Subsurface Conductivity Research Articles

Electrical resistivity tomography inversion combining deep variational autoencoder and stochastic adaptive sampling

Geophysical methods, such as electrical resistivity tomography (ERT), can be used to imaging the near-surface electrical resistivity as field measurements depend on the subsurface porosity, water saturation and fluid salinity. ERT has been widely applied to investigate mineral and groundwater resources, and in archaeological, environmental, and engineering studies. The prediction of subsurface electrical conductivity from ERT data requires solving a geophysical inverse problem. For near-surface characterization studies, this is often accomplished with deterministic inverse methods. These methods linearize the problem around an initial solution, and their smoothness depends on an imposed a priori spatial regularization term. Depending on this parameterization, these methodologies might struggle to capture the natural variability of the subsurface. Moreover, deterministic solutions have limited capabilities for uncertainty assessment. On the contrary, stochastic inverse methods can assess uncertainties by predicting multiple model realizations that fit similarly the recorded ERT data. However, they are often more computationally expensive than deterministic solutions. Deep learning algorithms based on deep generative models have been used to re-parametrize model and data spaces into low-dimensional domains and efficiently solve geophysical inverse problems. However, within this context, uncertainty assessment is challenging. We propose a deep convolutional variational autoencoder (VAE) coupled with stochastic adaptive optimization to perform stochastic ERT inversion. Geostatistical simulations of electrical resistivity are used as training data set of the VAE. After training, the VAE generates electrical resistivity models that reproduce the statistics and spatial continuity patterns of the training data set. Then, the VAE latent space is iteratively perturbed and updated with adaptive stochastic sampling based on the misfit between observed and predicted ERT data. The proposed methodology is illustrated in two-dimensional synthetic and real data sets to illustrate the ability of the proposed method to predict reliable electrical resistivity models while generating multiple possible scenarios for uncertainty assessment.

Probabilistic geophysical inversion of complex resistivity measurements using the Hamiltonian Monte Carlo method

SUMMARY In this work, we introduce the probabilistic inversion of tomographic complex resistivity (CR) measurements using the Hamiltonian Monte Carlo (HMC) method. The posterior model distribution on which our approach operates accounts for the underlying complex-valued nature of the CR imaging problem accurately by including the individual errors of the measured impedance magnitude and phase, allowing for the application of independent regularization on the inferred subsurface conductivity magnitude and phase, and incorporating the effects of cross-sensitivities. As the tomographic CR inverse problem is nonlinear, of high dimension and features strong correlations between model parameters, efficiently sampling from the posterior model distribution is challenging. To meet this challenge we use HMC, a Markov-chain Monte Carlo method that incorporates gradient information to achieve efficient model updates. To maximize the benefit of a given number of forward calculations, we use the No-U-Turn sampler (NUTS) as a variant of HMC. We demonstrate the probabilistic inversion approach on a synthetic CR tomography measurement. The NUTS succeeds in creating a sample of the posterior model distribution that provides us with the ability to analyse correlations between model parameters and to calculate statistical estimators of interest, such as the mean model and the covariance matrix. Our results provide a strong basis for the characterization of the posterior model distribution and uncertainty quantification in the context of the tomographic CR inverse problem.

Hydrological classification of mine pit lakes using modelling experiments

With the growing global prevalence of open-pit mining activities, there is an increasing necessity for sustainable mine life cycle plans with an early outlook towards mine closure. A major consideration in mine closure planning is the potential formation of lakes in the mine void and how these “pit lakes” can be managed to minimise risks and, if possible, create benefits. Understanding the long-term interactions between pit lakes, groundwater, and surface water systems is essential for that purpose. While numerous site-specific studies have been undertaken, there have been no studies that aim to provide a general, broadly applicable understanding of how pit lake hydrology relates to geographical context, and no efforts to hydrologically classify pit lakes using geographical criteria. This research employs an integrated generic pit lake water balance model to examine mine pit lake interactions with the surrounding surface water and groundwater. Simulating 243 scenarios, the influence of five input factors (climate, hydraulic conductivity, regional groundwater level, catchment area and pit slope) on pit lake behaviour up to 6000 years beyond the closure of the mine was considered. The assessment focused on four pit lake hydrological attributes once the system reached equilibrium: water level, time to equilibrium, the fraction of days where the pit recharges groundwater, and the fraction of days where there is surface overflow from the lake. All scenarios were assigned to one of five hydrological classes based on the interactions between the pit lake and the surrounding surface water and groundwater. Our findings show that, in many contexts, general data on climate type and subsurface hydraulic conductivity can yield reliable predictions of a pit lake's long-term hydrological classification without having to develop a detailed, site-specific pit lake model. The classification needs improvements in non-arid climates where inter-annual variation in rainfall is pronounced. The pit lake classification is particularly valuable for first-pass risk assessments to determine whether site-specific modelling is required.

2D Transdimensional Joint Inversion of Radio Magnetotelluric and Electrical Resistivity Tomography Data

Summary The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

Drone‐towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

AbstractDrone‐based geophysical surveying is an emerging measuring platform. High time‐cost efficiency and flexibility to survey over inaccessible areas make drones attractive or sometimes the only feasible option to carry geophysical measurements. This study presents a new drone‐towed electromagnetic induction and magnetic gradient sensor system used for near‐surface characterization and areal mapping.The system uses various datasets to enhance processing and interpretation. The system includes; an electromagnetic induction instrument; magnetic sensors; GNSS‐IMU system; photogrammetry; Lidar model data; and geoid model data. Robust data processing and stochastic inversion subsurface characterization for archaeological prospecting with drone‐towed electromagnetic induction and magnetic gradient sensor systems. Robust statistical methods were used to process the data.We conducted the fieldwork at one of the ancient Viking settlements in Denmark. The surveyed area was approximately 100200 m. We then implemented and applied a one‐dimensional laterally constrained non‐linear stochastic inversion to image the subsurface electrical conductivity. The inversion results show a consistent conductive layer at 5–8 m depths, likely associated with the groundwater level. This conductive layer is disrupted under a prominent anomaly within a 2–4 m wide area. Our analysis showed that this conductivity disruption could be a flint mine extending 7 m deep. This anomaly also has a strong signature in magnetic gradient data.

3-D parallel anisotropic inversion of controlled-source electromagnetic data using nested tetrahedral grids

SUMMARY Geophysicists today face the challenge of quickly and reliably interpreting extensive controlled-source electromagnetic (CSEM) data sets to map subsurface conductivity structures within realistic geological environments. An ideal 3-D CSEM inversion algorithm using tetrahedral grids should be capable of distinguishing different resolution requirements between forward modelling and inversion grids, have an optimal parallel strategy that fully exploits the inherent independence of CSEM data sets while also possessing the capability to handle large-scale geo-electrical models, and incorporate conductivity anisotropy which should be a common characteristic in realistic subsurface environments. However, existing tools in the geo-electromagnetic community often fall short of these three demands. Addressing this gap, our study introduces a scalable and parallel anisotropic inversion technique for CSEM data, capitalizing on the potential of unstructured tetrahedral grids. We first apply the tetrahedral longest-edge bisection method to create a refined dense, heterogeneous forward modelling grid from a coarse inversion grid. This refinement, focused on areas around transmitters and receivers, is seamlessly integrated within the coarser inversion grid’s topology, enabling precise conductivity mapping and preserving electromagnetic response accuracy during model updates. We further innovate with a source-mesh double-level parallel strategy, utilizing the message passing interface technique for parallel handling of independent CSEM data sets and large-scale geo-electrical models. Externally, we dedicate a processor for inversion model updates employing the Limited-memory Broyden–Fletcher–Goldfarb–Shanno optimization algorithm and divide other processors into groups, each associated with specific transmitting sources and frequencies. Internally, in each group, we employ a domain-decomposition-based scalable and robust iterative solvers using the Auxiliary-Space Maxwell pre-conditioner to parallel quickly calculate the electromagnetic responses from its assigned source-frequency set. Additionally, recognizing the potential for electrical conductivity anisotropy in field data, we incorporate the case of vertical transverse isotropy. We validate the effectiveness of our method through examples, including an isotropic land model with undulating topography, an anisotropic marine model and a real-field data case. Results from both synthetic and field data inversions underscore our method’s significant advancements in efficiency and practicality, particularly in addressing large-scale 3-D CSEM data sets inversion challenges in realistic geological environments.

Electrical conductivity model for transversely isotropic rocks with interconnected cracks

The electrical conductivity of subsurface rocks is generally anisotropic. The anisotropy of the subsurface electrical conductivity provides important information on the stress-strain state and geodynamics. To quantitatively interpret anisotropic conductivity structures revealed by electromagnetic surveys, it is essential to use a mixing model considering the anisotropy. Although there exists a mixing model for transversely isotropic rocks with crack-shaped pores, the previous model seems inappropriate in interpreting conductive anomalies revealed by electromagnetic exploration because cracks are assumed to be isolated in the model. Therefore, this study develops a theoretical mixing model for transversely isotropic rocks with mutually interconnected cracks by a statistical approach. The derived mixing model considers the macroscopic tortuosity of a collection of cracks as well as the tortuosity of each crack. The derived model can represent general transverse isotropy and includes the isotropic and parallel models as special cases. I compare the developed model to previously proposed mixing models, showing that the developed model can reproduce a much wider range of anisotropy than the already-existing anisotropic mixing model. By applying the developed model to an example of the anisotropic conductivity in the oceanic upper crust inferred by electromagnetic exploration, I demonstrate that the developed mixing model enables us to quantitatively infer the crack orientation and fluid volume fraction that reproduce significant anisotropic conductivity found by field observations. Furthermore, I compare the developed model to the anisotropic seismic velocity model for fluid-filled cracks.

Tackling capacitive coupling in broad-band spectral electrical impedance tomography (sEIT) measurements by selecting electrode configurations

SUMMARY Electromagnetic (EM) coupling effects including both inductive and capacitive coupling have long been an essential problem in broad-band spectral electrical impedance tomography (sEIT) measurements at the field scale. Efforts have been made to remove EM coupling numerically or to suppress the effects by modified data acquisition strategies. For near-surface applications with relatively small survey layouts, inductive coupling can be well removed in the mHz to kHz frequency range. With the use of shielded coaxial cables and so-called active electrodes where the amplifiers are mounted at the electrodes, capacitive coupling in sEIT measurements can also be reduced. However, it remains challenging to cope with capacitive coupling between the cable shield and the ground, especially in resistive field conditions. The aim of this study is to deal with this type of capacitive coupling effect by identifying and filtering out sEIT measurements that are strongly affected by capacitive coupling. Based on a correction method for capacitive coupling proposed in a previous study, an approach to estimate measurement errors due to capacitive coupling is presented first. In the second step, a workflow was proposed to calculate the capacitive coupling strength (CCS) for each electrode configuration, which is defined as the ratio of the imaginary part of the impedance induced by capacitive coupling and the imaginary part of the impedance due to the subsurface electrical conductivity. In the final step, measurements with low CCS were selected for inversion and the results were compared with inversion results obtained using the previously developed correction approach. It was found that the filtering method based on CCS is more capable in tackling capacitive coupling compared to using model-based corrections. Spectrally consistent sEIT results up to kHz were obtained using the newly developed filtering method, which were not achieved in previous work using model-based correction.

Groundwater Protection Assessment using Frequency Domain Electromagnetic Method and Direct Current Electrical Resistivity Method in Papalanto South-West Nigeria

The aim of this work is to assess the extent of protection of the subsurface hydrogeological structures. Fieldworks were performed integrated geophysical techniques namely Frequency Domain Electromagnetic Method (FDEM) using Geonics-EM-34 to determine the vertical and lateral variations of subsurface conductivity probing depths of 20m, 40m and 60m, while subsurface profiles were obtained using Direct Current Resistivity (DCRE) with AGI Super-sting Earth Resistivity meter; current electrode spacing (AB) ranging from 1 to maximum of 100m and the potential electrodes (MN) were consequently changed from 0.25m to 5m respectively. 1141.92 mmho/m was recorded as the highest true conductivity value for the Horizontal Dipole in the second layer (Profile EMPAP5) while the highest true conductivity value for the Vertical Dipole in the first layer was 134.31 mmho/m (Profile EMPAP1). Four principal geoelectric layers inferred from the VES data where the Topsoil is partly lateritic and alluvium, Sandy Clay/Clay/Silt, Sand/Clay/Shale, and Limestone/Sandstone. Resistivity values for these layers vary from 9.78 to 1428, 1.46 to 1057, 1.46 to 451, and 15 to 10,000 Ω m with corresponding thickness of 0.5m to1.43m, 1.29m to 13m, 2.8m to 84.4m and infinity, respectively. The higher resistivity values at the surface and extremes of both edges were indication of little or no presence of leachates or contaminant plume accumulation in other area. Also, there was a noticeable general decrease of resistivity values of formation rock with depth in these investigated areas; this is an indication that the plumes must have infiltrated rapidly into the subsurface through the massive presence of weathered rock materials of average lateral distances of 15m to 167m of considerable depth and thickness of about 24.9m. The degree of leachate contamination range from 0.0007264 mho to 0.668 mho with the highest value in VESPAP2 followed by VESPAP13 and VESPAP19 while VESPAP22 exhibited the lowest conductance.

Impacts of Terrain Conductivity Survey for Groundwater Investigation in a Typical Sedimentary Formation; A Case Study of Itori, South-West Nigeria

A geophysical investigation for groundwater development involving conductivity measurement using Electromagnetic profiling was conducted to locate fractured and fissured zones and associated groundwater containing media at Itori communities, Southwestern Nigeria. The area is underlain by the typical sedimentary rocks of South-West, Nigeria with the local geology predominantly clay, shale, clay sand, limestone and Sandstone. Measurements of the ground conductivity were carried out with Geonics EM 34-3 along 5 traverses whose profile lengths varied between 160 and 200 m. Intensive geophysical fieldworks were performed utilizing Frequency Domain Electromagnetic Method (FDEM) using Geonics-EM-34 to determine the vertical and lateral variations of subsurface conductivity probing depths of 20 m, 40 m and 60 m. The EM data were acquired at 500 m intervals along 10 profiles. The Vertical Dipole Moment in the first layer exhibited the highest true conductivity and lowest true conductivity of 42.65 mS/m and 29.1 mS/m for EMITO1 and EMITO2 respectively and the corresponding Horizontal Dipole Moment exhibited the highest true conductivity of 36.0 mS/m and 25.41 mS/m for EMITO1 alongside EMITO2 and EMITO8 respectively while the Vertical Dipole Moment in the second layer exhibited the highest true conductivity of 45.62 mS/m and lowest true conductivity of 34.76 mS/m for EMITO1 and EMITO8 respectively and the corresponding Horizontal Dipole Moment exhibited the highest true conductivity of 44.0 mS/m and lowest true conductivity of 36.3 mS/m for EMITO3 and EMITO8 respectively. The qualitative interpretation of EM results identified areas of hydrogeologic importance and forms a predictive and suggestive basis for Vertical Electrical Sounding (VES) investigation; points of positive EM anomalies were considered as priority area for electrical resistivity sounding and prospective groundwater development, since they suggest lithological variations within the unconsolidated overburden and/or water–filled fissures in the bedrock. The identified major geological interfaces were suspected to be of weathered zones

A probabilistic solution to geophysical inverse problems in complex variables and its application to complex resistivity imaging

SUMMARY We introduce a novel probabilistic framework for the solution of non-linear geophysical inverse problems in complex variables. By using complex probability distributions, this approach can simultaneously account for individual errors of real and imaginary data parts, independently regularize real and imaginary parts of the complex model, and still take into account cross-sensitivities resulting from a complex forward calculation. The inverse problem is solved by means of optimization. An application of the framework to complex resistivity (CR) imaging demonstrates its advantages over the established inversion approach for CR measurements. We show that CR data, with real and imaginary parts being subject to different errors, can be fitted adequately, accounting for the individual errors and applying independent regularization to the real and imaginary part of the subsurface conductivity. The probabilistic framework itself serves as a basis for the future application of global sampling approaches, such as Markov chain Monte Carlo methods.

PERFORMANCE EVALUATION OF VERY LOW FREQUENCY (VLF) TECHNIQUES FOR AQUIFER CONTAMINATION ASSESSMENT

This study explores the efficacy of VLF techniques in mapping aquifer contamination. Conducted in Agbor, a city within Delta State, Nigeria, the study focuses on a contaminated aquifer of approximately 5 km² known for nitrate, petroleum hydrocarbon, and heavy metal contamination. The methodology involves VLF data collection using a Geonics EM-16 VLF receiver and transmitter, subsequent analysis using Geonics VLF2XYZ software, and comparison with traditional groundwater monitoring methods. The results indicated that VLF surveys effectively differentiate between contaminated and clean areas based on subsurface conductivity. The study demonstrates the cost-effectiveness and efficiency of VLF techniques compared to traditional methods, as VLF surveys cover larger areas in less time without drilling. Interpreting VLF resistivity maps reveals potential contamination areas, aiding in identifying contaminant sources and pathways. The validation framework assesses VLF techniques' accuracy, precision, sensitivity, and robustness, emphasizing the need for careful interpretation and validation with traditional methods. Implications for aquifer contamination assessment include mapping contamination extent, identifying new contamination areas, and monitoring remediation efforts. The study concludes that VLF techniques offer a promising and cost-effective method for aquifer contamination assessment. Recommendations include incorporating VLF techniques into site investigation programs, further research to optimize VLF technologies, site-specific investigations based on VLF survey results, and regular VLF surveys for ongoing monitoring and risk assessment. Overall, the findings contribute to alternative methods for aquifer contamination assessment, enhancing understanding and managing groundwater resources.

MTAINV3D: A Three-dimensional Adaptive Magnetotelluric Inversion Tool Using Unstructured Grids

Currently, 3D magnetotelluric inversion often uses a fixed inversion grid. Too sparse inversion grids cannot approximate the complex subsurface conductivity distribution, and may also lead to inversion results failing to converge. Too dense inversion grids will increase not only the computational cost, but also the non-uniqueness of the inversion. Aiming at the above problems, we have developed a three-dimensional magnetotelluric adaptive inversion strategy with unstructured finite element and limited memory BFGS (L-BFGS) algorithms. Firstly, based on the unstructured finite element method, the high accuracy forward responses of the 3D geo-electric model with arbitrary complex terrain and conductivity distributions can be obtained. Then, the forward and inversion meshes are decoupled by using the nested forward and inversion tetrahedral meshes. The Thikhonov regularization objective function with smooth constraints is established and minimized by L-BFGS optimization algorithm with inexact line search procedure, where the gradient of the objective function is solved by using the adjoint principle. Most importantly, an inversion grid adjustment strategy based on the dual constraints of sensitivity matrix and model gradient is proposed, which are used to guide the automatic refinement of inversion grid in the optimization process. Finally, a topographic model is used to validate and test the performance of the proposed adaptive inversion algorithm.

An innovative hydrological model for the sparsely-gauged Essequibo River basin, northern Amazonia

ABSTRACT Tropical river basins – crucial components of global water and carbon cycles – are threatened by logging, mining, agricultural conversion, and climate change. Thus, decision-makers require hydrological impact assessments to sustainably manage threatened basins, such as the ∼68,000 km2 Essequibo River basin in Guyana. Emerging global data products offer the potential to better understand sparsely-gauged basins. We combined new global meteorological and soils data with established in situ observations to build the first physically-based spatially-distributed hydrological model of the Essequibo. We developed new, open source, methods to translate global data (ERA5-Land, WFDE5, MSWEP, and IMERG) into a grid-based SHETRAN model. Comparing the performance of several global and local precipitation and evaporation datasets showed that WFDE5 precipitation, combined with ERA5-Land evaporation, yielded the best daily discharge simulations from 2000 to 2009, with close water balances (PBIAS = −3%) and good discharge peaks (NSE = 0.65). Finally, we tested model sensitivity to key parameters to show the importance of actual to potential evapotranspiration ratios, Strickler runoff coefficients, and subsurface saturated hydraulic conductivities. Our data translation methods can now be used to drive hydrological models nearly anywhere in the world, fostering the sustainable management of the Earth’s sparsely-gauged river basins.

Magnetotelluric images of the medium enthalpy Bakreswar geothermal province within a granitic gneissic complex, Eastern Indian Peninsula

AbstractThe Bakreswar geothermal province represents a medium enthalpy geothermal system with its Bakreswar and Tantloie hot springs. It lies within the Chotanagpur Granite Gneissic Complex in the eastern part of the Indian Peninsula. The province has a high heat flow and a high geothermal gradient of 90°C/km. Magnetotelluric data from 95 sites in a frequency range of 10 kHz–10 Hz were acquired over the Bakreswar geothermal province to obtain an electrical conductivity model and map the geothermal reservoir with its fluid pathways and related geological structures. Subsurface conductivity models obtained from three‐dimensional inversions of the Magnetotelluric data exhibit several prominent anomalies, which are supplemented by gravity results. The conductivity model maps three features which act as a conduit (a) a northwest–southeast trending feature, (b) an east–west trending feature to the south of the northwest–southeast trending feature (which lies 1 km north of the Oil and Natural Gas Corporation fault marked by previous studies) and (c) shallow conducting features close to Bakreswar hot spring. The northwest–southeast trending feature coincides with the boundary of the high‐density intrusive block. This northwest–southeast trending feature provides the pathway for the meteoric water to reach a maximum depth of 2.7 km, where it gets heated by interacting with deep‐seated structures and then it rises towards the surface. The radiogenic process occurring within the granites of Chotanagpur Granite Gneissic Complex provides the heat responsible for heating the meteoric water. The northwest–southeast and east–west trending features are responsible for the transport of meteoric water to deeper depths and then towards the shallow regions of the Earth. The near surface features close to the Bakreswar hot spring are responsible for carrying the water further towards the hot spring. The resistivity of these structures plotted as a function of salinity and temperatures for saline crustal fluids suggests the involvement of meteoric water. Further, applying Archie's law to this resistivity suggests that the conduit path has a porosity greater than 10%. This study successfully maps the anomalous structures which might foster the migration of geothermal fluid in Bakreswar geothermal province.

Impact of complementary irrigation on soil properties in the Argentine Pampas

The growing application of complementary irrigation in extensive agricultural systems in the Pampas region (Argentina) has increased the frequency of disturbed soil cases. Irrigation management requires appropriate indicators for decision-making in order to avoid disturbance in soil quality. Among these indicators, the use of the sodium adsorption ratio adjusted by the dilution factor (SARdf), which considers the contributions of irrigation water and rainfall during the period projected, could be a sensitive tool. Based on studies conducted under extensive conditions of agricultural production, the objectives of this work were to (i) quantify the changes in the soil chemical and physical properties caused by the use of complementary irrigation with sodium bicarbonate water; (ii) establish relationships between the changes in the chemical and physical properties of the soil; (iii) validate the use of SARdf as a tool to help manage complementary irrigation; and (iv) identify the threshold values to be considered when monitoring and analyzing the evolution of soil properties in areas under complementary irrigation. Twenty-three sites under dryland and irrigated conditions were evaluated. With increasing SARdf, an increase was also recorded in surface bulk density (r = 0.59), subsurface bulk density (r = 0.50), electrical conductivity (r = 0.30; 0.41), pH (r = 0.56; 0.70) and exchangeable sodium ratio (ESR) (r = 0.45; 0.77), measured in the layers up to 0.10 m and up to 0.20 m in depth. The ESR was evaluated as a sensitive indicator of the effects of irrigation on the physical and chemical properties of soils. In the predominant Typic Argiudoll conditions of the Pampas region, it is advisable to avoid the complementary application of water with a SAR dilution factor greater than 3.0. This threshold value was associated with a > 60% reduction in basic infiltration. In climate change scenarios, the use of SARdf can help plan the proper management of complementary irrigation.

Oscillatory thermal response tests to estimate the ground thermal diffusivity

Thermal Response Tests (TRT) have been extensively studied in the past decades and is an important area of research belonging to ground heat exchanger design. The most important thermal property inferred in a TRT is the equivalent subsurface thermal conductivity. Previous studies were presented where the thermal diffusivity was estimated but with limited success. In this paper, a TRT with an oscillatory excitation (OTRT) is used to obtain a better uncoupling between thermal conductivity found from constant heat injection and thermal diffusivity. The method was already used in the past with a simplified ground model. It is presented here with a better model that takes into account the ground and the borehole thermal response. The method is based on the concept of concentric composite cylinder where the equivalent radius is used for the U-tube arrangement. Results obtained with this method were successfully correlated to those obtained with numerical simulations and the method was then used to analyze an experimental field test.

3D inversion of multifrequency controlled-source electromagnetic data and its application to geothermal exploration in the Tianzhen region of the northern Datong Basin, China

Owing to its sensitivity to hydrothermal alteration and geothermal fluids, the distribution of subsurface electrical conductivity provides critical information for characterizing geothermal systems. For geothermal exploration, the controlled-source electromagnetic (CSEM) technique provides a crucial tool to image the subsurface resistivity structures, especially in areas with strong cultural noise. Usually, land-based CSEM surveys are carried out with dozens of operating frequencies to enhance spatial resolution. However, the 3D inversion of multifrequency CSEM data using the commonly used direct solver is challenging with limited computational resources. In this study, we implement a practical inversion strategy for interpreting 3D multifrequency CSEM data. By combining a hybrid direct-iterative solver with the inexact Gauss-Newton optimization, 3D inversion of CSEM data involving multiple frequencies can be performed effectively on typical workstations. First, we test the effectiveness of the developed approach using synthetic multifrequency 3D CSEM data generated for a simplified geothermal model. The inversion strategy is then applied to the multifrequency CSEM field data collected for the geothermal exploration at Tianzhen region in Shanxi Province, China. The comparison between the inversion results using the sparse data set (six frequencies) and the dense data set (12 frequencies) highlights the necessity of using data from more frequencies in the inversion to improve the resolution. The resulting 3D resistivity model clearly delineates the clay alteration layers and shallow thermal reservoirs of the potential high-temperature geothermal system within the study area, while its deep heat source is not revealed owing to the limited investigation depth of the survey.

Hydrogeologic controls on barrier island geomorphology: Insights from electromagnetic surveys

Barrier islands provide a first line of defense for coastal communities against storms, hurricanes, and sea-level rise. The geomorphology of barrier islands exerts a major control on storm impact and island recovery. In turn, barrier island geomorphology is affected by subsurface hydrogeologic conditions. In this study, we investigated the relationship between subsurface hydrogeologic conditions and geomorphology of Padre Island, with a focus on the influence of human development. We measured apparent electrical conductivities using frequency-domain electromagnetic (FDEM) surveys and spatially correlated them with the island's morphology. The latter was generated from a 1 m resolution digital elevation model. Four distinct zones were identified from the observed variations in apparent conductivity and elevation, revealing their inverse correlation. The beach area (Zone I) exhibits the highest apparent conductivity (289.7 ± 66.3 mS/m) and the lowest elevations (1.4 ± 0.2 m). These trends are largely due to the proximity of the beach to saline groundwater and maritime floods. Conversely, the foredune area (Zone II) presents the lowest apparent conductivity (19.0 ± 3.4 mS/m) and the highest elevation (4.5 ± 0.4 m) due to a greater distance from saline waters, deeper groundwater levels, and relatively dry soil conditions. Human development has significantly impacted Zones III (east central zone) and IV (west central zone), contributing to an increase in apparent conductivity (Zone III: 40.3 ± 21.8 mS/m; Zone IV: 159.5 ± 83.0 mS/m) and a reduction in elevation (Zone III: 2.1 ± 0.5 m; Zone IV: 1.3 ± 0.4 m). Anthropogenic activities have modified hydrologic patterns, introduced conductive materials, and altered vegetation cover and soil composition. This research elucidates the interplay between subsurface electrical conductivity, surface morphology, and the impact of human development on barrier island geomorphology, providing crucial insights for coastal management and conservation efforts.

An information theoretic Bayesian uncertainty analysis of AEM systems over Menindee Lake, Australia

SUMMARY Long-range, active-source airborne electromagnetic (AEM) systems for near-surface conductivity imaging fall into two categories: helicopter (rotary-wing) borne or fixed-wing aircraft borne. A multitude of factors such as flying height, transmitter loop area and current, source waveforms, aerodynamic stability and data stacking times contribute to the geological resolvability of the subsurface. A comprehensive comparison of the relative merits of each system considering all such factors is difficult, but test flights over well-constrained subsurface geology with downhole induction logs are extremely useful for resolution studies. However, given the non-linear nature of the electromagnetic inverse problem, handling transmitter–receiver geometries in fixed-wing aircraft is especially challenging. As a consequence of this non-linearity, inspecting the closeness of downhole conductivities to deterministic inversion results is not sufficient for studying resolvability. A more comprehensive picture is provided by examining the variation in probability mass of the depth-wise Bayesian posterior conductivity distributions for each kind of AEM system within an information theoretic framework. For this purpose, probabilistic inversions of data must be carried out. Each acquiring system should fly over the same geology, survey noise levels must be measured and the same prior probabilities on conductivity must be used. With both synthetic models as well as real data from over the Menindee calibration range in New South Wales, Australia, we shed new light on the matter of AEM inverse model uncertainty. We do this using two information theoretic attributes derived from different Kullback–Leibler divergences—Bayesian information gain, and a strictly proper scoring rule, to assess posterior probabilities estimated by a novel Bayesian inversion scheme. The inversion marginalizes fixed-wing geometry attributes as generic nuisance parameters during Markov chain sampling. This is the first time-domain AEM study we know of, that compares nuisance marginalized subsurface posterior conductivities from a fixed-wing system, with rotary-wing derived posterior conductivities. We also compare field results with induction log data where available. Finally, we estimate the information gain in each case via a covariate shift adaptation technique that has not been used before in geophysical work. Our findings have useful implications in AEM system selection, as well as in the design of better deterministic AEM inversion algorithms.