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Abstract This study explores the feasibility of utilizing bedded salt deposits as sites for underground hydrogen storage. We introduce an innovative artificial intelligence framework that applies multi-criteria decision-making and spatial data analysis to identify the most suitable locations for storing hydrogen in salt caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms—KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, and MLR—creating rock salt deposit suitability maps for hydrogen storage. The performance of these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Correlation Coefficient (R2), compared against an actual dataset. The CatBoost model demonstrated exceptional performance, achieving an R2 of 0.88, MSE of 0.0816, MAE of 0.1994, RMSE of 0.2833, and MAPE of 0.0163. The novel methodology, leveraging advanced machine learning techniques, offers a unique perspective in assessing the potential of underground hydrogen storage. This approach is a valuable asset for various stakeholders, including government bodies, geological services, renewable energy facilities, and the chemical/petrochemical industry, aiding them in identifying optimal locations for hydrogen storage.

Through the examination of calcite twins, this research outlines the tectonic development and paleo stress patterns of the Paleozoic Routshon complex situated in the southeastern segment of the Sanandaj–Sirjan zone, a hinterland region of the Zagros orogeny in southeastern Iran. The study of orogenic phase indicates that the deformation event affecting the southern sector of the Sanandaj-Sirjan zone aligns with the Cimmerian orogenic phase of the Late Triassic period. A variety of structural features at both map and outcrop scales highlight the importance of slip partitioning in the structural evolution of this region, driven by inclined transpression. Observations suggest that the deformation related to contractional components includes steeply to moderately plunging folds, dip-slip domain deformation primarily involving thrusts, and ongoing deformation by strike-slip component motion, which results in thrust-related ductile shear zones. The analysis of calcite c-axis fabrics from mylonite samples obtained from these shear zones indicates a low-temperature monoclinic pattern of non-coaxial deformation. This deformation type underscores the impact of the strike-slip component in the development of progressive simple shear within thrust-related shear zones in this segment of the Sanandaj-Sirjan zone. Dynamic analysis of c-axis fabric data reveals a NE-SW orientation for the principal compressive axes (σ1) in this area. This direction, corroborated by additional data such as fault surface, GPS, and earthquake focal mechanism data, confirms that the orientation of the compressive axes (σ1) has remained consistent from the Late Triassic to the present.

Rhombic structures have been observed in the Qom-Zefreh-Nayin-Dehsheir-Baft region, specifically along the direction of the dextral faults, which have caused significant changes in strike length. This study investigates the geological features and fault interactions in the region through the examination of aerial images, fault-lithology correlations, petrology, crustal thickness, and seismic studies. The analysis of aerial photos and geological correlations revealed the presence of ophiolites and pluto-volcanics associated with faults and rhombic structures. By conducting field geology and combining various geological studies, a pull-apart basin was identified in the area, contributing to the formation of three rhombic structures. This basin played a crucial role in the genesis of the region’s ophiolites and pluto-volcanics. The research suggests that the initial tensional stress leading to the pull-apart basin was caused by the right step of a dextral fault within the Urumieh-Dokhtar magmatic arc. This fault formation occurred due to the oblique Arabian subduction towards the Iranian plateau. During the Zagros orogeny, the stretched area persisted, leading to the formation of oceanic crust in this location. The subduction angle changes from subduction to super-subduction, resulting in the classification of the region into two types: C and E genes. Different types of magma, including alkaline, subalkaline, shoshonite, calcalkaline, and adakitic, were identified in this region. The study highlights the significance of tholeiitic arcs, abyssal features, crust thickness, and seismicity in understanding oblique diagonal subduction models and tensional pull-apart basins, which are crucial in the transition from subduction to super-subduction. This research offers valuable insights into the geological complexities of the region and opens up opportunities for further exploration of similar models.

This study investigates the variations in stress, strain, and deformation of the Earth’s crust in Iran arising from tectonic movements and seismic activities. We employed the Kostrov and Molnar methods to quantify these parameters, focusing on the influence of different zoning techniques on the estimations. Analyzing data from 637 earthquakes (moment magnitudes > 5.5) spanning 1909 to 2016, we determined the directions of maximum pressure, tension, and seismic strain through two primary approaches: comprehensive zoning and individual earthquake analysis. Additionally, we assess horizontal shortening and vertical crustal adjustments. Our methodology involves three distinct strategies: individual earthquake analysis, 1° × 1° zoning, and tectonic zoning. The findings demonstrate that the choice of zoning method significantly affects the direction and magnitude of seismic strain estimations. Although both methods identified significant deformations in the Dasht Bayaz and Qaen regions of Eastern Iran, differences between the Kostrov and Molnar methods in estimating seismic strain are observed. The high Zagros region shows signs of crustal thickening, whereas the Zagros foreland exhibits crustal thinning. Intriguingly, Eastern Alborz indicates uplift, and Western Alborz suggests subsidence, offering an alternative view to the conventional tectonic understanding of the Alborz range. These results highlight the critical role of zoning in stress analyses and the disparities between widely used estimation techniques. They underscore the necessity of careful method selection and interpretation in geodynamic studies, particularly in seismically active regions like Iran.

Over recent decades, natural and artificial colloids, as well as nanoparticles, have been increasingly used in various applications. Consequently, with this rising consumption, surface and subsurface environments are more exposed to these particles. The presence of these particles and the colloid-facilitated transport of microorganisms, the interactions between dissolved contaminants and mobile colloids in porous media, and the fate and transport of colloids through groundwater—one of the primary sources of water supply for human societies—have attracted extensive research. This study investigates the performance of several image processing methods in the field of colloid detection, which is a prerequisite for the subsequent steps in porous media research. We employed four different categories of image processing approaches on microscopy images—segmentation-based methods, background-detection-based methods, filter-based methods, and morphology-based methods—to conduct the detection process of colloids. Eight methods were applied and subsequently analyzed in terms of their drawbacks and advantages to determine the best ones in this domain. Finally, we proposed an ensemble approach that leverages the strengths of the three best methods using a majority vote to detect colloids more accurately. In experiments, Precision, Recall, F-measure, and TCR criteria were considered as evaluation tools. Experimental results demonstrate the high accuracy of image processing methods in recognizing colloids. Among all these methods, morphology-based methods were the most successful, achieving the best detection performance and improving the limited distinguishing features of small colloids. Moreover, our ensemble approach, achieving perfect scores across all evaluation criteria, highlights its superiority compared with other detection methods.

This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R2), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.

Energy is a foundational element of the modern industrial economy. Prices of metals play a crucial role in energy sectors’ revenue evaluations, making them the cornerstone of effective payment management employed by resource policymakers. Copper is one of the most important industrial metals, and plays a vital role in various aspects of today’s economies. Copper is strongly associated with many industries, such as electrical wiring, construction, and equipment manufacturing; therefore, the price of copper has become a significant impact factor on the performance of related energy companies and economies. The accurate prediction of copper prices holds particular significance for market participants and policymakers. This study carried out research to address the gap in copper price forecasting using a one-dimensional convolutional neural network (1D-CNN). The proposed method was implemented and tested using extensive data spanning from November 1991 to May 2023. To assess the performance of the CNN model, standard evaluation metrics, such as the R-value, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), were employed. For the prediction of global copper prices, the proposed artificial intelligence algorithm demonstrated high accuracy. Lastly, future global copper prices were predicted up to 2027 by the CNN and compared with forecasts published by the International Monetary Fund and the International Society of Automation. The results show the exceptional performance of the CNN, establishing it as a reliable tool for monitoring copper prices and predicting global copper price volatilities near reality, and as carrying significant implications for policymakers and governments in shaping energy policies and ensuring equitable implementation of energy strategies.

Fault-related folds are present in most tectonic settings and may serve as structural traps for hydrocarbons. Due to their economic importance, many kinematic models present for them. Unfortunately, most of them have predominantly concentrated on the sliding mechanism parallel to the layering and often ignore the integral role of buckling in folding processes. This study is at the forefront of exploring the interplay among, sliding, buckling, and bending in the formation of the three fundamental types of fault-related folds: detachment, fault-propagation, and fault-bend folds. To this end, we developed five sets of two-dimensional (2D) finite element models, embodying both elastic and elastic-plastic behaviors. Our results indicate that sliding parallel to layering and faults, in conjunction with buckling, are the predominant mechanisms in fault-related folding. The strain ellipse patterns in our models are consistent with those observed in buckling models, thus affirming the significance of buckling in these geological structures. Furthermore, our models demonstrate that fault slip diminishes from the periphery towards the center in all three types of fault-related folds, in contrast to interlayer slip, which intensifies from the edge towards the center. In essence, a diminution in fault slip at the center is balanced by an augmentation in interlayer slip, leading to thickening and buckling. The genesis of all three fault-related fold types is attributed to the reduction in fault slip, with their distinctiveness defined by the location of this reduction: at the detachment fault tip for detachment folds, at the ramp tip for fault-propagation folds, and at the upper flat for fault-bend folds.

Abstract Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others.

Climate change has resulted in severe landslides in Maskun, Iran. This study evaluates and monitors the displacement caused by the landslide mass in Bam using Interferometry Synthetic Aperture Radar (DInSAR) and Persistent Scatterer Interferometry (PSI) techniques, as well as identifies relationships between climatic conditions and mass displacement. Temperature and precipitation data from 2007 to 2019 were combined with satellite images and the DInSAR method was used to determine the mass displacement differences after selecting eighteen radar images from the ASAR sensor of the ENVISAT satellite. Additionally, Sentinel 1 satellite images were acquired and analyzed using the PSI method from November 5, 2014, to June 24, 2019. The highest displacement level at the surface of the Maskun landslide mass was then extracted. The ASAR images show a monthly displacement rate of 7.3 mm. The smallest displacement, on the other hand, occurred between May and September 2009, at a rate of 3.1 mm/month. PSI results also revealed that the maximum Line Of Sight (LOS) velocities detected by PSI are −64.5 mm/yr (away from the satellite) and 32.45 mm/yr (toward the satellite). Rainfall is one of the main triggers for increasing the deformation of the Maskun landslide according to the time-series analysis.

Fault-related folds are intriguing geological structures that develop in compressional and extensional regimes. These folds serve as structural traps for hydrocarbon resources, making their numerical models crucial for understanding the stress and strain evolution of hydrocarbon reservoirs. In our research, we utilize the two-dimensional finite element technique to simulate three representative categories of fault-related folds. Our investigation encompasses their geometric transformation over time, the distribution of stress and strain, variations in slip and uplift, and the effects that various mechanical properties have on these gradients. In our study, we uncovered essential findings about the behavior of fault-related folds. We ascertained that the fault slip gradient in the fault-bend fold model is less than in the fault-propagation fold model. Regarding the uplift gradient, the fault-propagation and fault-bend fold models displayed the greatest and the least degree of change, respectively. The trend of stress-strain evolution on the fold surface in all models was consistent, starting with an increase, transitioning to a constant phase, and ending with a decrease. This pattern proved to be more intricate and divergent than what was evident on the fault surface. Importantly, the internal friction angle, a crucial mechanical characteristic, had a significant influence on the development of these structures. This angle affected both the degree of uplift and stress; an increased angle resulted in enhanced uplift and stress, while a decrease resulted in a decline. Furthermore, the internal friction angle determined the compactness of the fold and the thickness of the forelimb, the part of the fold that inclines towards the advancing direction. These findings have enriched our knowledge of fault-related folds, highlighting the need to consider mechanical properties when studying their formation and evolution.

Contamination of groundwater with fluoride represents a significant global issue, with high concentrations posing serious public health threats. While fluoride is a critical element in water, excessive levels can be detrimental to human health and potentially life-threatening. Addressing the challenge of removing fluoride from underground water sources via nanotechnological approaches is a pressing concern in environmental science. To collate relevant information, extensive literature searches were conducted across multiple databases, including Google Scholar, PubMed, Scopus, Web of Science, the American Chemical Society, Elsevier, Springer, and the Royal Society of Chemistry. VOS Viewer software version 1.6.20 was employed for a systematic review. This article delivers an exhaustive evaluation of various groundwater fluoride removal techniques, such as adsorption, membrane filtration, electrocoagulation, photocatalysis, and ion exchange. Among these, the application of nanoparticles emerges as a notable method. The article delves into nano-compounds, optimizing conditions for the fluoride removal process and benchmarking their efficacy against other techniques. Studies demonstrate that advanced nanotechnologies—owing to their rapid reaction times and potent oxidation capabilities—can remove fluoride effectively. The implementation of nanotechnologies in fluoride removal not only enhances water quality but also contributes to the safeguarding of human health.

Abstract This study presents a novel empirical classification system for stability analysis of rock slopes in weak rock based on their geotechnical and geological properties. For this purpose, consideration is given to the marly rock slopes, which include three main groups of weak rock (lime marlstone, marlstone, and marly limestone). The 40 different slopes located in the South Pars special zone (Assalouyeh), southwest of Iran, are targeted in classification. To prepare comprehensive graphical stability charts for weak rocks, extensive field surveys, sampling, geotechnical laboratory tests, and ground measurements are conducted in slope sites. Using the findings of the study, empirical stability charts for slopes composed of weak materials were developed. The charts are associated with geotechnical indexes, geo-units’ weathering impact, and in-situ stress conditions. Using these graphical charts assists in investigating the stability condition of rock slopes and estimating the geotechnical characteristics of clay-based weak rocks such as marlstones.

Introduction: The East Iran orogen has experienced multiple buckling phases resulting in the formation of strike-slip fault splays. The geometric and kinematic characteristics of these splays are influenced by folding mechanisms. This study focuses on investigating the structural characteristics and tectonic evolution model of the Khousf splay, located in the northern terminus of the Nehbandan right-lateral strike-slip fault system.Methods: Field visits and geometrical properties from map views were used to analyze the structural features of the Khousf splay. The splay was found to consist of a multi-plunging anticline and syncline, referred to as the Khousf anticline and Khousf syncline, respectively. Flexural slip was identified as a significant mechanism for the formation of these structures. Structural evidence, including parasitic folds, active folds, and strike-slip duplexes, suggested that flexural slip occurred on discrete movement horizons among the rock units.Results: Analysis of the parasitic folds in the cores and limbs of the Khousf anticline and syncline revealed M, W, Z, and S shapes, with complex slicken-line patterns observed on faults parallel to the beds at the limbs. The analysis results indicated strain partitioning and inclined left- and right-lateral transpressional zones. Shortening estimates obtained from profiles in the Shekarab inclined transpressional zone were approximately 33%, 65%, and 68% for NE-SW, N-S, and NW-SE profiles, respectively. In the Arc area, which is the core of the anticline, shortening estimates from NE-SW and N-S profiles ranged from 14% to 10%. Structural analysis of the folds in this area revealed broad, close, semi-elliptical, and parabolic shapes, suggesting that secondary folds with NW-SE axis directions have been superimposed on the first-generation folds with E-W axis directions in the Khousf refolded splay.Discussion: The findings of this study highlight the structural characteristics and tectonic evolution model of the Khousf splay in the northern terminus of the Nehbandan right-lateral strike-slip fault system. The results suggest that flexural slip played a crucial role in the formation of the multi-plunging anticline and syncline in the Khousf splay. The presence of parasitic folds and complex slicken-line patterns on faults indicate the complexity of deformation processes. The observed strain partitioning and inclined transpressional zones suggest a complex tectonic history in the study area. The superimposition of secondary folds with different axis directions on first-generation folds adds further complexity to the structural evolution of the Khousf refolded splay. Overall, this study provides new insights into the structural characteristics and tectonic evolution of the Khousf splay in the East Iran orogen.

Abstract The Zagros orogenic belt, one of the most prominent and famous collisional belts in the central part of the Alpine-Himalayan orogenic chain, is located between the southern margin of the Central Iranian microcontinent and the northern margin of the Arabian plate. The structural architecture and folds and faults relationships of a significant segment of the south-central part of the Zagros’ hinterland are investigated in this study through stereoscopy of aerial photographs, interpretations of satellite images, consideration of the major ground topographic variations, and field research. This research found that there must have been at least two major deformation events: (1) a ductile phase, which is older than the Eocene, and (2) a semi-brittle deformation stage, which is younger than the early Miocene and is represented by thrusting, folding, and strike-slip faulting. The presence of numerous fault-related folds and fold-accommodation faults in this area demonstrates the close kinematic relationship between folding and faulting. Based on the topographic changes, a major hidden tear fault and a basement hidden back thrust, which play important roles in the architecture of the area, have been suggested.

Abstract Landslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN–DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN–DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN–DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN–DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN–DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province.

Slope stability is one of the most important topics of engineering geology with a background of more than 300 years. So far, various stability assessment techniques have been developed which include a range of simple evaluations, planar failure, limit state criteria, limit equilibrium analysis, numerical methods, hybrid and high-order approaches which are implemented in two-dimensional (2D) and three-dimensional (3D) space. In the meantime, limit equilibrium methods due to their simplicity, short analysis time, coupled with probabilistic and statistics functions to estimate the safety factor (F.S), probable slip surface, application on different failure mechanisms, and varied geological conditions has been received special attention from researchers. The presented paper provides a review to limit equilibrium methods used for discontinuous rock slope stability analyses with different failure mechanisms of natural and cut slopes. The article attempted to provide a systematic review for rock slope stability analysis outlook based on limit equilibrium approaches.

Pressuremeter Test (PMT), Cone Penetration Test (CPT), and Standard Penetration Test (SPT) are the key in-situ experiments to directly estimate the in-situ modulus of deformation and strength parameters of soils, which are highly used in coastal alluvium. In addition, CPT and SPT are unique tests for estimating engineering properties that are ideal for onshore regions. These tests are adaptable for coastal alluvium with different saturation levels, which facilitates the determination of the field deformation modulus. Regression analysis, on the other hand, is primarily employed to estimate the empirical relationship between measured parameters and to predict geo-engineering properties. This technique is typically used to estimate the in-situ modulus of deformation and strength parameters from CPT, SPT, and PMT results. The proposed formulas in this paper used regression to correlate and characterize coastal alluvium located in phase 14 South Pars (Assalouyeh) and were compared with previously published equations. As a result of the evaluations, the correlations provided for phase 14 South Pars can be expressed as Em = 0.442 qc + 2.221 (R2 = 0.999) and PL = 0.06 Em0.778 (R2 = 0.515). • This empirical method can be useful for ground assessment and estimating the in-situ modulus of deformation. • This relationship can use as a modification for the original formula used based on CPT-PMT-SPT for alluvium. • This empirical correlation provides fast and reliable data for Southwest Iran nearby the Persian Gulf.

The seismic response of structures can have a significant impact on adjacent structures’ response. Although several numerical studies have been applied in the field of tunnel–soil–pile interaction systems, there is a lack of experimental research specifically focused on the effects of this interaction on tunnel cross-section deformation and the existence of structure on encircling soil response. In this study, shaking table tests were conducted to examine the seismic response of a tunnel and the surrounding soil when an eight-story structure with piles was located in the vicinity of the tunnel. Four series of physical models were analyzed, including free-field soil (S), tunnel–soil (TS), soil-piled structure (SP), and tunnel–soil-piled structure (TSP), under sinusoidal vibration at three frequencies on loose sand. According to the results, the tunnel significantly impacts the surrounding soil response during seismic excitation with reduced acceleration at the tunnel invert and increased acceleration at the tunnel crown. In the TSP model, applied frequency affects the recorded acceleration amplitude at the tunnel invert. Although acceleration amplitude decreases at 3 Hz frequency excitation compared to the free field model, 8 Hz excitation resulted in bigger values in tunnel invert. Displacements are higher at the tunnel crown, indicating tunnel-induced soil deformation and maximum shear strain concentrated near the tunnel crown. The tunnel cross-section exhibited oval shape changes, with higher forces on the tunnel crown in the presence of piles.

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.

Abstract Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from ‘selfie’ captures for personal identification. A less‐visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject‐specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow‐based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short‐term evaluations, the proposed methods showed lower accuracy when matching against long‐term data or when facing data captured under varying illumination and facial expressions.

The Alborz mountain range in northern Iran is part of the active and seismic Alpide belt, where assessing seismic hazards is crucial due to the region’s history of large instrumental earthquakes and destructive seismic background. Moment rate estimation, which quantifies tectonic activity, offers a novel approach to understanding the energy potential of active tectonic regions. In this study, a regional perspective is employed to investigate the maximum horizontal acceleration for Tehran, the major city in Alborz, resulting from the Sorkh-e Hesar and Ghasr-e-Firuzeh faults located approximately 7.5 km southeast of Tehran. These faults have a seismic potential of Mw 6.5 and a gravity of ~0.5723. While previous studies have identified faults in northern Tehran as the greatest seismic risk, our findings suggest otherwise. The calculated geological moment was 5.18218 × 1017 Nm/y, with a seismic moment rate of 1.83375 × 1014 Nm/y, providing valuable insights into fault activity and seismic potential in the study area.

In this study, the active tectonics, paleoseismicity, and seismic hazards of the Doroud Fault are examined through high-resolution satellite image interpretations, field investigations, outcrop and trench excavations, and the dating of geochronology samples. The Doroud Fault (DF), one of the essential segments of the Main Recent Fault in the northern margin of the Zagros mountain range, has a historical and instrumental background of high seismicity. We present the first constraints from tectonic geomorphology and paleoseismology along the Doroud Fault near the capital city of Dorud. Detailed observations from satellite imagery, field investigations, real-time kinematic (RTK) measurements, paleoseismological trenching, the radiocarbon (C14), and optically stimulated luminescence (OSL) as ages allowed us to map the fault in detail, describe and characterize its kinematics, and document its recent activity and seismic behavior (cumulative displacements, paleoseismicity, and magnitude, as well as recurrence interval) relevant to the recent seismic activity of the Doroud Fault during the late Holocene as one of the most important seismogenic faults in Zagros. Modern alluvial terraces of gullies and loess accumulations are systematically deflected and/or offset with co-seismic rupture, landslides, and scarps, indicating that the Doroud Fault has been active in the late Quaternary and is characterized by dextral strike–slip movements with a normal component. In addition, our findings provide a comprehensive analysis of the fault displacement, the timing of paleoearthquakes, and the right-lateral slip rate of the Doroud Fault. The late Holocene slip rate of the Doroud Fault using the OSL dating the gully is as follows: the minimum and maximum horizontal slip rates are estimated to be 1.82 and 2.71 mm/yr, and vertical slip rates of 1.03 and 1.53 mm/yr are calculated for the past 4600 ± 900 years in the middle segment of the fault. This study focused on a paleoseismological trench within the archeological sites of Darbe-Astaneh. The central portion of the fault has historically hosted more than nine earthquakes in the last 66 ka years, according to the study’s findings. According to paleoseismology studies, the Doroud Fault has the seismic capability to cause earthquakes with a magnitude of more than 7.4 and a total slip rate of about 3.83 ± 0.1 m. The average recurrence interval for the identified paleoearthquakes is approximately 104 ± 7 years.

This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The study area was analyzed by extracting watersheds from a Digital Elevation Model (DEM) and calculating eight morphometric indices. The morphometric parameters were normalized using fuzzy membership functions to improve accuracy. The performance of the machine learning algorithms is evaluated by mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R2) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R2 value of 0.974, MSE of 4.14 × 10−6, and MAE of 0.0015. The results of the machine learning algorithms were compared to the tectonic characteristics of the area, indicating the potential for utilizing the ANN algorithm in similar investigations. This approach offers a novel way to assess watershed morphometry using ML techniques, which may have advantages over other approaches.

The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R2) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves.

Iranshahr Basin is located in the Sistan and Baluchistan province, subject to severe drought and excessive groundwater utilization. Over-reliance on groundwater resources in this area has led to aquifer drawdowns and socio-economic problems. The present study aimed to identify appropriate sites for Artificial Recharge Groundwater (ARG) in a single platform by applying GIS fuzzy logic spatial modeling. Three stages were performed. In stage one, nine factors affecting ARG were collected based on the literature review. In stage two, geology, soil, and land-use layers were digitized from the existing maps. Some layers such as rainfall, unsaturated thickness, water quality, and transmissivity data were imported to ArcGIS environments, and their surface maps were made by Ordinary Kriging (OK) method. In stage three, the parameters were standardized with the fuzzy membership functions, and the GAMMA 0.5 fuzzy overlay model was applied for aggregation parameters. Results showed that 72.8%, 16.7%, 7.7%, 2.5% of the areas were classified as unsuitable, moderate, suitable, and perfectly suitable sites for planning a groundwater recharge site. Subsequently, the minimum area required regarding the possible errors based on the literature review determined six sites (A–E) as areas with higher priority. Then, the recommended unsuitable/suitable sites were validated and omitted by using some more detailed views. Finally, two sites (E and F) were omitted, and four sites (A, B, C, D) were recommended for future artificial recharge planning.

The present article provides an empirical relationship for rock slope stability assessment based on Qslope classification. The relationship is used as a correction procedure for classic Qslope for mountain regions with multiple fractures related to several faults. The relationship is derived from 25 distinct jointed slopes near the North Tabriz Fault (NTF). The NTF triggered numerous micro-faults and fractures in rocky landscapes, resulting in sliding on a variety of scales. The present empirical method is introduced based on a field survey and a stability analysis of the studied slopes based on Qslope principles. The results indicate that the classic formulation of Qslope can be modified to β = 62.6 log10 (Qslope) + 36 for mountain regions with multiple fault zones.• This empirical method can be useful for fast stability assessment on jointed rock slopes.• This relationship can use as a modification for the original formula in multiple faults zones.

Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments.

Biometrics use an individual's biological traits for personal identification. Various sensors have been used to obtain these measurements. Microwave biometric scans have recently gained traction as a non-contact technique due to their robustness to environmental lighting and unobtrusiveness. To evaluate microwave signature of human forearm as a biometric modality, an 8-antenna (Wi-Fi) data collection setup was developed and initially tested with foil-wrapped tubes of different geometric cross sections. The system was later evaluated by collecting microwave samples from human volunteers' forearms and classifying the data, from different antenna subsets, using Support Vector Machines and Naive Bayesian classifiers. Our results show that human identification via microwave signals is possible even with a subset of the above mentioned 8-antenna configuration.

Earth-dam failure starts with cracking in the clay core, and this cracking is not easy to detect and prevent. Therefore, swellable clay is a feasible solution, which helps to close the cracks automatically based on the self-healing process. The presented study utilizes experimental procedures to analyze the swelling behavior of fine-grained clayey soils to prevent structural failure regarding crack generations. In this regard, the clayey materials were modified using Kaolin and Bentonite mixed with various weight percentages (2.5, 5.0, 7.5, 10.0, and 12.5%) and extracted the geotechnical characteristics of the studied soils, which included 90 specimens and 85 tests, such as physical properties, consolidation, particle-size analysis, hydrometry, Atterberg limits, compaction, odometer, and pinhole. The experimental results revealed that the swelling of the Bentonite is more than Kaolin satisfied for self-healing features in clayey soils. Regarding the numerous swelling tests, Bentonite provides optimum results (attained 10%) compared to Kaolin. As a verification procedure, the pinhole test was performed on samples, which revealed that Bentonite was dominant in controlling the water flow through the samples.

This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models.

In this paper, faults, one of the most important causes of geohazards, were investigated from a kinematic and geometric viewpoint in the northern part of the Sistan suture zone (SSZ), which serves as the boundary between the Afghan and Lut blocks. Furthermore, field evidence was analyzed in order to assess the structural type and deformation mechanism of the research area. In the northern Birjand mountain range, several ~E–W striking faults cut through geological units; geometric and kinematic analyses of these faults indicate that almost all faults have main reverse components, which reveals the existing compressional stress in the study area. The northern Birjand mountain range is characterized by four main reverse faults with ~E–W striking: F1–F4. The F1 and F2 reverse faults have southward dips, while the F3 and F4 reverse faults have northward dips. Moreover, the lengths of the F1, F2, F3, and F4 faults are 31, 17, 8, and 38 km, respectively. These faults, with reverse components that have interactive relationships with each other, form high relief structures. The study area’s main reverse faults, including F1 to F4, are extensions of the Nehbandan fault system, while their kinematics and geometry in the northern Birjand mountain range point to an N–S pop-up structure.

This study provides a multi-disciplinary overview of the seismology and geodetic data with tectonics analysis in order to provide an evaluation of stress trajectories, and probabilistic fault rupture hazard assessment. Based on the different scenarios obtained from the comparison of several overviews and their interpretation, we investigated the kinematics and active tectonics of different structural zones. In this study, the magnitudes and directions of seismic and geodetic strain rates (SSR and GSR) were evaluated using reliable earthquake focal mechanisms and all available GPS data (1999–2015) in the structural subzones of northern Iran, where have experienced more than 14 strong instrumental earthquakes (Mw ≥ 6). In addition, a tectonic stress model was inferred from the Focal Mechanism Stress Inversion (FMSI). The new crustal stress map was proposed by the weighted average analysis of the SSR, GSR, and FMSI. N35.5° E and N104° E were estimated for the Alborz and Talesh mountains, respectively. The numerical analysis of stress regimes confirms the slip partitioning mechanism of oblique shortening on the sub-parallel thrusts and strike-slip faults in the area. Four main stress regime categories were defined, including thrust (49.37%), strike-slip (39.24%), thrust with a strike-slip component (2.53%), normal (1.27%), and unknown faulting (7.59%). Seismic and geodetic moment rates (SMR and GMR) and their comparison were also calculated in order to evaluate the function of these parameters in determining the seismicity arrangement. The ratio of the seismic/geodetic moment rates for the area is ~70.7%. This ratio for the Alborz, western Koppeh-Dagh, north part of Central Iran, South Caspian Basin, and Talesh is ~0.9, 0.3, 11.9, 0.3, and 57.3, respectively, which indicates the most elastic energy has been released in the Talesh and the north part of Central Iran. The comparison of geodetic moment rates in the subzones of the area indicates that geodetic deformation is high in the Central Alborz (networks 8, 9, 17) and western Koppeh-Dagh (networks 5, 13).

This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo Optimization Algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases. • The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. • The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies.

This data article focuses on the arsenic in the groundwater of Rafsanjan plain in Kerman Province of Iran where the groundwater is being extensively used for drinking and irrigation of pistachio gardens. The measured arsenic concentrations range from 4 to 278 μg/L (with an average of 59 μg/L). About 85.3% of water samples have arsenic concentrations above 10 μg/L provided by the World Health Organization, WHO, guideline value. This data article provides also map showing the concentration of arsenic in groundwater of Rafsanjan area based on the situation of the sampling points in Rafsanjan region.

This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the ‘degree of reliability (degree of stability-instability contingency)’ for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making.

Earth fissure structures are a common type of geohazard associated with geotechnical surface ruptures that can occur almost anywhere on the ground surface due to a variety of different mechanisms, and in most cases, these hazardous cracks have resulted in severe damage to infrastructure. Investigation of the structural characteristics of fissures in an active tectonic area (such as Birjand in eastern Iran) can improve our understanding of connections between the fissures and structural geology. The first reported earth fissures in NW Birjand are distributed along the North Birjand fault. Field observations and analysis of the detailed topography cross-sections using the real-time kinematic (RTK) technique have been used to infer the fissure development. Morphometric analysis of faults and fissures shows that the long axis of most fissure structures is distributed perpendicular to the strike of the North Birjand fault. The average volume strain in the study area was calculated to be 14.68 percent based on fissure geometry studies, with the maximum volume strain corresponding to the location of the North Birjand fault’s youngest splay. This splay formed fault-propagation folds with exposed fissure structures. This study shows that the fault-propagation folds in this system are subjected to the activity and evolution of the ground fissure as a result of the expansion of tensile stress.

The presented article provides a comprehensive study on the stability analysis of earth-fill dams under rapid drawdown and transient flow conditions used to prepare stability analysis charts by conducting coupled finite-element numerical and analytical limit equilibrium procedures. In this regard, the impacts of different rapid drawdown conditions on the safety factor of the Alavian earth-fill dam are determined. The slope stability charts present for both shallow and deep slip surfaces with various permeabilities are verified by ground information obtained with extensive instrumentation on the dam’s site. The results showed that by decreasing the permeability of the core’s material, despite preventing seepage, the instability risk of the upstream slope as a result of rapid drawdown intensifies. Also, as stability charts can be stated, with increasing the slip surface’s depth and decreasing the hydraulic hydration, the reliability decreases, and the sliding surfaces’ sensitivity increases based on the drawdown rates, which have been revealed to be from 0.2 to 0.6, the most critical state for safety factors, showing significant declines.

Energy has become an integral part of our society and global economic development in the twenty-first century. Despite tremendous technological advancements, fossil fuels (coal, natural gas, and oil) continue to be the world’s primary source of energy. Global energy scenarios indicate a change in coal consumption trends in the future, which in turn will have commercial, geopolitical, and environmental consequences. We investigated coal consumption up to 2030 using a new hybrid method of WOANFIS (whale optimization algorithm and adaptive neuro-fuzzy inference system). The WOANFIS method’s performance was assessed by the MSE (Mean Squared Error), MAE (Mean Absolute Error), STD (error standard deviation), RMSE (Root Mean Squared Error), and coefficient of correlation (R2) among the real dataset and the WOANFIS result. For the prediction of global coal consumption, the proposed WOANFIS had the best MAE, RMSE, and correlation coefficient (R2) values, which were 0.00113, 0.0047, and 0.98, respectively. Lastly, future global coal consumption was predicted up to 2030 by WOANFIS. Following 150 years of coal dominance, the results demonstrate that WOANFIS is a suitable method for estimating worldwide coal consumption, which makes it possible to plan for the transition away from coal.

This data article provides a precise level data on alluvial fans of the western border of Lut desert, a unique location on the Earth, known as one of the hottest spot on the Earth. This data is essential for morphotectonic investigations and is valuable in the field of tectonic and geomorphology studies. It helps to evaluate the region from the viewpoint of tectonic activity by considering the dynamics of alluvial fans, climate and tectonic setting. The data which are presented for 68 quaternary alluvial fans, is taken by processing of remotely sensed Landsat satellite data, photogeology of aerial photographs, measuring on topographic maps and controlled by field checking. This data is useful for defining of a morphotectonic model of this limited access region considering the uplift of the source area along Nayband fault, as the basin–margin fault, respect to the base level. Keywords: Tectonics, Geomorphology, Morphometrics, Geology, Kerman

Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies.

Morphotectonic indices by representing the longer period of time than recorded earthquake data, are useful in evaluating the tectonic activity of a region. Dashtkhak area is located in Kerman province of Iran, where one of the most active faults, Kouhbanan strike slip fault, passes through. This data article provides a precise level data on mountain fronts and valleys of Dashtkhak region that is fundamental for morphotectonic investigations of the relationship among geomorphology and tectonic activity. This data is valuable in the field of geology and geography. Mountain fronts and valleys data is more relevant in the field of tectonics and geomorphology. It helps to evaluate a region from the viewpoint of tectonic activity. The data which are presented for 31 mountain fronts and 61 valleys, is taken by processing of remotely sensed Landsat satellite data, photogeology of areal photographs, measuring on topographic maps and controlled by field checking. This data is useful for calculating of some morphotectonic indices such as sinuosity of mountain fronts (smf), mountain front faceting percentage (Facet%), the ratio of valley floor width to valley height (Vf) and the valley ratio (V).

The presented article provides an empirical method on rock slope classification, slope mass rating (SMR), Qslope, stability condition, failure type and stabilisation procedures for 35 road/railway discontinuous rock slopes after field surveys in Isfahan Province of Iran. Also, it presents the empirical correlation for SMR and Qslope classification system that prepares a link between the stability status (safety factor, reliability condition) and stabilisations (failure mechanism, support system) which performed on natural/trench slopes cases related sedimentary rocks cuts in the studied region. As results, the SMR-Qslope equation for Isfahan Province obtained as SMR = 11.89 ln(Qslope) + 71.92 (R2 = 0.756).• This method can be useful on a stability assessment and providing appropriate stabilisations for the discontinuous rock slope based on simple assumptions where used in different geotechnical projects such as road/railway slope, excavations, open-pit mining, trench boring, etc.• This method can be useful for quick calculation of stability conditions and suggestion of slope maintenance system in a short time as preliminary reactions.• This method can be used as an effective way to convert SMR and Qslope equations and used both benefits in geo-engineering application faced with discontinuous rock masses.• This method can be useful for future research on the empirical geomechanically classification and rock mass preliminary quantifications.• This method can be used as an appropriate database for SMR and Qslope classification.

The oblique collision between the northeastern margin of the Arabian platform and the Iranian microcontinent has led to transpressional deformation in the Zagros orogenic belt in the central part of the Alpine–Himalayan orogenic belt. Although previous articles have emphasized the dextral sense of shear in the Zagros orogenic belt, in this paper, using several indicators of kinematic shear sense upon field checking and microscopic thin-section studies, evidence of the development of a sinistral top-to-the NW deformation belt is presented. The mean attitudes of the foliations and lineations in this belt are 318°/55°NE and 19°/113°, respectively.

Interferometric Synthetic Aperture Radar (InSAR) monitors surface change and displacement over a large area with millimeter-level precision and meter-level resolution. Anar fault, with a length of ~200 km, is located in central Iran. Recent seismological studies on the fault indicated that it is approaching the end of its seismic cycle. Although a large earthquake is imminent, the mechanism of the fault is not well understood. Therefore, understanding and discovering the mechanism of Anar fault remains a challenge. Here, we present an approach of displacement fault analysis utilizing a combination of InSAR data obtained from the persistent scatterer interferometry (PSI) method and 178 Sentinel-1 images (ascending and descending) (2017–2020). We incorporated groundwater samples from 40 wells, radon concentration anomaly mapping, Global Positioning System (GPS), and 3D displacement measurement acquired over four years (2016–2020). We investigated and monitored the deformation of the fault plate’s behavior over the last three years (2017–2020) to explore new evidence and signature of displacement. The results show that the time series analysis in the fault range has an increasing displacement rate in all dimensions. We observed that the line-of-sight (LOS) displacement rate varied from −15 mm to 5 mm per year. Our calculations show that the E–W, N–S, and vertical displacement rates of the fault blocks are 2 mm to −2 mm, 6 mm to −6 mm, and 2 mm to −4 mm per year, respectively. An anomaly map of the radon concentration shows that the complete alignment of the high concentration ranges with the fault strike and the radon concentration increased on average from 23.85 Bq/L to 25.30 Bq/L over these three years. Therefore, we predict rising the radon concentration is due to the increase in activity which resulted in a deformation. Finally, our findings show that the Anar fault is an oblique and right-lateral strike-slip with a normal component mechanism. We validated the proposed method and our results by comparing the GPS field data and PSI measurements. The root mean square error (RMSE) of the PSI measurement is estimated to be 0.142 mm. Based on the supporting evidence and signature, we conclude that the Anar fault activity increased between 2017 and 2020.