevated bulk electrical conductivity (BEC) associated with past liquid waste discharges. Previous analyses performed on samples from four boreholes showed a high correlation between nitrate concentration and BEC. In 2022, ERT data were re‐collected along the same profiles and six additional profiles in an area not previously surveyed. Compared to background uncontaminated areas, BEC was higher in contaminated areas at the waste sites. Given the correlation between nitrate concentration and BEC previously found at this site, ERT images show the spatial distribution and relative ionic concentration of vadose zone contaminants at BCCT. Between 2005 and 2022, ERT difference images showed a decrease in BEC surrounding most waste sites, with exceptions where there were known anthropogenic surface changes. An evaluation of recharge‐driven nitrate migration using synthetic flow and transport simulations showed that downward migration causes a decrease in BEC from the decrease in ionic strength at the trailing end of the plume where contaminants migrated downward. From this, we interpret ERT difference images as showing the predominant regions of downward ion flux.

Electrical Resistivity Tomography Investigation of Permafrost Conditions in a Thermokarst Site in Fairbanks, Alaska

&lt;abstract&gt; &lt;p&gt;The degradation of permafrost poses severe environmental threats to communities in cold regions. As near-surface permafrost warms, extensive topographic variability is prevalent in the Arctic and Sub-Arctic communities. Geologic hazards such as thermokarst are formed due to varying rates of permafrost degradation, resulting in ground subsidence. This gradual subsidence or abrupt collapse of the earth causes a danger to existing infrastructure and the economic activities of communities in cold regions. Understanding the causes of thermokarst development and its dynamics requires imaging its underground morpho-structures and characterizing the surface and subsurface controls. In this study, we conducted a two-dimensional (2D) electrical resistivity tomography (ERT) survey to characterize the permafrost conditions in a thermokarst prone site located in Fairbanks, Alaska. To increase the reliability in the interpretability of the ERT data, borehole data and the depth-of-investigation (DOI) methods were applied. By using the 2D and three-dimensional (3D) ERT methods, we gained valuable information on the spatial variability of transient processes, such as the movement of freezing and thawing fronts. Resistivity imaging across the site exhibited distinct variations in permafrost conditions, with both low and high resistive anomalies observed along the transects. These anomalies, representing taliks and ice wedges, were characterized by resistivity values ranging from 50 Ωm and above 700 Ωm, respectively. The results from this study showed the effectiveness of ERT to characterize permafrost conditions and thermokarst subsurface morpho-structures. The insights gained from this research contribute to a better understanding of the causes and dynamics of thermokarst, which can be instrumental for engineers in developing feasible remedial measures.&lt;/p&gt; &lt;/abstract&gt;

Geophysical, hydrogeological and laboratory assessment of groundwater quality and its health implication in some urban communities in cross river state

The electrical resistivity technique which involved the Schlumberger depth sounding method, electrical resistivity tomography and geochemical analyses of water samples collected from boreholes were used to investigate the quality of groundwater in some urban communities in Cross River State, also the health implication of the said groundwater was also investigated. Forty nine electrical resistivity data were collected, modeled, and interpreted after calibration with lithologic logs. Ten electrical resistivity tomography data was were also collected, modeled and interpreted. Forty nine borehole water samples were collected and analyzed to determine anion, cation concentrations and some physical and chemical parameters, such as water colour, temperature, total dissolved solids, and electrical conductivity. The results show that the lithostratigraphy of the study area is composed of sands, sandstones (fractured, consolidated and loosed), siltstones, shales (compacted and fractured) of the Asu River Group, Eze-Aku Formation which comprises the aquifer units, and the Nkporo Shale Formation. The aquifer conduits are known to be rich in silicate minerals, and the groundwater samples in some locations show a significant amount of Ca2+, Mg2+, and Na+. These cations balanced the consumption of H+ during the hydrolytic alteration of silicate minerals. The geochemical analysis of groundwater samples revealed dominant calcium–magnesium–carbonate– bicarbonate water faces. The groundwater quality was observed to be influenced by the interaction of some geologic processes but was classified to be good to excellent, indicating its suitability for domestic and irrigation purposes.

Delineation of LNAPL plumes in a clay-rich site in Gyeongsangnam-do Province, South Korea: integration of geophysical survey data with borehole data and soil sampling information.

To effectively delineate the spatial distribution of oil contaminant plumes, geophysical methods indirectly measure the physical properties of the subsurface and can provide spatial information and images on a large scale, as opposed to traditional direct methods such as borehole drilling, sampling, and chemical analysis, which are time-consuming and costly. However, interpreting geophysical responses over non-aqueous phase liquid (NAPL)-contaminated sites is not straightforward due to inconsistent responses from biodegraded oil contaminants. In this study, we performed multi-geophysical surveys including seismic refraction, ground-penetrating radar, electrical resistivity tomography (ERT), and induced polarization (IP) surveys, to locate NAPL-contaminated zones in a clay-rich site. To reduce ambiguity in discriminating between oil contaminants and clay layers, we first figure out the geological structure of the site by interpreting geophysical data incorporating with borehole data. The ERT data highlighted the heavily contaminated regions in the unsaturated zone but were less distinctive below groundwater levels. Conversely, IP responses revealed potential hotspots within the clay layers, extending beneath the groundwater. Considering the 3D geological model, NAPL-contaminated zones are properly delineated through interpretation of ERT and IP data together with borehole data, and the contaminant source zone was properly estimated within the site.

A deep-learning framework for borehole formation properties prediction using heterogeneous well-logging data: A case study of a carbonate reservoir in the Gaoshiti-Moxi area, Sichuan Basin, China

The properties of borehole formations, such as porosity, permeability, and water saturation, play a crucial role in characterizing and evaluating subsurface reservoirs. Although core sample experiments offer precise measurements, they are time consuming and cost intensive. An alternative method is to use the logging data to construct an empirical model that predicts formation properties, which is widely studied due to its speed and affordability. Nevertheless, because the response of a logging point reflects its surrounding formation, conventional logging methods relying on point-to-point (P2P) mapping perform poorly in complex reservoirs. Furthermore, the resolution of conventional logging is lower compared with imaging logging. To address these limitations, this study presents a novel approach to predict formation properties based on a deep-learning framework using heterogeneous well-logging data. Our neural network framework takes short sequences of conventional logging data and windowed imaging logging data as inputs. The neural network applies 1D convolution to extract features from the conventional logging sequences and 2D convolution to extract features from the resistivity imaging data. Then, these two feature vectors are fused and fed into a multilayer fully connected neural network to predict formation properties. A case study of a carbonate reservoir demonstrates that our method delivers more accurate predictions of formation porosity, permeability, and water saturation than the P2P, sequence-to-point, and image-to-point prediction methods. Moreover, it is expected that our paradigm will serve as a source of inspiration for forthcoming research endeavors aimed at enhancing the accuracy of predicting borehole formation properties in complex reservoirs.

ERT investigation of mud volcanoes: detection of mud fluid migration pathways from 2D and 3D synthetic modelling

Mud volcanoes are geological structures observed throughout the world that arise from the upwelling of deep fluids along discontinuities in the subsoil. The detection of mud fluid migration pathways can be challenging, even when using Electrical Resistivity Tomography (ERT) as detectability issues may arise from complex geological settings. This paper presents new results from 2D and 3D ERT synthetic modelling for the investigation of the shallow, internal structure of terrestrial mud volcanoes. This study revealed the internal structure of the ‘Cenerone-Pineto’ mud volcano (Central Italy) and provided further clues as to its internal structure. The main results of the study are: the presence of a mud chamber, which represents the last phase of mud accumulation before final emission, not located beneath the crater but laterally offset, as well as the presence of a narrow, shallow feeder channel; these findings represent evidence of a much more complex structure than one would expect. This means that the mud volcano is not supplied with mud fluids directly from below as would be the case with an uprising of deep fluid along a near-vertical open fracture and that the shallow mud fluid reservoir is not correlated to the distribution of any mud volcano observed on the surface. Findings from this study are consistent with the observed structural features already noted in ERT and seismic field data collected at the ‘Cenerone-Pineto’ mud volcano and may be helpful in explaining the mechanisms and processes involved in mud volcanism in similar geological settings.

Improving evapotranspiration computation with electrical resistivity tomography in a maize field

AbstractHydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model‐based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot‐scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO‐56 model), and (4) evaluating the performance of ERT‐based ET computation. Our results showed that ERT was able to capture decimeter‐scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point‐based sensing, which can further our understanding of the hydraulic dynamics in the soil‐plant‐atmosphere continuum.

Numerical modelling of two-phase flow hydrodynamics of column flotation - validation against ERT data

ABSTRACT In mineral processing, column flotation is being used extensively for fine particle beneficiation. Although a few attempts were made to develop computational fluid dynamics (CFD) models for column flotation in the past, validation against comprehensive experimental data is yet to be demonstrated. This work employs an Eulerian-Eulerian model coupled with the standard k-ε turbulence model for modeling the hydrodynamics in the column flotation. The two-phase simulations were performed on both laboratory and industrial-scale column flotations. The two-fluid model was further modified with the appropriate interphase forces (Ishii-Zuber drag force and Tomiyama lift force) and population balance method (PBM) to accurately simulate and predict the bubble dynamics. The axial and radial gas holdup variations along with the axial liquid velocity are predicted at different air and liquid superficial velocities. The numerical predictions were then validated against the Electrical Resistance Tomography (ERT) data in a 4-inch laboratory column flotation and conductivity probe data in an industrial column flotation. The modified two-fluid model was able to predict accurate hydrodynamics close to ERT data. The gas holdup was found to increase with the superficial air velocity, liquid height, and liquid velocity. The gas holdup found uniformly dispersed radially at low superficial air velocities and heterogenous dispersed bubble flow at higher superficial velocities. A parabolic profile of liquid flow observed at higher superficial air velocities.

An Approach to Define and Quantify Porosity System in Heterogeneous Carbonate Reservoirs through Textural Analysis using High-Resolution Resistivity Image in the Bassein Formation of Mumbai Offshore Basin, India

Summary In evaluating the porosity system of the Bassein formation, high-resolution resistivity image (FMI) data have been used. Major features observed from the FMI data are the presence of low-angle fractures and solution channels of varying length and thickness. Against the layers with very-low-density neutron porosity, solution channels are seen as conductive features. These mud-filled solution channels can actively participate in production. Being very small-scale features, these types of porosities are not identifiable from conventional log data. To define and quantify porosity types such as vugs, solution channel, and matrix porosity, the resistivity image data are processed and image-based porosity is calculated. A core thin-section study is carried out and is used for calibration of the image-derived porosity system. Using resistivity image data, we have computed porosity types, for example, vugs, connected matrix and resistive, and their contribution to total porosity. Identification of hydrocarbons in reservoir layers with their porosity contribution (porosity partitioning) gives better insight into hydrocarbon production in these low-porosity layers which are producing after acid job.