Geological Variables Research Articles

Mapping the spatial transferability of knowledge-guided machine learning: Application to the prediction of drain flow fraction.

Machine learning (ML) methods continue to gain traction in hydrological sciences for predicting variables at large scales. Yet, the spatial transferability of these ML methods remains a critical yet underexamined aspect. We present a metamodel approach to obtain large-scale estimates of drain fraction at 10m spatial resolution, using a ML algorithm (Gradient Boost Decision Tree). Our variable of interest is drain, as artificial drainage of agricultural land is widespread in areas with high groundwater tables. Drainage has significant effects on the hydrological cycle, and impacts groundwater recharge, streamflow partitioning and nutrient transport. Drain flow is controlled by small-scale variations in topography, geology and groundwater depth, which presents challenges to its estimation at large scale. Drain fraction is the average ratio between drain flow and precipitation. The metamodel combines covariates based on topography, land use and geology with simulated drain fraction from 45 field-scalephysically-based hydrological models of Danish drain catchments. The 45 models were jointly calibrated against timeseries of drain flow observations. The metamodel was used to upscale predictions of drain fractions for the entirety of Danish agricultural land. This involved considerable extrapolation beyond the 45 drain catchments used for training, calling for an assessment of spatial transferability. To map transferability of the model, and distinguish areas where metamodel results are reliable or not, we used the concept of area of applicability (AOA). The AOA is determined from the similarity of covariate space covered by the training data compared to each prediction point, assuming a correlation between model performance and covariate similarity. AOA mapping showed 71% of Denmark's agricultural land falling within the AOA of the metamodel. The study presents a stepwise methodology to obtain national-scale results using a ML model trained on local-scale numeric models and an evaluation of its spatial transferability, highly relevant for decision-support purposes.

Southern California basin and non-basin classification algorithm for ground-motion site amplification model applications

In ground-motion modeling, the estimated level of ground shaking at any given location for an expected earthquake scenario depends on the contributions from the source component (type of fault mechanism and size of the fault slip), the path component (distance between the source and site of interest, and the geologic characteristics of that region), and the site component (the local geology at the site of interest). Each component captures some level of variability and uncertainty in the overall ground-motion estimate. In particular, the site component represents the potential amplification (or de-amplification) of the seismic waves that may lead to magnified and prolonged ground shaking at any given location. This feature is referred to as site effects and in current ground-motion models (GMMs) is dependent on the time-averaged shear wave velocity in the upper 30 m of the earth’s crust ( Vs30) and the depth to a particular shear wave velocity iso-surface (“basin depth,” zx). The latter is responsible for determining the contributions of basin effects, which is additional ground-motion amplification due to three-dimensional effects such as trapped seismic waves that lead to surface wave generation. However, an evaluation of the relationship between zx and basin locations reveals cases of misclassification that is a result of geologic variability (i.e. zx is not sufficient in differentiating basins from non-basins). The study performed in this article proposes a resolution in the form of a statistical classification model that determines the probability of a location residing within or outside a basin based on simple geologic features such as ground surface texture.

The Role of Geological Information’s in The Development of Geopark Area in South Sulawesi, Indonesia

Abstract A geopark is a single geographical area with geological, biological, and cultural diversity. The purpose of geopark is to promote the earth’s heritage and prosper its people. Geoparks must have geological heritage sites and valuable landscapes related to geological, biological, and cultural diversity. Geological diversity is the backbone of geoheritage, geoconservation, and modern society. Geological diversity has a value of existence (regardless of its use), cultural, aesthetic, economic, functional, scientific, and educational values. The United Nations Educational Scientific and Culture Organization (UNESCO) has included the Maros Pangkep Geopark area in South Sulawesi as part of the UNESCO Global Park (UGG) in 2022. This has had a positive impact on several areas in South Sulawesi to create new geopark areas that have geological variations. Therefore, this paper provides information on the role of geological information in the development of geopark areas in the South Sulawesi Province. The research method used is field survey, data analysis through geological assessment and geosite as a source of geoheritage information. It is hoped that this research will provide geological heritage information towards a new geopark area after the Maros Pangkep Geopark in South Sulawesi.

New insights into Holocene dust activity in eastern Uzbekistan

Central Asia is a substantial source of long-range-transported dust, yet the historical and geological variability of dust activity in this region remains poorly understood. This study presents a Holocene record of dust activity from a 6.2-m loess section located near Tashkent in the westerlies-dominated region of eastern Uzbekistan, Central Asia. Utilizing the quartz optically stimulated luminescence dating protocol, we employed grain-size analysis and trace-element geochemistry to reconstruct Holocene dust activity. Dating indicated that this section was deposited over the last 9.6 ka. Four grain-size end-member (EM) components were identified, each representing different atmospheric circulation patterns and sedimentary environments. End-member 2, with a modal size of 11.2 μm, likely represents particles transported by upper-level westerlies, while EM 3, with a modal size of 28.3 μm, is associated with near-surface winds linked to dust storms. Zirconium is concentrated in coarse particles, whereas Rb is enriched in finer particles during dust deposition. Therefore, higher Zr/Rb ratios indicate stronger or more distant dust transport; hence, the Zr/Rb ratio is a reliable indicator of dust activity. Holocene dust activity was reconstructed using the EM 3 component and Zr/Rb ratio, revealing several extreme dust-storm events. During the early–middle Holocene (9.6–5 ka), dust activity was stronger but less frequent compared with the subsequent shift to lower intensity but higher frequency dust events. The long-term orbital-scale decline in Holocene dust activity can be attributed to reduced solar insolation and weakening of the Siberian High since the early Holocene. On a centennial to millennial scale, extreme dust-storm events are teleconnected with cold ice-rafted debris events in the North Atlantic. Projections for the coming century suggest that dust activity in eastern Uzbekistan may further decline, accompanied by an increase in precipitation. This study provides new insights into understanding and predicting dust storms in Central Asia.

Subsurface microbial community structure shifts along the geological features of the Central American Volcanic Arc.

Subduction of the Cocos and Nazca oceanic plates beneath the Caribbean plate drives the upward movement of deep fluids enriched in carbon, nitrogen, sulfur, and iron along the Central American Volcanic Arc (CAVA). These compounds fuel diverse subsurface microbial communities that in turn alter the distribution, redox state, and isotopic composition of these compounds. Microbial community structure and functions vary according to deep fluid delivery across the arc, but less is known about how microbial communities differ along the axis of a convergent margin as geological features (e.g., extent of volcanism and subduction geometry) shift. Here, we investigate changes in bacterial 16S rRNA gene amplicons and geochemical analysis of deeply-sourced seeps along the southern CAVA, where subduction of the Cocos Ridge alters the geological setting. We find shifts in community composition along the convergent margin, with communities in similar geological settings clustering together independently of the proximity of sample sites. Microbial community composition correlates with geological variables such as host rock type, maturity of hydrothermal fluid and slab depth along different segments of the CAVA. This reveals tight coupling between deep Earth processes and subsurface microbial activity, controlling community distribution, structure and composition along a convergent margin.

Controls on regional sulphate distribution in shallow groundwater in the western Canadian Interior Plains

Sulphate (SO4), predominantly derived from sulphur (S)-bearing glacial sediments distributed widely across the Canadian Interior Plains, contributes to high groundwater salinity and can be detrimental to riparian and dry-land ecosystems, agricultural production, and water use. While previous researchers investigated SO4 distribution and dynamics in shallow groundwater at local scales (<1500 km2), we examine SO4 occurrence in groundwater at larger scales, and to depths of ∼150 m, considering variations in geology, glacial history, climate, and geochemical and hydrogeological settings in the Canadian province of Alberta. Sulphate concentrations in groundwater vary considerably, with 15 % of 139,130 samples above the 500 mg/L Canadian drinking water aesthetic objective. Analysis of δ34SSO4 and δ18OSO4 from 179 wells throughout Alberta shows SO4 is dominantly derived from oxidation of S-bearing material. At this large scale, marine shale bedrock subcrops, glacial ice flow directions, and climate control SO4 distribution. Groundwater contains less SO4 (<200 mg/L) in western Alberta compared to the east due to a lack of S-bearing bedrock, higher groundwater recharge rates, thinner glacial sediments, and increased groundwater circulation. Focusing on a region in south-central Alberta (Red Deer area), hydrogeological characteristics relating to SO4 formation, recharge, permeability, hydraulic gradient, and travel time are included in a principal component analysis to identify six groundwater groups at varying stages of evolution and SO4 concentrations depending on their hydrogeological setting. Low SO4 concentrations are associated with areas of high recharge, where SO4 has been leached from the sediments, or with more evolved bedrock groundwater where SO4 has been reduced or was recharged in pre-glacial times. High SO4 concentrations are found in areas of lower recharge and permeability, where flushing occurs more slowly. Combining the Red Deer area and province-wide analyses, we identify seven regions across Alberta with characteristic distribution and controls on SO4 occurrence in shallow groundwater.

Multi-Task Learning Network-Based Prediction of Hydraulic Fracturing Effects in Horizontal Wells Within the Ordos Yanchang Formation Tight Reservoir

Accurate prediction of fracture volume and morphology in horizontal wells is essential for optimizing reservoir development. Traditional methods struggle to capture the intricate relationships between fracturing effects, geological variables, and operational factors, leading to reduced prediction accuracy. To address these limitations, this paper introduces a multi-task prediction model designed to forecast fracturing outcomes. The model is based on a comprehensive dataset derived from fracturing simulations within the Long 4 + 5 and Long 6 reservoirs, incorporating both operational and geological factors. Pearson correlation analysis was conducted to assess the relationships between these factors, ranking them according to their influence on fracturing performance. The results reveal that operational variables predominantly affect Stimulated Reservoir Volume (SRV), while geological variables exert a stronger influence on fracture morphology. Key operational parameters impacting fracturing performance include fracturing fluid volume, total fluid volume, pre-fluid volume, construction displacement, fracturing fluid viscosity, and sand ratio. Geological factors affecting fracture morphology include vertical stress, minimum horizontal principal stress, maximum horizontal principal stress, and layer thickness. A multi-task prediction model was developed using random forest (RF) and particle swarm optimization (PSO) methodologies. The model independently predicts SRV and fracture morphology, achieving an R2 value of 0.981 for fracture volume predictions, with an average error reduced to 1.644%. Additionally, the model’s fracture morphology classification accuracy reaches 93.36%, outperforming alternative models and demonstrating strong predictive capabilities. This model offers a valuable tool for improving the precision of fracturing effect predictions, making it a critical asset for reservoir development optimization.

Gas exposure trials of the Hall Formation, Lehen Field, Austria: Laboratory simulations of gas-caprock-brine interactions in an underground hydrogen storage system

In underground hydrogen storage (UHS) systems a reliable caprock is required to contain the stored hydrogen and prevent it from migrating out of the reservoir formation. Potential chemical reactions, which may occur upon the introduction of hydrogen into the subsurface, may change the sealing capacity of the caprock. To assess the risk of gas-caprock-brine interactions, gas exposure experiments using caprock and brine taken from an UHS site (Lehen Field, Austria) were undertaken at 50 °C and 150 bar pressure for treatment intervals of 3 months, 6 months and 12 months. Three different gas treatments were used: argon, as a non-reactive, experimental control, hydrogen and a gas mix of methane, hydrogen and carbon dioxide. Post-treatment scanning electron microscope imaging shows no significant dissolution features in the samples and the hydrochemistry before and after treatment do not indicate chemical changes attributable to reactions with hydrogen. The variations measured in porosity and mineralogy are interpreted to be due to geologic variation rather than resulting from chemical reactions in the experiments. These results confirm that large scale dissolution should not be expected in the caprock under the conditions of this UHS setting.

Hydrochemical characterization and health risk assessment of shallow groundwater in a northern coalfield of Anhui Province, China

In a global context, the hydrochemical characteristics of shallow groundwater in coalfields exhibit high degrees of diversity and complexity that are rooted in the intricate interplay of geological variations, diverse climatic conditions, and extensive human activities. The specific types and concentrations of ions, such as Ca2+, Cl−, and NO3−, show stark differences across geographical regions. Given the crucial roles of coalfields as energy suppliers, the potential environmental contamination risks posed by mining activities to groundwater cannot be overlooked as such pollution directly impacts human health and ecological safety. This study focuses on the Huainan coal mining area in northern Anhui Province (China), where shallow groundwater samples were systematically collected and analyzed to determine the hydrochemical characteristics and ascertain the water quality status. By integrating hydrogeochemical analysis techniques with inverse modeling methods, it was revealed that the groundwater in this region is predominantly HCO3-Ca type, exhibiting weak alkaline characteristics. The formation mechanisms are primarily governed by silicate rock weathering and mineral dissolution–precipitation processes, albeit with discernible influences from human activities. PHREEQC simulations were used to further confirm the precipitation tendencies of minerals like calcite, dolomite, and fluorite as well as the significant dissolution characteristics of halite. The inverse modeling pathway analysis reveals specific hydrochemical processes along different paths: paths I and IV are notably dominated by Ca2+ dissolution–precipitation and cation exchange–adsorption processes, whereas paths II and III are closely associated with the precipitation of calcium montmorillonite as well as dissolution of kaolinite, calcite, quartz, and mineral incongruents. Moreover, evaluations based on the entropy-weighted water quality index (EWQI) indicated overall positive trends of the groundwater quality indicators within the Huainan mining area, reflecting the effectiveness of regional water quality management efforts and providing a scientific basis for future water quality protection and improvement strategies. In summary, this study not only deepens our understanding of the groundwater chemistry in the Huainan coal mining area but also underscores the importance of scientifically assessing and managing groundwater resources to address the environmental challenges potentially arising from coal mining activities.

Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model’s loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach.

Mineral Signatures in Oued K’sob River (Algeria): Highlighting Geological Variations and Potential Anthropogenic Impacts

This study examines the mineral composition of sediment samples collected from five distinct zones along the Oued K’sob River in Bordj Bou Arreridj, Algeria using scanning electron microscopy (SEM). The analysis revealed intriguing patterns, the Zone 5 emerged as a distinct outlier, exhibiting significantly lower calcium (29.32% vs. 53.25% in Zone 1) and iron (14.94% vs. 6.31% in Zone 1) content compared to other zones, while displaying a higher silicon content (28.82% vs. 25.27% in Zone 1). This suggests a unique geological origin or specific weathering processes shaping the mineral fingerprint of Zone 5. Interestingly, Zones 1, 2, and 3 displayed a relative consistency in silicon content (around 25%, with pairwise comparisons exceeding p &gt; 0.05), suggesting potential similarities in mineral sources or enrichment mechanisms within their watersheds. However, Zone 4 deviated slightly, showing a higher calcium content (64.18% vs. 29.32% in Zone 5) and lower silicon content (17.99% vs. 28.82% in Zone 5), indicating potential differences in its geological makeup or weathering processes. Furthermore, all zones exhibited trace amounts of elements like lead (Pb) and cadmium (Cd) (ranging from 0.76% to 2.16% for Pb and 0.000% to 0.114% for Cd) (Please recheck these values They are very high). This warrants further investigation to understand their potential anthropogenic origins and impact on the river ecosystem. Further investigations incorporating detailed geological surveys, isotopic analyses, and expanded sampling strategies are recommended to gain a more comprehensive understanding of the complex factors shaping the sediment chemistry within the Oued K’sob River system.

A slope scaling heuristic for the multi-period strategic planning of carbon capture and storage

Around the world, efforts are currently underway to implement various decarbonization strategies to meet net-zero emissions objectives. This includes carbon capture and storage (CCS), which involves capturing CO2 at emitter sites, and transporting it to geological reservoirs, where it is to be injected underground for long-term storage. In this work, we focus on the multi-period strategic planning of a CCS value chain involving pipeline CO2 transportation. From an Operations Research standpoint, this problem exhibits the characteristics of combined facility location and network design. To account for multiple scenarios of input parameters (e.g. market and geological variability), this problem has to be solved hundreds or thousands of times. Thus, reaching high-quality solutions quickly is crucial. As commercial solvers struggle to provide high-quality solutions under these time constraints, we propose a slope scaling heuristic based on previous work on single-period CCS planning and network design. This new heuristic approximates the cost of design variables, generates upper bounds via dynamic programming, uses a long-term memory search strategy, and includes a final improvement phase where a restricted model is solved. Computational experiments show that the proposed heuristic generates better solutions than CPLEX for most instances considered, at a fraction of the computational time.

Variability in interseismic strain accumulation rate and style along the Altyn Tagh Fault

Major strike-slip faults that develop between strong and weaker regions are thought to focus along narrow shear zones at the rheological boundary. Here we present the InSAR-derived velocity field spanning almost the entire length of one such fault, the 1600 km-long Altyn Tagh Fault (ATF), and analyse the strain distribution. We find that localisation of strain is actually variable, in contrast to other major strike-slip faults that show little variation, with strain concentrated at the fault for some sections and distributed over broad (>100 km) shear zones for others. Slip rate along the ATF is also variable, decreasing along the fault from 11.6 ± 1.6 mm/yr in the west to 7.2 ± 1.4 mm/yr in the central portion, before increasing again to 11.7 ± 0.9 mm/yr over the eastern portion. We show that the variable shear zone width may be linked to geological variability and the influence of heat flow, and the results imply that sub-parallel faults play an important role in the overall deformation field. This demonstrates the significance of accurately characterising strain rates over a broad region when assessing seismic hazard.

Impact of Environmental Conditions on Soil Geochemistry in Southern Kazakhstan

This study investigated the elemental composition of soils in Kyzylorda and Turkestan (southern Kazakhstan), an area rich in natural resources but facing potential environmental threats from industry and agriculture. The goal was to establish baseline geochemical values and assess soil contamination risks. Soil samples were collected from across the region and analyzed using ICP-MS and INAA techniques, providing a comprehensive profile of 72 elements. Statistical analysis revealed significant variations in elemental concentrations, with enrichments observed for specific elements when compared with reference values. Notably, both regions shared a core set of elements including rare earth elements (yttrium series: holmium, erbium, thulium), noble metals (gold, platinum, ruthenium, palladium), and tungsten. Enrichment patterns, however, provided distinct insights. Rare earth element enrichments likely reflect the region’s geology, while elevated radioactive elements necessitate further investigation to understand potential environmental and health risks. Enrichment of iron group elements might be linked to a combination of geological factors and anthropogenic activities like mining or industrial processes. A significantly higher number of elements exceeded background levels in Kyzylorda compared with Turkestan, suggesting greater element accumulation in Kyzylorda’s soil. This difference could be attributed to variations in regional geology or historical anthropogenic activities. The established geochemical baseline for 72 elements and the identified areas of potential contamination will inform land management practices, guide future environmental monitoring efforts, and ultimately contribute to the safeguarding of public health in southern Kazakhstan.

Integrated Assessment of Bearing Capacity and GHG Emissions for Foundation Treatment Piles Considering Stratum Variability

Foundation treatment piles are crucial for enhancing the bearing capacity and stability of weak foundations and are widely utilized in construction projects. However, owing to the complexity of geological conditions, traditional construction methods fail to meet the demand for low-carbon development. To address these challenges, this study introduced a comprehensive decision-making approach that considers the impact of stratum variability on greenhouse gas (GHG) emissions and pile bearing capacity from the design phase. During the design process, the GHG emissions and bearing capacities of deep cement mixing (DCM) and high-pressure jet grouting (HPJG) piles were quantitatively assessed by analyzing the environmental and performance impacts of foundation treatment piles related to materials, transportation, and equipment usage. The results suggest that the bearing capacity of piles in shallow strata is highly susceptible to stratum variability. Using piles with a diameter of 800 mm and a length of 20 m as an example, compared with DCM piles, HPJG piles demonstrated a superior bearing capacity; however, their total GHG emissions were 6.58% higher, primarily because of the extensive use of machinery during HPJG pile construction. The GHG emissions of foundation treatment piles in shallow strata were influenced more by geological variability than those in deep strata. Sensitivity analysis revealed that the pile diameter is a critical determinant of GHG emissions and bearing capacity. Based on the bearing capacity–GHG emission optimization framework, a foundation treatment strategy that integrates overlapping and spaced pile arrangements was introduced. This innovative construction method reduced the total GHG emissions by 22.7% compared with conventional methods. These research findings contribute to low-carbon design in the construction industry.

A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

Assessment of α and γ parameters in landslide volume estimation equations based on geological variability

Assessment of α and γ parameters in landslide volume estimation equations based on geological variability

Regression analysis and its application to oil and gas exploration: A case study of hydrocarbon loss recovery and porosity prediction, China

In oil and gas exploration, elucidating the complex interdependencies among geological variables is paramount. Our study introduces the application of sophisticated regression analysis method at the forefront, aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging. Through a rigorous assessment, we explore the efficacy of eight regression models, bifurcated into linear and nonlinear groups, to accommodate the multifaceted nature of geological datasets. Our linear model suite encompasses the Standard Equation, Ridge Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net, each presenting distinct advantages. The Standard Equation serves as a foundational benchmark, whereas Ridge Regression implements penalty terms to counteract overfitting, thus bolstering model robustness in the presence of multicollinearity. The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models, enhancing their interpretability, while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator, offering a harmonized solution to model complexity and comprehensibility. On the nonlinear front, Gradient Descent, Kernel Ridge Regression, Support Vector Regression, and Piecewise Function-Fitting methods introduce innovative approaches. Gradient Descent assures computational efficiency in optimizing solutions, Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns, and Support Vector Regression is proficient in forecasting extremities, pivotal for exploration risk assessment. The Piecewise Function-Fitting approach, tailored for geological data, facilitates adaptable modeling of variable interrelations, accommodating abrupt data trend shifts. Our analysis identifies Ridge Regression, particularly when augmented by Piecewise Function-Fitting, as superior in recouping hydrocarbon losses, and underscoring its utility in resource quantification refinement. Meanwhile, Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A, evidencing its aptness for intricate geological structures. This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector. The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction, evaluation, and recovery.

The effects of clays on bacterial community composition during arthropod decay

Fossilization, or the transition of an organism from the biosphere to the geosphere, is a complex mechanism involving numerous biological and geological variables. Bacteria are one of the most significant biotic players to decompose organic matter in natural environments, early on during fossilization. However, bacterial processes are difficult to characterize as many different abiotic conditions can influence bacterial efficiency in degrading tissues. One potentially important variable is the composition and nature of the sediment on which a carcass is deposited after death. We experimentally examined this by decaying the marine shrimp Palaemon varians underwater on three different clay sediments. Samples were then analyzed using 16S ribosomal RNA sequencing to identify the bacterial communities associated with each clay system. Results show that samples decaying on the surface of kaolinite have a lower bacterial diversity than those decaying on the surface of bentonite and montmorillonite, which could explain the limited decay of carcasses deposited on this clay. However, this is not the only role played by kaolinite, as a greater proportion of gram-negative over gram-positive bacteria is observed in this system. Gram-positive bacteria are generally thought to be more efficient at recycling complex polysaccharides such as those forming the body walls of arthropods. This is the first experimental evidence of sediments shaping an entire bacterial community. Such interaction between sediments and bacteria might have contributed to arthropods’ exquisite preservation and prevalence in kaolinite-rich Lagerstätten of the Cambrian Explosion.

Assessing foundation characteristics at the war dam site, lake tana basin, Ethiopia: A geophysical and geotechnical perspective

An integrated geophysical and geotechnical study evaluated the foundation conditions at the War dam site in northwest Ethiopia. This investigation included the classification of rock quality, shallow seismic refraction, and magnetic approaches. The dam's location comprises quaternary soil deposits and rhyolite rock units that have undergone varied weathering and fracturing. The shallow seismic refraction method distinguishes three layers of p-wave velocities that are less than 1.5 km per second with a depth range of 2–6 m, 1.5–2.5 km per second at a depth range of 15–20 m, and 2.5–3.5 km per second ranging from 20 to 40 m, respectively. Magnetic data were used to identify lineaments, and the RQD value acquired from boreholes ranged from extremely poor to excellent. Lineaments were recognized using the tilt angle approach. The results of the permeability tests demonstrated that the rock mass that serves as the dam's foundation had characteristics that are resistant to low permeability. The maximum and minimum lugeon values obtained from the testing were 9Lu and 0.81Lu, respectively. There are weak zones at and below the surface of the dam site, according to the overall findings acquired from seismic refraction, magnetic, and discontinuity surveying. These results were obtained from monitoring the dam site. These significant structures are directed towards a SW-NE, NE-SW, NNW-SSE, and SSW-NNE orientation. The study assessed the geological suitability of a proposed dam site using seismic refraction and magnetic survey methods. Significant geological variations were observed, particularly in the right abutment and valley floor, indicating the need for targeted grouting. The findings suggest that while the site is generally suitable for dam construction, specific areas require further ground improvement to ensure stability.