Subsurface Distribution Research Articles

An efficient 3D finite element procedure for simulating wheel–rail cyclic contact and ratcheting

Various models for simulating rail ratcheting behaviour were developed to study rolling contact fatigue (RCF) damage in rails. However, limitations remain in terms of the accuracy of wheel–rail contact modelling and computational efficiency of the cyclic loading simulation. This study developed an efficient 3D finite element (FE) procedure to simulate ratcheting in rails subjected to numerous load cycles. The procedure simulates a wheel rolling repeatedly over a rail section with updated stress–strain states, enabling automatically executed cyclic loading simulation given a predefined number of cycles. To ensure the accuracy of the contact modelling, the effect of meshing schemes on subsurface stress distribution was examined. In addition, the FE contact model with the selected meshing scheme, which balances accuracy and computational efficiency, was verified against the widely accepted CONTACT program. Subsequently, a non-linear kinematic hardening (NLKH) steel material was used in the FE model for ratcheting simulations with up to 100 wheel-loading cycles. The rail surface and subsurface stress states were replicated under partial-slip wheel–rail rolling contact conditions with traction coefficients of 0.10, 0.20 and 0.35, respectively. The ratcheting behaviour was extensively analysed in terms of plastic deformation, contact patch evolution, and ratcheting rates. The simulated plastic deformation was found to alter the contact geometry and thus contact stresses, which in turn affect further accumulation of plastic deformation and subsequent ratcheting strains. These findings highlighted the importance of considering the interplay between the rail ratcheting behaviour of the rail and evolving contact conditions for predicting ratcheting and RCF damage in rails.

Soil–water contamination assessment due to dumpsite-impacted leachates in a metamorphic environment

Environmental contamination is a complex issue that significantly impacts human health and ecological systems, especially in developing countries like Nigeria. This study explores the interplay between surface-subsurface soil-rock resistivity and subsurface geotechnical properties, along with multiple soil-water pollution indices at and around the Old Owo-Ikare road (OOIR) dumpsite in southwestern Nigeria. Water samples from five wells and one stream were collected and analyzed, alongside twelve soil samples from four trial pits at varied depths for geoenvironmental assessment. Analyzed hydrogeochemical parameters include Na, Ca, Mg, K, Cr, Pb, Zn, Ni, Mn, Fe, and cations (NO, SO4, Cl) in the water samples. Nine dipole-dipole electrical resistivity tomography (ERT) profiles and six Schlumberger vertical electrical sounding (VES) points were used to determine surface–subsurface resistivity distribution. Water analysis results showed elevated levels of K, Ni, Cr, and Pb concentrations. Geotechnical tests indicated that most dumpsite soil meets good landfill material criteria with low permeability (5.92×10−6 to 1.49×10−4cm/s). ERT and VES inverted models revealed four lithologic layers with two main curve types (HA and QA). Elevated heavy metal levels in the dumpsite soil and inverted resistivity models suggested leachate contamination in the topsoil and weathered bedrock units. Despite relatively lower heavy metal concentrations compared to other Nigerian and Ghanaian dumpsites, multi-pollution indices indicated significant contamination in both water and soil samples, posing moderate to very high environmental risks based on World Health Organisation standards. Additionally, human health risk assessment suggested a risk of cancer and non-cancer-related diseases from prolonged well water consumption. Therefore, ongoing monitoring and remediation efforts are essential to forestall imminent pollution in uncovered areas.

Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation

Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R2 ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments.

Surface characteristics and material removal mechanisms during nanogrinding on C-face and Si-face of 4H-SiC crystals: Experimental and molecular dynamics insights

4H-SiC wafers hold significant promise for applications in power devices and radiofrequency devices. In order to develop a cost-effective and efficient grinding technology for 4H-SiC wafers, it is imperative to thoroughly investigate the mechanisms underlying damage evolution on different crystal faces of 4H-SiC. Our approach involved employing a range of experimental techniques including nanoindentation, scratching, and grinding, coupled with molecular dynamics (MD) simulation to systematically analyze the material removal mechanisms on both the C-face and Si-face. Through a comprehensive comparison of mechanical properties, surface morphology, and subsurface damage distribution between the C-face and Si-face, we uncovered the plastic deformation mechanisms operating on each face. Our research findings emphasize that, compared to the Si-face, the C-face exhibits higher elastic modulus and hardness, but lower fracture toughness. Additionally, due to the C-face’s greater propensity for high-density dislocation formation compared to the Si-plane, stress release occurs in the machining area, resulting in smaller normal forces and aiding in reducing surface roughness after grinding. This study provides important guidance for the grinding of the two critical basal planes of single-crystal 4H-SiC.

Deep generative networks for multivariate fullstack seismic data inversion using inverse autoregressive flows

The simultaneous prediction of the subsurface distribution of facies and acoustic impedance (IP) from fullstack seismic data requires solving an inverse problem and is fundamental in natural resources exploration, carbon capture and storage, and environmental risk management. In recent years, deep generative models (DGM), such as variational autoencoders (VAE) and generative adversarial networks (GAN), were proposed to reproduce complex facies patterns honoring prior geological information. Variational Bayesian inference using inverse autoregressive flows (IAF) can be performed to infer the solution to a geophysical inverse problem from the encoded latent space of such pre-trained DGM. Successful applications of such approach on crosshole ground-penetrating radar synthetic data inversion demonstrated that the technique's accuracy is comparable to that of Markov chain Monte Carlo (MCMC) inference methods, while significantly reducing the computational cost. Nonetheless, these application examples did not account for the spatial uncertainty affecting the facies-dependent continuous physical property, from which the geophysical data are calculated. This uncertainty can significantly affect the inversion accuracy and its applicability to real data. In this work, specific VAE and GAN architectures are proposed to simultaneously predict facies and co-located IP, while accounting for their spatial uncertainties. The two types of generative networks are used in Bayesian inversion with IAF for the inversion of seismic data. The results are found to reproduce the statistics of the training images and solve the seismic inversion problem accurately, comparably to MCMC inversion. Furthermore, advantages and limitations of the two DGMs are evaluated by comparing the results obtained.

A simplified spherical roller bearing model and its application in the wind turbine main bearing system finite element modeling

Different from most bearing design guidelines, deformation of bearing rings and supporting structures usually influences the bearing performance significantly. Finite element analysis (FEA) can be used in bearing system design considering the structure deformation, but the contact region between rolling elements and raceways modeling is very complex and usually causes non-convergent problems. As to a spherical roller bearing (SRB), the model must also consider both its misalignment ability and roller contact stiffness. This paper presents a simplified SRB model considering the angular misalignment ability. In order to define the SRB roller raceway load deformation relationship, a lamina model is developed. And this simplified model is validated by solid roller model of a bearing sector. A wind turbine main bearing system is analyzed using the developed SRB model. Load distribution considering the system deformation and angular misalignment are discussed. With the help of fine meshed solid elements roller raceway contact model, the raceway contact pressure and subsurface stress distribution can also be obtained.

Modeling and interpretation of electrical resistivity data in anisotropic media using a generalized regression neural network: A case study on the Lokbatan mud volcano, Azerbaijan

This study explores the electrical anisotropy effects in a vertical electrical sounding dataset acquired from the Lokbatan mud volcano in Azerbaijan. Vertical electrical sounding is a fundamental geophysical technique for mapping subsurface resistivity distributions; however, electrical anisotropy is a factor often ignored, can significantly influence field measurements, especially with respect to layer thicknesses. This study investigates electrical anisotropy effects on vertical electrical sounding data using generalized regression neural networks for estimating parameters of layered subsurface. Schlumberger electrode arrays were employed for these purposes with a 22-sounding dataset. In general, a classical approach for estimating electrical anisotropy coefficient fails. Vertical and horizontal resistivity values cannot be estimated using vertical electrical sounding data. Thus, generalized regression neural networks are considered. The generalized regression neural networks for appraising parameters of electrical anisotropy can be used with keeping vertical resistivity higher than horizontal resistivity in each layer as an assumption. Further, the proposed method applied how the surrounding clay-rich geological material and layered structure influence electrical properties of the Lokbatan mud volcano in Azerbaijan. As a conclusion, without considering electrical anisotropy makes interpretation erroneous especially with respect to layer thicknesses.

Groundwater Study Using Vertical Electrical Sounding (VES) Data Based on Resistivity and Porosity of Rocks in Kampung Melayu, Bengkulu City

This research aims to identify groundwater based on the value of resistivity and porosity of rocks, which are important parameters in the management of groundwater resources and Development Planning in the region. This groundwater research using Vertical Electrical Sounding (VES) data based on the value of resistivity and porosity of rocks was carried out by measuring resistivity using the (VES) method at 20 sounding points located in Kampung Melayu District, Bengkulu City. The VES method is one of the approaches in the geoelectric method to determine the lithological characteristics of subsurface rocks. Resistivity Data obtained were then inverted and analyzed and interpreted to obtain lithology of subsurface rocks and groundwater level distribution in Kampung Melayu, Bengkulu City. The results showed the presence of groundwater based on low resistivity anomaly values with a range of values from 5 to 30 meters in the West and 10 to 50 meters in the East, with resistivity values ranging between 5 Ω.m up to 45 Ω.m. This research is expected to make a significant contribution to the management of groundwater resources in the District of Kampung Melayu, Bengkulu City and can be the basis for development planning in the area. In addition, it is hoped that the results of this research can be a reference for further research in the field of hydrogeology and groundwater modeling. Keywords: Aquifer; geoelectricity; groundwater; porosity; resistivity

Fracture identification of carbonate reservoirs by deep forest model: An example from the D oilfield in Zagros Basin

Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution. Typically, conventional logs exhibit responses in fracture zones, and almost all wells have such logs. However, detecting fractures through logging responses can be challenging since the log response intensity is weak and complex. To address this problem, we propose a deep learning model for fracture identification using deep forest, which is based on a cascade structure comprising multi-layer random forests. Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning. The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield, Zagros Basin, Middle East, and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures. The log package includes the gamma-ray, caliper, density, compensated neutron, acoustic transit time, and shallow, deep, and flushed zone resistivity logs. Experiments have shown that the deep forest obtains high recall and accuracy (>92%). In a blind well test, results from the deep forest learning model have a good correlation with fracture observation from cores. Compared to the random forest method, a widely used ensemble learning method, the proposed deep forest model improves accuracy by approximately 4.6%.

Subsurface architecture of the Wajrakarur Kimberlite Field, Dharwar Craton, India: Evidence from ground magnetic surveys

The Ground magnetic study in the Wajrakarur Kimberlite Field and adjoining area has unveiled prominent geological variations, including felsic doming beneath the PGC-II formation to the west of the Cuddapah Basin and mafic doming beneath the Closepet Granite zone. These doming features within the upper mantle are identified as sources of mantle upwelling, which influence the emplacement of corresponding intrusive rocks. The presence of mafic chambers enhances the magnetic response, leading to localized high signatures in magnetic data and contributes to a better understanding of the subsurface composition and distribution of rock types. The present magnetic data throw lights to study the complexity of the sub-surface structure and geological activities, have shaped the study area and serve as valuable markers for understanding the geological evolution of the area. The intersections of magnetic lineament oriented in NW-SE, NE-SW, E-W yield crucial insights into geological structures and the emplacement of Kimberlite intrusions. Through comprehensive magnetic data analysis, the study offers a unique cross-sectional view of the subsurface, reaching depth up to 60 km. This perspective unveils magnetic sources and structures, revealing shallow features, interfaces of the Archaean basement, crustal roots, and upper mantle configuration. Profound magnetic gradients observed in the study area serve as indicators of fault lines influenced by tectonic activity, kimberlite intrusion, and other fault-related mineralization if any. These features contribute to a deeper understanding of the structural configurations and geological history of the area. The present study provides information about the lateral and vertical layouts of major structures as well as the deep-seated channels. It also introduces the mafic and felsic categories of rocks, explaining the complex relationships between underlying structures, geological characteristics, and magnetic anomalies.

Predicting natural arsenic enrichment in peat-bearing, alluvial and coastal depositional systems: A generalized model based on sequence stratigraphy

Hazardously high concentrations of arsenic exceeding the threshold limits for soils and drinking waters have been widely reported from Quaternary sedimentary successions and shallow aquifers of alluvial and coastal lowlands worldwide, raising public health concerns due to potential human exposure to arsenic. A combined sedimentological and geochemical analysis of subsurface deposits, 2.5–50 m deep, from the SE Po Plain (Italy) documents a systematic tendency for naturally-occurring arsenic to accumulate in peat-rich layers, with concentrations invariably greater than maximum permissible levels.A total of 366 bulk sediment samples from 40 cores that penetrated peat-bearing deposits were analysed by X-ray fluorescence. Arsenic concentrations associated with 7 peat-free lithofacies associations (fluvial-channel, levee/crevasse, floodplain, swamp, lagoon/bay, beach-barrier, and offshore/prodelta) exhibit background values invariably below threshold levels (<20 mg/kg). In contrast, total arsenic contents from peaty clay and peat showed 2–6 times larger As accumulation. A total of 204 near-surface (0–2.5 m) samples from modern alluvial and coastal depositional environments exhibit the same trends as their deeper counterparts, total arsenic peaking at peat horizons above the threshold values for contaminated soils.The arsenic-bearing, peat-rich Quaternary successions of the Po Plain accumulated under persisting reducing conditions in wetlands of backstepping estuarine and prograding deltaic depositional environments during the Early-Middle Holocene sea-level rise and subsequent stillstand. Contamination of the Holocene and underlying Pleistocene aquifer systems likely occurred through the release of As by microbially-mediated reductive dissolution. Using high-resolution sequence-stratigraphic concepts, we document that the Late Pleistocene-Holocene lithofacies architecture dictates the subsurface distribution of As. The “wetland trajectory”, i.e. the path taken by the landward/seaward shift of peat-rich depositional environments during the Holocene, may help predict spatial patterns of natural As distribution, delineating the highest As-hazard zones and providing a realistic view of aquifer contamination even in unknown areas.

Subsurface distribution of granites and greenstones in the Paleoarchean East Pilbara Terrane from 3-D gravity inversion

The Paleoarchean East Pilbara Terrane contains some of the oldest rocks on Earth, and has a distinctive surface architecture of granite domes surrounded by arcuate greenstone belts. Explanations for creation of the domes include fold interference, core complex formation and the subsidence of a dense, mafic crust between relatively buoyant granitic complexes. In the latter “sagduction” process, greenstones are often viewed as descending to the base of the crust. Here we present a 3-D inversion of Bouguer gravity data for density contrast constrained by shear wave velocities and a seismic reflection profile. We show that the East Pilbara Terrane is predominantly characterized by a two-layer crust with high-density greenstone rocks largely confined to the upper crust, which is inconsistent with vertical tectonic models requiring large-scale greenstone preservation in the lower crust. The eastern margin of the terrane is marked by a west-to-east transition from low-density ovoid bodies to linear northwest-trending sources associated with the Proterozoic Paterson Orogen. We identify the previously unknown 10–15 km wide Tumbinna Pool Dome, between the Mount Edgar and Yilgalong domes. Higher density sources related to the surface distribution of greenstone rocks largely occur above 10–15 km depth, though they may locally reach >15 km depth on the north side of the Mount Edgar Dome. Some granitic domes have higher density cores related to distinct granite suites, while the Split Rock Supersuite appears to exhibit systematically lower density at depths of 5–10 km. Low-density sources due to small intrusions such as the North Pole Dome only extend to depths up to 10 km, while the large Mount Edgar Dome can be identified at depths as great as 15–20 km. Some low-density bodies in the lower crust, one of which underlies the Yilgalong Dome, correlate with regions of reduced seismic reflectivity, which we interpret to be possible relict migmatisation zones or melt pathways. A weakly constrained ESE-trending high-density region immediately below the Moho correlates with isolated sub-Moho reflectors, and may be a relict zone of rifting and delamination.

Physics-informed W-Net GAN for the direct stochastic inversion of fullstack seismic data into facies models

Predicting the subsurface spatial distribution of geological facies from fullstack geophysical data is a main step in the geo-modeling workflow for energy exploration and environmental tasks and requires solving an inverse problem. Generative adversarial networks (GANs) have shown great potential for geologically accurate probabilistic inverse modeling, but existing methods require multiple sequential steps and do not account for the spatial uncertainty of facies-dependent continuous properties, linking the facies to the observed geophysical data. This can lead to biased predictions of facies distributions and inaccurate quantification of the associated uncertainty. To overcome these limitations, we propose a GAN able to learn the physics-based mapping between facies and seismic domains, while accounting for the spatial uncertainty of such facies-dependent properties. During its adversarial training, the network reads the observed geophysical data, providing solutions to the inverse problems directly in a single step. The method is demonstrated on 2-D examples, using both synthetic and real data from the Norne field (Norwegian North Sea). The results show that the trained GAN can model facies patterns matching the spatial continuity patterns observed in the training images, fitting the observed geophysical data, and with a variability proportional to the spatial uncertainty of the facies-dependent properties.

Analysis of Subsurface Resistivity Distribution of the Kelud Volcano Using Magnetotelluric Method

Indonesia has the world’s most active volcanoes, with more than 30% of them lying within its borders. The Kelud volcano erupted in 2007, resulting in the development of a Lava Dome in the crater lake. This phenomena is known as effusive. The magnetotelluric approach is used in this study to predict the subsurface resistivity distribution in the Kelud volcano area. Data was obtained at 10 different locations around the Kelud volcano area. The subsurface structure of the Kelud Volcano can be identified based on resistivity values that show significant differences, according to the modelling results. According to the findings, the surface resistivity surrounding the Kelud volcano ranges from 1 to 100 ohm. The main structure of the hydrothermal system’s clay cap was discovered at a depth of 1000 m from the surface in the western part of the summit, with resistivity values ranging from 1-10.m. With a resistivity of more than 100 ohm.m., a high resistivity forming a conduit appears in the centre of the summit area. The subsurface structure values obtained indicate that these structures are volcanic.

Trial-and-error modeling of ground-airborne electromagnetic data in the Yishu Faulting Basin, China

Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.

Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model

Abstract Lithostratigraphic modeling holds a vital role in mineral resource exploration and geological studies. In this study, we introduce a novel approach for automating pseudo-lithostratigraphic modeling in the deep subsurface, leveraging inversed geophysical properties. We propose a three-dimensional convolutional neural network with adaptive moment estimation (3D Adam-CNN) to achieve this objective. Our model employs 3D geophysical properties as input features for training, concurrently reconstructing a 3D geological model of the shallow subsurface for lithostratigraphic labeling purposes. To enhance the accuracy of pseudo-lithostratigraphic modeling during the model training phase, we redesign the 3D CNN framework, fine-tuning its parameters using the Adam optimizer. The Adam optimizer ensures controlled parameter updates with minimal memory overhead, rendering it particularly well-suited for convolutional learning involving huge 3D datasets with multi-dimensional features. To validate our proposed 3D Adam-CNN model, we compare the performance of our approach with 1D and 2D CNN models in the Qingniandian area of Heilongjiang Province, Northeastern China. By cross-matching the model’s predictions with manually modeled shallow subsurface lithostratigraphic distributions, we substantiate its reliability and accuracy. The 3D Adam-CNN model emerges as a robust and effective solution for lithostratigraphic modeling in the deep subsurface, utilizing geophysical properties.

Estimation of the subsurface electromagnetic velocity distribution from diffraction hyperbolas by means of a novel automated picking procedure: Theory and application to glaciological ground-penetrating radar data sets

We have developed an auto-picking algorithm that is designed to automatically detect subsurface diffractors within ground-penetrating radar (GPR) data sets, to accurately track the hyperbolic diffractions originating from the identified scatterers, and to recover the subsurface electromagnetic (EM) velocity distribution, among other possible analyses. Our procedure presents several advantages with respect to other commonly applied diffraction tracking techniques because it can be applied with minimal signal preprocessing, thus making it more versatile and adaptable to local conditions; it requires only limited input from the interpreter in the form of a few thresholds for the tracking parameters, thus making the results more objective; and it does not involve pretraining as opposed to machine-learning algorithms, thus removing the need to gather a large and comprehensive image database of all possible subsurface situations, which would not necessarily be limited to only examples of diffractions. The presented algorithm starts by identifying those signals that are likely to belong to diffraction apexes, which are then used as initial seeds by the auto-tracking process. The horizontal search window used during the auto-tracking process is locally adapted through a rough preliminary estimate of the size of each diffraction. In addition, multiple seeds within the same apex can produce several acceptable hyperbolas tracking the same diffraction phase. The algorithm thus selects the best-fitting ones by assessing several signal attributes while also removing redundant hyperbolas and the expected false positives. The algorithm is applied to two glaciological GPR profiles, and it is able to accurately track the vast majority of the recorded diffractions, with very few false positives and negatives. This produces a statistically sound EM velocity distribution, which was used to assess the state of the surveyed alpine glacier.

Delineation of the proper locations for wastewater disposal in arid regions using TEM and ERT techniques: A case study at Rubiki industrial zone, Badr City, Egypt

Proper wastewater disposal is crucial for the sustainable development of rapidly growing megacities worldwide. The emergence of waterlogging near evaporation ponds (EVP) and in desert areas surrounding these cities poses a significant environmental threat. This study employs an integrated approach, combining transient electromagnetic (TEM) and 2D-electrical resistivity tomography (2D-ERT) methods with remote sensing techniques, to understand wastewater flow patterns around EVPs and identify suitable disposal sites. The research focuses on a complex subsurface structure in the Cairo-Suez district, characterized by extensive surface waterlogging. Initial monitoring relies on satellite imagery and land use-land cover maps. TEM provides an overview of subsurface layer distribution, while 2D-ERT offers detailed insights into near-surface flow paths. To address the limitations of geoelectrical methods in locating conductive layers, advanced inversion techniques (AIS) are applied. The findings reveal a four-layer subsurface composition, with waterlogging primarily attributed to the argillaceous limestone layer interbedded with shale. This layer is shallower in the eastern and northwest areas, correlating with higher instances of waterlogging. Moving northward, the argillaceous limestone layer deepens, and the upper clastic layer thickens, resulting in reduced waterlogging. The study underscores the influence of subsurface structure on near-surface layer distribution. Based on these findings, optimal wastewater disposal sites are recommended, with a focus on the northern region where the argillaceous limestone layer is deeper and the upper clastic layer is thicker. To mitigate waterlogging, the installation of disposal wells is highly advised, leveraging the insights derived from this research. This study offers a versatile and reproducible approach suitable for both pre-project planning and post-project phases in desert regions.

Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method.

Re-evaluation of a geothermal system model based on high-resolution gravity field inversion: A case study of the Maritaing geothermal field, Alor Regency, East Nusa Tenggara Province, Indonesia

The Maritaing region in East Nusa Tenggara Province, Indonesia, offers significant geothermal potential for direct power generation and use. This study aims to re-evaluate the geothermal system model in the Maritaing geothermal field using high-resolution gravity inversion. High-resolution gravimetric data were collected using satellite observations and data processing techniques, allowing a better understanding of the subsurface geological structure and density distribution of rocks associated with the geothermal reservoir. This study applied data corrections to acquire complete Bouguer anomalies, gravity gradient analysis to detect geological structures, and inversion modeling of residual gravity anomalies to predict subsurface rock density distribution. The complete Bouguer anomalies exhibit positive values ranging from 70 to 140 mGal and have a northwest–southeast orientation, indicating the existence of andesitic lava and pyroclastic volcanic rocks. Gravity gradient analysis determines the geological structures, such as faults or fractures, used as conduits to create Kura Hot Springs. The model provides useful insights into geological occurrences, such as faults, rock layers, and reservoir accumulations that support geothermal fluids. Based on the present study’s findings, we provide recommendations for developing and managing geothermal resources in the Maritaing area.