Assessment of a Multi-Layer Aquifer Vulnerability Using a Multi-Parameter Decision-Making Method in Mosha Plain, Iran

In recent decades, there has been a growing emphasis on assessing aquifer vulnerability. Given the availability of spatial data and the GIS advantages, mapping the groundwater vulnerability has become a common tool for protecting and managing groundwater resources. Here, we applied the GIS indexing and an overlay method to explore a combination of the potential contamination factors needed to assess groundwater vulnerability in the Mosha aquifer. The data from a borehole data logger and chemical analysis of spring water show groundwater responses to the surface contaminating sources. To assess the aquifer vulnerability, the potential contaminating sources were classified into three groups, namely (1) geological characteristics such as lithology and structural geology features; (2) the infrastructures induced by human activities such as roads, water wells, and pit latrines; and (3) land use. By considering these components, the risk maps were produced. Our findings indicate that the aquifer is very responsive to the anthropogenic contaminants that may leak into the aquifer from urbanized areas. Additionally, roads and pit latrines can significantly release pollutants into the environment that may eventually leak into the aquifer and contaminate the underlying groundwater resources

Small Object Detection and Tracking: A Comprehensive Review

Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability

The impact of pore-throat shape evolution during dissolution on carbonate rock permeability: pore network modelling and experiments

Pore network model simulation (PNM) is an important method to simulate reactive transport processes in porous media and to investigate constitutive relationships between permeability and porosity that can be implemented in continuum-scale reactive-transport modeling. The existing reactive transport pore network models (rtPNMs) assume that the initially cylindrical pore throats maintain their shape and pore throat conductance is updated using a form of Hagen-Poiseuille relation. However, in the context of calcite dissolution, earlier studies have shown that during dissolution, pore throats can attain a spectrum of shapes, depending upon the imposed reactive-flow conditions (Agrawal et al., 2020). In the current study, we derived new constitutive relations for the calculation of conductance as a function of pore throat volume and shape evolution for a range of imposed flow and reaction conditions. These relations were used to build animproved new reactive pore network model (nrtPNM). Using the new model, the porosity-permeability changes were simulated and compared against the existing pore network models. In order to validate the reactive transport pore network model, we conducted two sets of flow-through experiments on two Ketton limestone samples. Acidic solutions (pH 3.0) were injected at two Darcy velocities i.e., 7.3 x 10(-4) and 1.5 x 10(-4) m.s(-1) while performing X-ray micro-CT scanning. Experimental values of the changes in sample permeability were estimated in two independent ways: through PNM flow simulation and through Direct Numerical Simulation. Both approaches used images of the samples from the beginning and the end of experiments. Extracted pore networks, obtained from the micro-CT images of the sample from the beginning of the experiment, were used for reactive transport PNMs (rtPNM and nrtPNM). We observed that for the experimental conditions, most of the pore throats maintained the initially prescribed cylindrical shape such that both rtPNM and nrtPNM showed a similar evolution of porosity and permeability. This was found to be in reasonable agreement with the porosity and permeability changes observed in the experiment. Next, we have applied a range of flow and reaction regimes to compare permeability evolutions between rtPNM and nrtPNM. We found that for certain dissolution regimes, neglecting the evolution of the pore throat shape in the pore network can lead to an overestimation of up to 27% in the predicted permeability values and an overestimation of over 50% in the fitted exponent for the porosity-permeability relations. In summary, this study showed that while under high flow rate conditions the rtPNM model is accurate enough, it overestimates permeability under lower flow rates

Investigation of strain localization in sheared granular layers using 3-D discrete element modeling

In this work, we investigate slip localization in sheared granular faults at seismic velocities using 3-D numerical simulations with the discrete element method (DEM). An aggregate of non-destructive spherical particles is subjected to direct shear by using two moving boundaries in a sandwich configuration to identify the impact of particle-scale parameters on slip localization. We impose a thin layer of fine-grained particles with variable contrast in thickness and grain size adjacent to the boundary as well as in the middle of the granular layer to simulate boundary and Y shears observed in both natural and laboratory fault gouges. The results show that larger amounts of strain is accommodated within the pre-described finer-grained layer even with a small (< 10%) contrast in grain size. Up to 90% of the displacement is localized in a finer-grained layer when the contrast ratio of the grain size is 50%. Based on the concept of the average spreading velocity of particles and squeeze expulsion theory in granular flow, we suggest that the phenomenon of localization is likely from result from the contribution of larger grains collisions with smaller grains. Since the amount of frictional heat generated depends on the degree of localization, the results provide crucial information on the heat generation and associated slip accommodation in sheared gouge zones. We conclude that the occurrence of a weaker, fine-grained layer within a dense fault zone is likely to result in self-enhanced weakening of the fault planes

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater

Review on pore-network modeling studies of gas-condensate flow: Pore structure, mechanisms, and implementations

Gas-condensate flow is a critical process in the near-well region where the well production efficiency is strongly affected by the production of condensate dropout. Pore-scale simulations have provided an understanding of the underlying processes such as snap-off and the effect of the interplay between viscous and capillary forces on gas-condensate flow and its induced blockage within the pore spaces. Among various modeling approaches used to explore these phenomena, pore-network modeling, due to its computational efficiency and the ability to simulate relatively large sample sizes, has appealed to researchers. This article presents a review of the development of pore-network models to simulate gas-condensate flow, particularly in the near wellbore regions. This contribution reviews pore-scale mechanisms that should be included in simulating the gas-condensate flow, together with the involved processes and the peculiarities pertinent to such modeling efforts. After a brief review of different pore scale studies and their differences, advantages, and disadvantages, the review focuses on pore-network modeling, and the application of pore-network modeling in gas-condensate flow in the recent studies. The employed methodologies, highlights, and limitations of each pore network study are examined and critically discussed. The review addresses pore-space evolution, flow mechanisms, and the involved flow and transport parameters. The formulations of capillary entry pressure in different pore geometries, the corresponding conductance terms, snap-off criteria, and conditions for the creation of condensate bridging in different pore structures are presented. Additionally, three major approaches used in pore-network modeling of gas condensation, namely quasi-static, dynamic methods and dynamic compositional pore-network modeling, are presented and their main governing equations are provided using various tables. Finally, the significance of gas-condensate flow modeling including its modeling challenges together with the main similarities and differences among pore-network studies are provided

Application of the modified Q-slope classification system for sedimentary rock slope stability assessment in Iran

Abstract(#br)The Q-slope system is an empirical method for discontinuous rock slope engineering classification and assessment. It has been introduced recently to provide an initial prediction of rock slope stability assessment by applying simple assumptions which tend to reflect different failure mechanisms. This study offers a correlation relationship between Q-slope and slope stability degree using case studies of sedimentary rock slopes from 10 regions of Iran. To this end, we have investigated 200 areas from these regions, gathered the necessary geotechnical data, have classified the slopes from a Q-slope perspective, and have estimated their stability relationships. Based on artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, Decision tree, Random-forest, Multilayer perceptron, AdaBoost, Naive Bayes and Quadratic discriminant analysis, the relationships and classifications were implemented and revised in the Python high-level programming language. According to the results of the controlled learning models, the Q-slope equation for Iran has indicated that the stability-instability class distributions are limited to two linear states. These limits refer to the B-Line (lower limit) as

A quantitative study of salinity effect on water diffusion in n-alkane phases: From pore-scale experiments to molecular dynamic simulation

Numerous mechanisms have been proposed to untangle the effect of a low concentration of dissolved salts in the water flooding medium. One potential mechanism for enhanced oil movement is proposed with osmosis effect, however, the process of water transport through the oil phase, due to a salinity contrast, is not fully understood. In our study, we used three aqueous solutions and two alkanes in a series of microfluidic experiments with hydrophobically coated glass micro-chips for mimicking the low-salinity waterflooding process in an oil-wet rock formation. We created multiple systems of low-salinity water-alkane/high-salinity water in the porous micromodel, and afterward, continuously monitored the domain for 70 h. We noted that ionic strength and the hydrocarbon chain length both played important roles in water diffusion. A salinity contrast of 1.7 g/L-170 g/L caused a higher water volumetric flux than 50 g/L-170 g/L for both alkanes. The difference in water volumetric fluxes for those two contrasts were not proportional to the salinity contrast during the experimental period. There was no simple relationship between the chain length of hydrocarbon and water volumetric flux. Moreover, to investigate the effect of salinity on water behavior in heptane, we conducted molecular dynamic (MD) simulations by considering three different concentrations in the high-salinity water region featuring our experiments. The results indicated that high salinity limited the water diffusion from high-salinity phase into the oil phase and reduced the possibility of water entering the heptane phase. Therefore, the net flux of water from the pure water side to the salty waterside was enhanced