https://www.ijgeophysics.ir/ Iranian Journal of Geophysics en daily 1 Mon, 25 Aug 2025 00:00:00 +0330 Mon, 25 Aug 2025 00:00:00 +0330 https://www.ijgeophysics.ir/article_227596.html The study aims to provide new insights into the subsurface crustal and thermal structures of the Gongola Basin, NE Nigeria, using airborne magnetic and gamma-ray spectrometric datasets for hydrocarbon prospecting. The total magnetic field, reduced to the magnetic equator (TMI-RTE), underwent various enhancement techniques and depth estimation methods, including vertical derivative (VD), Rose diagram (RD), source parameter imaging (SPI), and 2D magnetic depth modelling to distinctly map the subsurface geological structural elements, basement architecture, and the depth to the top of the magnetic basement. Conversely, the radiogenic heat production (RHP) and regions favourable for hydrocarbon maturation and accumulation were determined via thorium normalisation techniques (TNT) from the concentration of radioactive elements (K, eTh, and eU). The FVD, SVD, and RD indicate that the dominant structures within the study area trend northeast-southwest, north-northeast-south-southwest, and northwest-southeast, which may serve as migratory pathways or traps for hydrocarbon accumulation. Additionally, the SPI and the 2D magnetic depth models reveal that the thickness of the sedimentary beds ranges from over 1000 m to over 5000 m. Depths between 3000 m and 5000 m can be found in the central, eastern, southeastern, and northeastern parts of the study area. These areas correspond to Alkaleri, Akko, Gombe, Futuk, Yuli, and Debar Fulani, respectively. The depth range of 3000 m to 5000 m is sufficient for hydrocarbon maturation and accumulation. The positive DRAD values indicate zones of probable hydrocarbon potential. The estimated total RHP rates for the study area range from 289.4 to 1477.6 ρwkg-1. The RHP values exceeding 797.87 Wkg-1 obtained in the Keri-Keri Formation (Kst) and Pindiga Formation (Psh are attributable to clay, limestone, shale, and sandstone. This falls within the moderate RHP range (750–1500 ρWkg-1), which is adequate for hydrocarbon maturation and accumulation in the study area.   https://www.ijgeophysics.ir/article_216198.html The solar wind plays a crucial role in Earth's magnetosphere, driving nonlinear interactions that result in geomagnetic storms and energy transfer to the ionosphere. This study investigates the relationship between solar wind parameters (e.g., speed, density, and interplanetary magnetic field components) and geomagnetic indices (e.g., AL, AE) with the electron energy flux entering the ionosphere. A ten-year satellite dataset (2004–2014), covering a complete solar cycle, was analyzed. Advanced machine learning techniques, including Random Forest, Support Vector Regression, Ridge Regression, and Principal Component Analysis, were employed to identify the most influential features affecting the electron energy flux. Additionally, preprocessing steps, such as outlier detection, normalization, and feature selection, were implemented to ensure optimal model performance. The analysis revealed that the importance of parameters dynamically varies during periods of solar minimum and maximum activity, with parameters such as Bz and the AL index having distinct effects on electron energy flux. This study not only presents an innovative approach to analyzing the complex nonlinear Sun-Earth interactions but also emphasizes the need for adaptive modeling techniques that account for temporal variations. The findings of this research provide a basis for more accurate space weather and climatology predictions, as well as for improving hybrid data-driven and physical models for future studies. https://www.ijgeophysics.ir/article_229620.html This study develops a comprehensive theoretical framework for quantifying frequency-dependent seismic attenuation in porous fractured carbonate rocks by integrating pore-scale microstructural characteristics with fluid saturation dynamics using the T-matrix approach. The model establishes an explicit connection between the hydraulic permeability tensor and ellipsoidal crack geometries, incorporating this relationship into a unified squirt flow formulation to predict both velocity dispersion and attenuation characteristics. Through systematic sensitivity analysis, the research demonstrates that permeability reaches maximum values in prolate-shaped cracks, while both attenuation patterns and velocity behavior are predominantly controlled by specific crack geometries and fluid composition properties. In single-phase fluid systems, increasing oil saturation produces elevated attenuation levels within typical logging frequency ranges of 1-20 kHz. In two-phase oil-gas systems, gas saturation below 20% induces substantial reduction in P-wave velocity along with increased attenuation effects, primarily resulting from significant compressibility contrasts between the fluid phases. The model validation utilized experimental data comprising five carbonate samples with complete petrophysical characterization. The inversion process for permeability estimation, employing a crack aspect ratio of 0.0001, demonstrated close alignment with experimentally measured values. Predicted quality factors (Q) showed strong correspondence with experimental attenuation measurements for homogeneous samples, though some deviations emerged in the heterogeneous sample where scattering effects presumably dominate over squirt-flow mechanisms. This research addresses a significant gap in existing models by establishing a direct microstructurally-based connection between permeability and dynamic squirt-flow response in fractured carbonate systems. The methodology advances beyond conventional approaches by incorporating the full permeability tensor derived from realistic pore geometries rather than relying on simplified scalar permeability values derived from empirical correlations. The results underscore the critical importance of incorporating pore shape frequency effects, and proper fluid substitution models into including attenuation for seismic data interpretation. The study acknowledges certain limitations in the current model, particularly its assumption of uniform fluid saturation and its inability to account for large-scale heterogeneities such as vugs and macro-fractures. These limitations are particularly relevant for complex carbonate reservoirs exhibiting multi-scale pore systems. Future work will focus on integrating patchy saturation effects for effective fluid modulus calculation, incorporating multi-scale pore inclusions representing different pore types, and combining the squirt-flow approach with scattering theory to better represent realistic porous-fractured environments. These enhancements will extend the model's applicability to more realistic geological formations. The framework presented here provides a foundation for developing more comprehensive rock physics models that can better address the challenges of characterizing heterogeneous carbonate formations through seismic attenuation analysis.  https://www.ijgeophysics.ir/article_230775.html Porphyry copper deposits play a critical role in global metal supply, emphasizing the need for non-invasive exploratory geophysical techniques to identify mineralized zones at depth. This study employs coupled hydro-geophysical finite-element modeling within the a 2D framework to simulate groundwater flow, ion diffusion, and time-lapse electrical resistivity in a plausible  geological domain representative of near-surface porphyry copper systems. The model considers fluid flow through layered geological units and a mineralized zone, while tracking ion transport to mimic copper leaching. Meanwhile, real-time electrical resistivity tomography (ERT) captures electrical resistivity variations induced by ion concentrations. Additionally, the ERT data are then inverted using the Conjugate Gradient Least Squares (CGLS) algorithm, allowing the reconstruction of four 2D electrical resistivity models that illustrate the evolution of the mineralized zone over time. The results highlight distinct flow and concentration patterns, with the inverted electrical resistivity models aligning with the velocity gradients from the forward simulations, improving the detection of the target zone. This integrated approach combines hydrological and geophysical perspectives, providing a scalable framework to optimize drilling strategies while minimizing environmental impact. This innovative approach serves as a non-invasive tool for the exploration of porphyry copper deposits, offering a highly effective methodoslogy for detecting mineral resources beneath the earth's surface. The precision and reliability of this exploration tool not only enhance the efficiency of mineral deposit identification but also contribute to more sustainable mining practices.   https://www.ijgeophysics.ir/article_233800.html A seismic vulnerability assessment was performed at Mount Gamalama using Horizontal to Vertical Spectral Ratio (HVSR)-derived microtremor data and shear strain estimation methods. The measurement instrument utilized was a three-component portable seismometer (Lunitek). Microtremor data acquisition was performed at 123 locations with a duration of 30-45 minutes and a spacing of 250-500 meter. The primary data consisted of recorded microtremor signals from the seismometer. within the research coordinate range of 0.7526°N – 0.868°N and 127.292°E – 127.3926489°E. Data were processed to quantify dominant frequency and amplification, which were used to model seismic responses under Peak Ground Acceleration (PGA) scenarios from 6.2%gal to 75%gal. Data processing was conducted using two platforms: Geopsy for microtremor analysis and Surfer for interpolation. Structural damage ranging from surface vibration to major cracking and collapse was identified. Zones A, B, and C were shown to exhibit increasing levels of vulnerability, with Zone A being most susceptible at high acceleration levels. Amplification effects caused by surface geological conditions were observed, particularly in zones characterized by alluvial and pyroclastic materials. Elevated shear strain values were associated with soil types D through K, reinforcing their contribution to local seismic hazard. The spatial progression of seismic risk areas was confirmed to correlate with Peak Ground Acceleration (PGA) escalation, supporting the application of this methodology for future regional disaster preparedness. https://www.ijgeophysics.ir/article_234761.html This study presents a comprehensive analysis of the subsurface geological structures of the Sarulla geothermal field located in North Sumatera, Indonesia, utilizing satellite-based gravity modelling techniques. The aim of this study were to: a) Identify the presence and characteristics of fault structures in the Sarulla Geothermal Field using gravity derivative analysis; b). Interpret the subsurface lithology of the geothermal system through 2D forward gravity modelling; c). Evaluate the geothermal resource potential. Employing high-resolution satellite gravity data from the GGMplus model, gravity anomaly maps were generated and interpreted through advanced modeling approaches to identify density variations indicative of subsurface features such as faults, fractures, and lithological boundaries. The methodology integrates satellite gravity data processing, anomaly separation, and forward and inverse gravity modelling to construct a detailed subsurface structural model. Key findings reveal significant gravity anomalies correlating with major tectonic structures and geothermal reservoirs, highlighting zones of enhanced permeability and heat flow. These results provide critical insights into the spatial distribution of geothermal reservoirs and the structural controls influencing fluid migration and heat accumulation. The study underscores the effectiveness of satellite gravity modelling as a non- invasive, cost-efficient tool for geothermal exploration, particularly in regions with limited ground-based geophysical data. The implications of this research extend to optimizing drilling targets, reducing exploration risks, and supporting sustainable geothermal development in the Sarulla field and similar geothermal systems worldwide. This work contributes to the growing body of knowledge on remote sensing applications in geothermal resource assessment and demonstrates the potential of integrating satellite gravity data with geological and geophysical information for enhanced subsurface characterization. https://www.ijgeophysics.ir/article_230880.html Indonesia, located at the convergence of active tectonic plates, is highly susceptible to earthquakes. Musi Banyuasin District in South Sumatra is one of the regions characterized by complex geological structures and potential seismic hazards. This study investigates sediment thickness and seismic vulnerability index as key parameters for evaluating local site effects. The Horizontal to Vertical Spectral Ratio (HVSR) method was applied to analyze dominant frequency (f₀), amplification factor (A₀), dominant period (T₀), seismic vulnerability index (Kg), and sediment thickness (H). The results show that dominant frequency ranges from 2 to 18 Hz, amplification values vary between 1.6 and 5.2, dominant period spans 0.04–0.38 s, seismic vulnerability index ranges from 0.2 to 3.2, and sediment thickness varies from 8 to 64 m. The majority of the study area is classified as type I soil with hard rock formations based on frequency and period parameters. Overall, the low amplification and seismic vulnerability index values indicate a relatively minimal potential for severe earthquake damage. These findings provide new insights into the subsurface conditions of Musi Banyuasin and highlight their relevance for disaster risk reduction. The results can serve as a scientific basis for future land-use planning and construction strategies aimed at mitigating seismic risk in the region.  https://www.ijgeophysics.ir/article_241207.html The low strength of clay soils is one of the main challenges in road construction projects. Geopolymerization using various additives such as fly ash, blast furnace slag, and alkaline activators is a common method to improve the mechanical properties of these soils. However, accurate and rapid evaluation of the effects of these materials on soil strength traditionally requires extensive and time-consuming laboratory tests. This study proposes an integrated machine-learning framework to predict the unconfined compressive strength (UCS) of clay soils stabilized with fly ash and blast‑furnace slag. A dataset comprising 283 experimental samples of lime/slag-stabilized clay activated with sodium hydroxide was utilized. Input variables include fly ash and slag percentages, alkaline activator molarity, alkaline‑to‑binder ratio, atomic Na/Al and Si/Al ratios, and the soil’s liquid and plastic limits.  To this end, several machine learning models including artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and extreme gradient boosting (XGBoost), were employed in combination with metaheuristic optimization algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithm (GA). The findings revealed that the proposed ANN-PSO hybrid model demonstrated outstanding predictive accuracy, achieving an R² value of 0.98, confirming its effectiveness for reliable UCS estimation. Sensitivity analysis further indicated that blast furnace slag was the most influential factor in enhancing soil strength, while the type of clay also had a considerable effect on the final UCS.  https://www.ijgeophysics.ir/article_233413.html Buried pipelines are vital components of lifeline infrastructure, ensuring the safe and efficient transport of water, gas, and other essential resources. Earthquake-induced fault displacements, however, pose a significant threat to their structural integrity. Numerous numerical studies investigate soil–pipe interaction under fault movement. These approaches, while effective, often require high computational effort, such as modeling the pipeline with shell elements and representing the surrounding soil as a continuum. In practice, engineering analyses frequently adopt Winkler-type spring models to simulate the soil medium, but concerns remain regarding the suitability of this simplified representation and the assignment of p–y responses. To address these limitations, the present study adopts a moderately demanding yet efficient numerical approach. An HDPE pipe buried in sandy soil is subjected to normal faulting at a dip angle of 90°. The pipeline is modeled as a beam element embedded within continuum soil zones with interfacial contact, providing a balance between computational efficiency and modeling accuracy. Comparisons between numerical results and centrifuge test data demonstrate satisfactory agreement. The computed axial and bending strain distributions along the pipeline, as well as the p–y response, align well with centrifuge observations, whereas the ASCE (1984) guideline significantly overestimates the ultimate force and stiffness of the springs. Furthermore, parametric analyses varying design parameters such as burial depth, pipe diameter, and wall thickness show that the numerical trends are physically consistent and provide practical guidance for engineers in selecting optimal values. https://www.ijgeophysics.ir/article_243673.html The 7.3 magnitude Serpel Zahab earthquake, which occurred on November 12, 2017, in the Zagros region, underscores the need for thorough seismic hazard analyses in this active tectonic zone. In the present study, the spatiotemporal variations of the seismic parameters b-value and DC-value in the vicinity of the earthquake were examined using an integrated earthquake catalog recorded by the National Seismological Center (IRSC) during the period from January 1, 2006, to October 31, 2024. The results indicate that in the active tectonic segments, the b-value varied between 0.4 and 1.4, a reduction in this parameter adjacent to the fault prior to the mainshock suggests stress accumulation and the potential for an imminent rupture. In contrast, an increase in the DC-value reflects enhanced heterogeneity in the fault structure and a more complex fracture mechanism nearing failure. Immediately following the main event, a significant increase in the b-value was observed, indicative of stress release through smaller aftershocks, alongside a decrease in the DC-value. The temporal analysis of the b-value revealed a decreasing trend before and a pronounced increasing trend after the earthquake, attributable to stress redistribution within the region’s geological framework. Additionally, low b-value values (less than 0.5) at depths between 10 and 20 kilometers confirm a concentration of stress within this depth interval. These findings clearly demonstrate the complementary roles of the b-value and DC-value in characterizing seismic behavior, assessing fault evolution, and understanding the tectonic processes in reverse, strike-slip, and extensional faults of the Zagros region. The obtained results serve as a crucial tool for evaluating seismic hazard in tectonically active areas. https://www.ijgeophysics.ir/article_242044.html This study examines temporal changes in seismicity and solar-geomagnetic parameters prior to moderate-to-large earthquakes in Iran during 2017-2018, with emphasis on the evolution of the Gutenberg-Richter b-value as a potential indicator of changing seismic regime. We selected five significant earthquakes for detailed analysis. While the study primarily focuses on main shocks with magnitudes around 6, the 2017 M 5.2 Malard (Tehran) earthquake was specifically included due to its proximity to the capital, affecting a population of over 10 million, and its status as the most significant instrumental event near Tehran in recent decades. Declustered earthquake catalogs from the Iranian Seismological Center (IRSC) were used to track b-value variations, while external parameters - including Earth’s magnetic field strength, solar wind proton density, geomagnetic indices including Disturbance Storm Time (Dst) and planetary K index (Kp), sunspot number, and 10.7 cm solar radio flux (F10.7 index) concerning fluctuations in b-value preceding major earthquakes - were analyzed at daily, hourly and 27‑day resolutions. In four out of five cases, the b-value showed a pronounced decrease within several weeks before the mainshock, indicating a temporary shift toward the dominance of larger-magnitude events, consistent with stress concentration patterns observed in prior studies. Four mainshocks also occurred when sunspot numbers and the F10.7 index were near their minimum values over the study period, whereas 27‑day averaged proton density tended to be relatively high close to the occurrence times. Moreover, the Kp index displayed low 27‑day averages for four events, and Dst and Kp indices showed localized anomalies within ±48 hours of some mainshocks. These patterns suggest possible temporal associations between seismic and solar-geomagnetic variability at monthly timescales, although no formal statistical tests were applied. However, the small number of events, uncertainties in the seismic and magnetic datasets and the absence of formal statistical testing limit the strength of any conclusions and preclude causal inference. The correlations identified here should therefore be regarded as preliminary and do not imply that solar or geomagnetic activity directly triggers earthquakes; rather, they suggest that solar-magnetic parameters may offer complementary contextual information on the evolving state of the crust when used alongside conventional seismotectonic indicators, motivating further studies with larger datasets and more rigorous statistical and physical modeling frameworks. https://www.ijgeophysics.ir/article_242149.html Missing intervals or unreliable well-log measurements pose a persistent challenge for subsurface characterization, particularly for curves critical to porosity, lithology, and velocity analysis. This study investigates two machine-learning strategies for reconstructing missing logs: a hybrid Multivariate Imputation by Chained Equations with Gradient Boosted Trees (MICE+GBT), and also Convolutional Neural Network (CNN). The prediction targets are three essential logs-neutron porosity (NPHI), bulk density (RHOB), and compressional travel time (DT). Each reconstructed artificial log from complementary measurements including resistivity, Photoelectric Factor (PEF), and spectral gamma-ray. A rigorous preprocessing workflow was applied, followed by evaluation under a well-level cross-validation scheme to simulate deployment on unseen wells. Performance was measured using Mean Squared Error (MSE), Mean Absolute Error (MAE), correlation coefficient (R), and coefficient of determination (R²).  This research utilizes two separate methods to estimate absent well logs in the Sarvak Formation, a diverse carbonate reservoir noted for intricate pore configurations and variability in lithology. These approaches tackle deficiencies in log data while assessing performance in regulated environments. In the following section, we will outline the dataset, prediction objectives, preprocessing steps, workflows, and evaluation methods. Results indicate that the MICE+GBT approach consistently outperforms Convolutional LSTM across all three target logs, particularly for intervals affected by washouts or tool measurement failures. Beyond statistical performance, the reconstructed curves preserved geological consistency in density–neutron and sonic–density trends, ensuring reliability for downstream reservoir interpretation. The findings demonstrate the practical benefits of adapting ensemble-based imputation methods to petrophysical data, providing a robust and interpretable framework for improving log data alliance in reservoir studies. Results demonstrated that the MICE+GBT workflow consistently delivered accurate, stable, and interpretable predictions across both Danan and Azadegan fields. Its strength was most evident in DT reconstruction, where ConvLSTM struggled with convergence and produced elevated error magnitudes. Ensemble models also yielded narrower error distributions and superior generalization across wells, underscoring their robustness in data-limited carbonate environments. Conversely, the ConvLSTM framework captured local depth-wise dependencies effectively, particularly for NPHI, and showed potential when sufficient training data were available. However, its sensitivity to heterogeneity and tendency toward error dispersion limit its reliability in reservoirs with strong lithological variability. Overall, the findings suggest that ensemble methods presently offer more dependable solutions for carbonate reservoirs with restricted datasets, while deep learning approaches hold promise for future applications given larger, more diverse training corpora. Integrating the interpretability and stability of ensemble models with the representational power of deep learning could form the basis of next-generation workflows for missing log prediction. https://www.ijgeophysics.ir/article_243045.html Dust storms represent one of the most critical environmental hazards in southwest Iran, particularly in Khuzestan Province, where both local and transboundary sources contribute to severe summertime air pollution. Among the atmospheric drivers of dust activity, the low-level jet (LLJ) is recognized as a key dynamical mechanism capable of enhancing surface wind stress, dust emission, and horizontal transport. Despite its importance, the quantitative linkage between LLJ variability and dust loading in this region has not been systematically investigated. In this study, the role of summertime low-level jets in modulating dust activity over Khuzestan during June–August (JJA) of 2021–2025 is examined using a combined analysis of ERA5 reanalysis and MERRA-2 aerosol products. LLJ events were identified based on dynamical constraints rather than sole reliance on wind speed maxima, ensuring discrimination between true low-level jets and background synoptic winds. Temporal and spatial relationships between LLJ occurrence and dust indicators were assessed using both optical (DUEXTTAU) and mass-based (DUSMASS) dust variables. Results show that LLJ frequently occurs at lower pressure levels (under 900hPa), 67% of these cases corresponds to dynamically meaningful LLJs penetrating toward the surface. On average, approximately 24% of summertime hourly dust observations coincide with LLJ events. Despite this limited temporal overlap, dust mass concentrations increase substantially during LLJ conditions: the mean DUSMASS under LLJ conditions is about 1.86 times higher than during non-LLJ periods, whereas the corresponding ratio for DUEXTTAU is notably smaller (~1.09). This contrast highlights the stronger sensitivity of near-surface dust mass to low-level dynamical forcing compared to column-integrated optical properties. Spatial analysis reveals pronounced heterogeneity in dust activity across the study domain. Dust hotspots identified using the 90th percentile of summer-mean dust distributions occupy only about 12% of the total spatial grid, indicating that severe dust activity is confined to limited source-prone areas rather than being spatially uniform. Furthermore, grid-cell-based spatial correlation analysis between monthly dust anomalies and 925 hPa wind speed anomalies demonstrates a marked difference between dust indicators. While DUEXTTAU exhibits near-zero mean spatial correlation with low-level wind speed and no areas of strong correlation, DUSMASS shows a moderate positive mean correlation (r ≈ 0.45), with approximately 78% of grid cells displaying positive correlations and nearly 38% exhibiting strong correlations (|r| ≥ 0.7). Overall, the results indicate that LLJs play a critical role in enhancing dust mass emission and transport in Khuzestan, while their influence on dust optical depth is weaker and more spatially variable. These findings emphasize the importance of low-level dynamical processes—particularly LLJs—in shaping the regional dust cycle and provide independent spatial and temporal evidence supporting the role of Khuzestan as a key contributor to dust activity in southwest Asia. https://www.ijgeophysics.ir/article_244197.html The site selection process for a geomagnetic observatory placement is essentially a rigorous effort to eliminate or minimize all possible sources contaminating observatory measurements now and for the foreseeable future. The performance of a geomagnetic observatory critically depends on the signal-to-noise ratio of the recorded magnetic field components. All EM noise sources that distort this ratio must be identified and considered in site selection. Site selection for geomagnetic observatories traditionally involves evaluating multiple environmental, geological, and infrastructural factors, but without classification, these criteria are often assessed ad hoc, leading to inefficiencies and suboptimal choices. Classification addresses these by enabling systematic, data-driven prioritization of criteria, reducing subjectivity, and facilitating scalable GIS-based multi-criteria evaluations across large regions. In this study, we first carry out a detailed review and classification of EM noise sources that affect geomagnetic measurements. These sources are grouped into three main categories: (a) artificial EM noise, (b) natural EM noise related to geological, crustal magnetic anomalies and atmospheric processes, and (c) natural hazards that threaten long-term observatory operation. Artificial EM noise includes power transmission and distribution networks (three‑phase high-voltage lines and single‑phase medium-voltage feeders), electrified railways and DC metro systems, buried metallic pipelines with cathodic protection, radio and radar communication systems, and electric security fences. For each of these sources, published case studies and theoretical considerations are used to describe the frequency content, dominant field components, amplitude decay with distance, and recommended minimum distances (e.g., tens of meters to several kilometers) required to avoid strong contamination of geomagnetic records. Natural EM noise and geophysical conditions are also analyzed as essential criteria. Topographic variations and associated charge accumulation on surface irregularities, spatial changes in crustal magnetic susceptibility inferred from airborne magnetic data, and large-scale atmospheric and ionospheric phenomena can all deform the EM field and reduce the interpretability of observatory data. Moreover, because one of the main tasks of a geomagnetic observatory is to provide long time series for studies of core dynamics, the site must remain stable over time and be protected from destructive geological processes. Therefore, active faults, seismic hazard zones, mass-movement–prone slopes, and other geohazards in Tehran Province are mapped and incorporated as exclusion or low-suitability zones.In the next step, these conceptual criteria are translated into spatial information layers for Tehran Province. Ten main layers are generated for artificial EM noise sources, including the locations of high-voltage transmission lines, oil and gas pipelines, railways and DC metro lines, cities and villages, active mines, industrial zones, power plants, and transformer substations. These data are compiled from national geological and mining databases and from open, collaborative geographic databases and are validated and edited using satellite imagery and GIS tools. Additional layers representing topographic slope derived from SRTM-based digital elevation models, surface water bodies, crustal magnetic anomalies, lithological units, active faults, and seismic hazard zones are also prepared. https://www.ijgeophysics.ir/article_243145.html Mount Pusuk Buhit is an active volcano around Lake Toba which has a height of 1980 meters above sea level. On Mount Pusuk Buhit there are geothermal manifestations in the form of hot water flows and fumaroles. What was carried out in this research was the identification of subsurface structures in geothermal manifestation areas using the GGMPlus satellite image gravity method. Based on the Sidikalang geological map sheet, in this study there are 5 types of rock formations, namely the Pusuk Buhit Formation (Qvpb), Kluet Formation (Puk), Kualu Formation (Mtks), Alluvium Formation (Qh) and Toba Tufa Formation (Qvt). Bouger anomaly values in this study ranged from -7.8 mGal to 6.0 mGal, low anomalies ranged from -7.8 mGal to -1.4 mGal. Moderate anomalies range from -1.0 mGal to 0.4 mGal. Meanwhile, the anomaly height in this study area ranges from 0.9 mGal to 6.0 mGal. The geothermal manifestation area on Mount Pusuk Buhit is located at a low anomaly. The depth of the residue map in this study is 520 meters while the regional depth is 1992 meters. From the results of this research, 22 faults were identified, namely 14 normal faults and 8 reverse faults. At this research location, the slicing results pass through 2 faults that are visible or detected on the surface based on the geological map of the research area. Slicing A passes through 1 fault while slicing B passes through 2 faults. In this research, to determine the existence of structures using the FHD (First Horizone Derivative) map, while to determine the type of structure found in the research area, use the SVD (Second Vertical Derivative) map. The results of slicing the FHD and SVD maps will then be processed in Microsoft Excel and an FHD and SVD graph will be created. To determine the existence of a curve structure, the FHD will show the maximum curve, while to determine the type of structure the SVD curve will pass through line 0. After obtaining the type of structure, the next process will be a 2D modeling was created. This 2D modeling will show a picture of the subsurface starting from the type of subsurface lithology found in the research area as well as the location of each previously identified fault. The second result of slicing in this research area passes through the geothermal manifestations of Mount Pusuk Buhit, Samosir Regency, North Sumatra. It is suspected that the appearance of manifestations on the surface is controlled by the existence of a structure directly beneath the manifestation. https://www.ijgeophysics.ir/article_244868.html The rainy season (RS) constitutes a fundamental component of the hydroclimatic system in arid and semi-arid regions, where water availability is highly sensitive to changes in precipitation timing and persistence. In a country such as Iran, where large areas are characterized by limited and irregular rainfall, variations in the onset, cessation, and duration of the rainy season can exert profound impacts on water resources management, agricultural productivity, food security, and ecosystem sustainability. Despite its importance, comprehensive assessments of long-term changes in rainy season characteristics across different spatial scales in Iran remain limited. The present study aims to investigate spatiotemporal variations in the timing of the rainy season—including onset, cessation, and length—as well as changes in the proportion of rainy season precipitation relative to annual precipitation. These variations are examined for both the widespread rainy season and regional rainy seasons across Iran, with a particular focus on contrasting their behavior during two distinct climatic phases: a relatively wet period (1960–1997) and a predominantly dry period (1998–2023). To achieve these objectives, daily precipitation records from 72 synoptic meteorological stations distributed across Iran were analyzed. The selected stations provide long-term observational records ranging from 32 to 63 years, ensuring adequate temporal coverage for detecting climatic shifts. Rainy season indicators were derived using consistent criteria applied across all stations, allowing for a robust comparison of temporal changes and spatial patterns. In addition, multivariate regression analysis was employed to assess the influence of key geographical factors—longitude, latitude, and altitude—on the spatial variability of rainy season timing. The results indicate pronounced changes in rainy season characteristics during the second climatic phase. Specifically, the onset of the widespread rainy season has been delayed in many regions, while its cessation has occurred earlier, leading to a statistically significant shortening of the rainy season length. These changes are most evident in the northeastern, southeastern, and parts of the southwestern regions of Iran. Furthermore, the explanatory power of spatial variables varies across the country, highlighting strong regional contrasts in the controls governing rainy season dynamics. At the regional scale, the rainy season has experienced reduced rainfall continuity, accompanied by an expansion of dry intervals within the season. The most substantial declines in continuity are observed in the northeastern and southeastern regions, which are already vulnerable to water scarcity. Overall, the findings reveal structural changes in the seasonal rainfall regime of Iran, reflecting modifications in the behavior of rain-producing atmospheric systems. These changes are consistent with the broader signals of recent climate change and global warming, underscoring the need for adaptive strategies in water resources planning, agriculture, and climate-resilient development. https://www.ijgeophysics.ir/article_243909.html Studying seismic response of rock mass has been in the limelight during the recent decades. In the present study, Kelvin-Voigt model is adopted to model rock material viscosity where a nonlinear shear criterion is also employed to include the sliding mechanism across the joints. The both are then incorporated in the time-domain recursive method (TDRM) for calculating transmission and reflection coefficients. In order to assess the outcomes, a linearly elastic shear model is separately employed in the code so that the results could be comparatively analyzed. In the developed code, dynamic shear stress of the joints is monitored at each time step with respect to the dynamic shear strength in a way that sliding occurs whenever the slipping criterion is satisfied over a range of quality factors. Results clearly show that slipping exerts a significant negative effect on the shear transmission coefficients while inducing the opposite on the reflection coefficients. It is also observed that joint spacing plays a dual role on the results as emboldening the viscosity effects on one hand while improving the stability of the joints against sliding, on the other. This phenomenon which is induced by the dynamic normal stress of the joints, is shown to be reduced to zero in the case of normal incidence angle where the dynamic shear strength solely arises from the initial static shear strength. It is also shown that the maximum shear transmission coefficient could occur in a specific quality factor wherein the sliding starts to happen. Studying seismic response of rock mass has been in the limelight during the recent decades. In the present study, Kelvin-Voigt model is adopted to model rock material viscosity where a nonlinear shear criterion is also employed to include the sliding mechanism across the joints. The both are then incorporated in the time-domain recursive method (TDRM) for calculating transmission and reflection coefficients. In order to assess the outcomes, a linearly elastic shear model is separately employed in the code so that the results could be comparatively analyzed. In the developed code, dynamic shear stress of the joints is monitored at each time step with respect to the dynamic shear strength in a way that sliding occurs whenever the slipping criterion is satisfied over a range of quality factors. Results clearly show that slipping exerts a significant negative effect on the shear transmission coefficients while inducing the opposite on the reflection coefficients. It is also observed that joint spacing plays a dual role on the results as emboldening the viscosity effects on one hand while improving the stability of the joints against sliding, on the other. This phenomenon which is induced by the dynamic normal stress of the joints, is shown to be reduced to zero in the case of normal incidence angle where the dynamic shear strength solely arises from the initial static shear strength. It is also shown that the maximum shear transmission coefficient could occur in a specific quality factor wherein the sliding starts to happen. https://www.ijgeophysics.ir/article_244937.html The Intertropical Convergence Zone (ITCZ), the ascending branch of the Hadley meridional circulation, plays a crucial role in the tropical and extratropical climate through the transfer of energy, momentum, and mass. The key factors contributing to the formation of the ITCZ include the convergence of trade winds, high sea surface temperatures (SSTs), and sufficient surface moisture. Although extensively studied, the ITCZ over the Indian Ocean has received comparatively less attention than extreme monsoonal rainfall events. Shifts in the location or intensity of the ITCZ can modulate the descending branch of the Hadley cell, driving meridional displacements of subtropical high-pressure systems. Accordingly, examining spatial fluctuations of the ITCZ and their impacts on the climate of countries bordering the Indian Ocean, Southwest Asia, and the southern Iranian Plateau is of considerable importance. This study employs the ERA5 reanalysis data at 0.25° spatial resolution in both latitude and longitude for the period 1983–2023. Analyses are conducted for two extreme months—February and July—as well as the seasons of summer (December, January, February) and winter (June, July, August) in the Southern Hemisphere. Meteorological parameters used in this study include convective precipitation, SST, horizontal wind vectors, temperature, dew point temperature, potential temperature, equivalent potential temperature, saturated equivalent potential temperature, and vertical velocity at different pressure levels. Using these variables, the position of the ITCZ in the Indian Ocean region was identified, then thermodynamic factors in the boundary layer and free atmosphere contributing to its formation were analyzed. Additionally, based on convective precipitation data, the latitudinal position of ITCZ occurrences was calculated, and its spatial distribution across the extreme months and seasons was determined. Results indicate that the highest probability of ITCZ occurrence—in terms of both intensity and spatial extent—is seen between 10°S and 12°S during the austral summer. Factors such as elevated moist static energy (equivalent potential temperature), the development of warm air masses accompanied by SSTs around 300 Kelvin or higher, and strong SST gradients—acting as enhancers of horizontal wind vectors—play significant roles in initiating deep convective processes. Moreover, the intensification of vertical velocity resulting from the convergence of horizontal wind vectors can act as a forcing mechanism on moist air parcels with sufficient moist static energy, leading to the formation of the ITCZ over the Indian Ocean. In addition to identifying ITCZ during the austral summer in the southern Indian Ocean, the study also highlights the occurrence of a double ITCZ during the austral autumn and winter, attributed to the symmetrical meridional distribution of convective precipitation in the western Indian Ocean region. Another notable finding is the formation of a stable cold oceanic tongue south of the convergence zone, characterized by an SST difference of about 1 Kelvin. Vertical profiling of variables such as equivalent potential temperature, saturated equivalent potential temperature, relative humidity, and vertical velocity clearly illustrates the contrast between the area influenced by the cold oceanic tongue and the surface convergence zone in terms of static stability. https://www.ijgeophysics.ir/article_245540.html The sex ratio at birth (SRB), defined as the proportion of male to total live births, is a sensitive demographic indicator that can be influenced by biological, environmental, and social factors. Previous studies have reported alterations in SRB following natural disasters, particularly earthquakes, potentially due to stress-related effects on reproductive physiology and fetal survival. However, most investigations have focused on short-term monthly changes in SRB, and evidence regarding the impact of prolonged earthquake sequences on annual SRB remains limited. This study examined whether a sequence of major earthquakes affected the annual SRB in Sarpol-e Zahab County, western Iran. Sarpol-e Zahab County experienced five earthquakes with moment magnitude greater than 5 and peak ground acceleration (PGA) exceeding 200 cm/s² between November 2017 and November 2018. Earthquake information, including magnitude, location, and PGA, was obtained from the Building and Housing Research Center of Iran. Annual birth data for Sarpol-e Zahab County, Kermanshah Province, and the entire country were obtained from the Statistical Centre of Iran for the period 1390–1401 Hijri Shamsi (2011–2023). SRB was calculated as the ratio of male to total live births. Annual SRB values were compared with the overall 12-year mean using one-sample t-tests. In addition, Google Trends data were analyzed to explore public behavioral responses and information-seeking patterns following the earthquakes. A significant decline in SRB was observed in Sarpol-e Zahab County during 1398 HS (2019–2020), the year following the earthquake sequence. The SRB decreased to 0.506 compared with the 12-year mean of 0.522 (p = 0.0004), representing the lowest value recorded during the study period. In contrast, no comparable reduction was observed in either Kermanshah Province or Iran as a whole, supporting a localized effect associated with the earthquake sequence. Furthermore, the annual number of births in Sarpol-e Zahab declined markedly after the earthquakes, with post-earthquake births averaging approximately 20% fewer than those recorded during the preceding seven years. Google Trends analyses demonstrated sharp increases in searches related to “earthquake,” “Red Crescent,” and “ceiling” following the major seismic events, reflecting heightened public concern regarding safety, structural damage, and disaster assistance. These behavioral indicators suggest sustained psychological and social impacts on the affected population. The findings indicate that the prolonged earthquake sequence affecting Sarpol-e Zahab County was associated with a significant reduction in annual SRB in the subsequent year. Unlike previous studies that primarily reported monthly fluctuations, this study demonstrates that extended exposure to disaster-related stress may influence SRB at an annual scale. The concurrent decline in birth numbers and increased disaster-related online search activity further support the broader demographic and psychosocial consequences of prolonged seismic events. These results contribute to growing evidence that major natural disasters can affect reproductive outcomes and population dynamics in affected communities. https://www.ijgeophysics.ir/article_245181.html Explicit representation of inertia–gravity waves in general circulation models is not yet feasible due to their small scale relative to the spatial resolution of the models. With enhancement of technology in recent decades, there has been a significant interest in applying machine learning (ML) to improve the performance of general circulation models (GCMs). This research investigates the performance of Convolutional Neural Network (CNN) as a non-parametric model in reconstructing nonorographic gravity waves in mid-latitude oceanic regions using the ERA5 reanalysis data. This data set with low-resolution of 2.5° × 2.5° for predictor variables and with high-resolution of 0.25° × 0.25° for predictand variables have been selected to quantify the characteristics of gravity waves. The explanatory variables employed at 15 pressure levels are representative of gravity waves sources. These variables include temperature gradient, potential vorticity, potential vorticity anomaly, relative vorticity, pressure vertical velocity, and horizontal wind velocity. On the other hand, for the gravity-wave activity in the lowermost stratosphere, the target variables have been chosen as the standard deviation of three key parameters: horizontal divergence, absolute momentum flux, and vertical velocity at the 100 hPa pressure level. Two approaches have been evaluated; the first approach examined the effect of training data volume while keeping the number of input channels constant at 150 channels, and the second one emphasizes optimizing the number of input channels while keeping the training period constant at three years. The second approach employed three distinct configurations characterized by varying numbers of input channels: 150 channels encompassing all explanatory variables, 15 channels restricted to horizontal wind velocity only, and 42 channels selected based on the most relevant explanatory variables in reconstructing gravity waves. Data from three years—2017, 2018, and 2020—were selected for the training and validation datasets, while data from 2019 were employed for the testing dataset. The results obtained from predicting the standard deviation of three target variables show that increasing the volume of training data in first approach is able to preserve the spatial structure and intensity of variables more effectively. Examining the results of the second approach showed that the model has low performance using 15 input channels, yet has comparable results with 42 and 150 input channels in expressing the importance of horizontal wind variable to predict target variables. The relatively similar results in the 42- and 150-channel combinations indicate the inherent capability of CNN in identifying less-important variables using appropriate activation function. Despite limitations in predicting extreme events, CNN can detect periods of gravity waves activity properly and potentially reconstructs inertia–gravity waves well. Despite the advantages of using CNN in gravity wave reconstruction, conducting more comprehensive experiments could prepare CNN for operational implementation within climate models. https://www.ijgeophysics.ir/article_246055.html Accurate probabilistic quantitative precipitation forecasting plays a vital role in effective water resource management, agricultural planning, and flood risk mitigation, especially in regions prone to extreme weather events. Although Ensemble Prediction Systems (EPS) serve as the primary tools for generating these probabilistic forecasts, their raw outputs often contain systematic errors, including inherent bias and ensemble under-dispersion. Such inaccuracies can lead to less reliable forecasts that undermine decision-making for emergency responses and infrastructure planning. To overcome these significant limitations, statistical post-processing techniques are widely applied to calibrate raw ensemble outputs and improve forecast reliability. In this study, 24-hour cumulative precipitation forecasts were analyzed for lead times of 24, 48, and 72 hours using two prominent post-processing approaches: Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics with a Censored Shifted Gamma distribution (EMOS-CSG). The dataset consists of precipitation forecasts generated by eight different configurations of the Weather Research and Forecasting (WRF) model over diverse synoptic stations across Iran, spanning the period from September 2015 to February 2016. A key focus of the research is to evaluate and compare two different parameter estimation strategies: a “global” model that uses data from all stations collectively and a clustering-based “semi-local” model that groups stations with similar climatological characteristics. Results indicate that raw ensemble predictions are poorly calibrated and tend to over-forecast. Post-processing provides substantial added value, particularly at moderate to high precipitation thresholds (> 5 mm to > 25 mm), as evidenced by increasing RSS. Reliability analyses reveal that optimal configurations are highly model-dependent: BMA requires a Semi-local approach to prevent severe under-forecasting at higher accumulations, whereas EMOS-CSG achieves optimal calibration using a Global configuration. In terms of discrimination and overall accuracy (AUC and Brier Score), Global configurations demonstrate a consistent numerical advantage over Semi-local ones. Furthermore, Brier Skill Score (BSS) evaluations highlight that while BMA_Global excels at the lowest threshold (> 0.1 mm), CSG_Global dominates at higher thresholds, maintaining positive skill where Semi-local methods often fail. However, analysis of 95% confidence intervals reveals that the numerical differences between Global and Semi-local approaches, as well as between the BMA and EMOS-CSG models, generally lack statistical significance. Ultimately, CSG_Global and BMA_Semi-local emerge as the most robust frameworks for reliable precipitation forecasting. It should be noted that the results are based on data from a single autumn–winter season (September 2015–February 2016). Therefore, the findings should not be assumed to generalize to year-round operational forecasting without further evaluation using multi-season datasets. https://www.ijgeophysics.ir/article_245508.html The study of sedimentation in ports and harbors is a serious topic in coastal engineering and oceanography studies which is annually accompanied by high dredging costs. Study of sediment transport and determination of deposition and erosion rates are fundamental studies in construction and development of ports. Numerical modeling of fine-grained and cohesive sediments transport and morphodynamics provides an effective tool for understanding sediment problems in coastal areas with fine-grained material. Study of sedimentary processes depends on the type of sediment. Due to this fact that many coastal zones have fine-grained or cohesive sediment, studying of this kind of sediment is important. Fine-grained marine sediments, due to their behavior, have certain complex characteristics compared to other marine sediments. This type of marine sediment can be transported in two phases: suspended load and bed load, each of which has different consequences and characteristics. In this study, a two-dimensional numerical model was used, which includes all processes related to the transport of marine cohesive sediments such as flocculation and its effect on the settling velocity of marine cohesive sediment particles, consolidation of bed layers and transport of marine sediments between layers, changes in bed shear stress under the influence of the simultaneous presence of waves and currents, bed morphology, deposition and erosion. To evaluate the transport of marine sediments, all processes were considered. To simulate fine-grained sediment transport, the main parts of the computational engine are an advection-dispersion engine and some experimental formulas related to cohesive sediment processes. This model determines the rate of erosion and deposition of cohesive sediment under different wave and current conditions. To verify the model performance, the model results were compared with the Finite Volume Coastal Ocean Model (FVCOM) results and field data reported in the Bushehr Bay simulation phase, a case study. In the performance section of the cohesive sediment module, the model results with and without applying morphological changes were compared with the FVCOM model results. The comparisons show that the model successfully simulated the hydrodynamics and cohesive sediment in the Bushehr Bay area in July 2010. The location chosen for comparing sedimentation between the two models is located exactly at the beginning of the outer channel. This location has high dynamics in terms of sedimentation and erosion. This is because the natural outlet path of Bushehr Bay intersects with the outer channel at this location, which results in more complex sedimentation patterns, including deposition and erosion. https://www.ijgeophysics.ir/article_246275.html The evaluation of local seismic response plays a fundamental role in post-disaster spatial planning, particularly in regions affected by landslides and characterized by soft sedimentary materials. This study analyzes the seismic response characteristics in a post-landslide area in Mendala Village, Brebes Regency, Central Java, to support the planning of permanent housing development. The Research area is situated within the Sedimentary Pliocene Formation, which is primarily composed of fine-grained clay sediments with intercalations of sandstone and limestone. These lithological conditions are widely recognized as sensitive to seismic-wave amplification and ground deformation under dynamic loading. Microtremor measurements were conducted at numerous observation points distributed across the planned residential development area. Ambient ground vibrations were recorded using a three-component seismometer under existing post-landslide conditions. The Horizontal-to-Vertical Spectral Ratio approach was applied to the recorded signals to determine the dominant frequency and amplification factor of the near-surface soil layers. These parameters reflect the dynamic behavior of subsurface materials and are commonly used as indicators of local seismic response. The derived parameters were subsequently integrated to estimate the seismic vulnerability index, peak ground acceleration, and ground shear strain, thereby enabling a comprehensive assessment of the site's seismic hazard potential. The analysis results reveal pronounced spatial variations in seismic response, even within the same geological unit. Zones characterized by low dominant frequency values and high amplification levels are mainly distributed in the northern to northeastern sectors of the study area. These conditions indicate the presence of thick, relatively soft sediment layers with pronounced impedance contrasts between subsurface strata. Such conditions contribute to increased seismic vulnerability and a higher potential for ground deformation. These areas are associated with elevated peak ground acceleration and moderate ground shear strain, suggesting the potential for surface cracking, differential settlement, and progressive degradation of shallow soil layers under seismic shaking. In contrast, the southwestern part of the study area exhibits higher dominant frequency values, lower amplification, and reduced seismic vulnerability. These characteristics reflect more compact sedimentary conditions and a more stable seismic response. As a result, this sector is considered relatively suitable for permanent housing development, provided that standard earthquake-resistant design principles are applied. The differences observed between sectors highlight the importance of site-specific seismic response evaluation in areas with heterogeneous sedimentary conditions. The integration of microtremor-based seismic parameters provides a practical framework for preliminary seismic response zoning and soil vulnerability assessment. This approach enables the identification of relatively safe zones and areas requiring additional engineering mitigation measures, such as soil improvement, foundation reinforcement, or structural design modifications. The results of this study represent the current ground conditions following a landslide event and are intended as an initial assessment to support risk-informed decision-making. Further detailed geotechnical investigations and site-specific engineering analyses are recommended before the final implementation of permanent housing construction. https://www.ijgeophysics.ir/article_247723.html Climate change and the intensification of its impacts have increased the need for accurate station-scale reproduction of precipitation and temperature. However, raw CMIP6 outputs, because of their coarse spatial resolution and systematic biases, require bias correction and a robust procedure for selecting appropriate model–method combinations. To address the gap of fragmented and single-variable evaluations in eastern Iran, this study proposes a station-based, multi-criteria framework for the simultaneous ranking of model–method combinations in reproducing precipitation, minimum temperature, and maximum temperature across the three Khorasan provinces (19 stations, 1989–2014). Within this framework, outputs from eight CMIP6 models (five ESMs and three GCMs) were evaluated using eight statistical bias-correction methods implemented in the CMHyd software (five methods for precipitation and four for temperature). The evaluation metrics included NRMSE, MAE, r, and KGE, together with variable-specific PBIAS for precipitation and MBE for temperature. After normalization, the criteria were weighted using Shannon entropy with regionally fixed weights, and station-level ranking was performed using TOPSIS. The results were then aggregated using median ranks, IQR, and the number of winning stations, and statistically assessed using the Friedman and Nemenyi tests. Performance differences among alternatives were significant for all three variables (Tmin: χ² ≈ 538, p ≈ 10⁻⁹⁴; Tmax: χ² ≈ 458, p ≈ 10⁻⁷⁷; Pr: χ² ≈ 286, p ≈ 10⁻⁴⁴). MPI|VarianceScaling and MPI|DistributionMapping were identified as the best regional options for Tmin and Tmax, respectively. Nevertheless, the Nemenyi test showed that 12 (Tmin) and 11 (Tmax) combinations were not statistically distinguishable from the top-ranked option, forming an excellent group characterized by convergent and stable performance indices. For precipitation, HadGEM|DistributionMapping ranked first and produced more accurate performance indices than other members of the excellent group. In contrast, many INM-, CMCC-, and GFDL-based combinations, together with some DeltaChange/LinearScaling and LocalIntensity/PowerTransformation methods, were significantly separated from the excellent group and occupied the lowest ranks. Accordingly, in the Khorasan region, MPI combined with Variance Scaling and Distribution Mapping, and HadGEM combined with Distribution Mapping, are the most stable options for reproducing temperature and precipitation, respectively. The proposed framework provides a practical basis for reducing structural uncertainty and supporting robust selection of climate model–bias correction combinations for climate change impact assessment and regional hazard management. https://www.ijgeophysics.ir/article_246723.html Seismic reservoir horizon picking is an active area of research, as it is one of the primary methods of interpretation. A problem with Machine Learning (ML) based methods is that they are prone to being trapped in local minima during optimization, leading to overfitting. This research mainly uses the Support Vector Machines (SVM) method to overcome overfitting and the complex mathematical operations of linear and nonlinear ML methods. We used eleven seismic attributes in our process; there is no need to define a point on the horizon to start hydrocarbon horizon tracking in a local window, which restricts the algorithm to window length and operator choice. Before feeding the input data to SVM, a Principal Component Analysis is performed as a dimensionality-reduction technique to improve efficiency and accuracy. Optimal horizon tracing requires tuning parameters, including regularization control C and the Gaussian kernel width (gamma). In most of the studied cases, the first parameter's effect was negligible for larger gamma values. A mediate-to-coarse Gaussian kernel is more useful for selecting the true horizon locations from multiple. While amplitude information is crucial for feature selection, it is also important to consider complementary information such as phase and frequency-related attributes. Our approach to learning and optimization showcases the robustness and reliability of our workflow for horizon picking. This is evident even in challenging areas with a low signal-to-noise ratio, similar to the outcomes achieved through deep learning in faulted zones, and it allows us to complete tasks in a relatively short amount of time. Seismic horizon picking, as one of the mainstays of seismic interpretation, has been a topic of discussion in the history of exploration seismology from the age of manual picking methods to the most automated ones. Because of the inherent characteristics of subsurface geologic boundaries and their corresponding sonic features, seismic horizon picking is a multi-dimensional problem. Lateral changes of the corresponding waveform, faults with high throws, unconformities, and signal-to-noise ratio are some of the factors that are responsible for the complexity of the task of horizon picking.The emergence of 3D seismic data acquisition along with complex data processing methods has made seismic data interpretation a time-consuming, labour-intensive and almost manually impossible task to do; therefore, automating procedures was felt necessary by seismic interpreters. Seismic horizon picking was one of the main tasks that have been automated during the last decades using various methods from cross-correlation to the most complicated ones. Machine Learning (ML) based methods have gained popularity for automation, and their ever-increasing development is tempting geophysicists to use them. In our view, ML can be one of the main methods to automate many interpretation procedures because of its inherent non-linearity and high flexibility to any problem that is coincident with the geophysical data. https://www.ijgeophysics.ir/article_247720.html Tourism is highly sensitive to climatic conditions because tourists’ comfort and travel decisions are influenced by heat and cold stress. This issue is particularly important in arid and semi-arid regions where strong seasonal and spatial thermal variations create both opportunities and constraints for tourism development. The Central Plateau of Iran, encompassing the provinces of Isfahan, Yazd, Kerman, and Fars, represents one of the country’s most important tourism regions due to its cultural heritage, desert landscapes, mountainous environments, and diverse natural attractions. However, climatic contrasts and topographic variability significantly affect thermal comfort and tourism suitability across the region. This study assessed the tourism potential of the Central Plateau of Iran using the Physiological Equivalent Temperature (PET) index during the period 2008–2017. Meteorological variables including air temperature, relative humidity, vapor pressure, wind speed, cloud cover, and mean radiant temperature (Tmrt) were prepared and analyzed. Because Tmrt is a key component of PET calculation, the RayMan Pro software was employed to estimate Tmrt and compute PET values. Standard human parameters were considered, including a height of 1.75 m, body weight of 75 kg, age of 25 years, clothing insulation of 0.9 clo, and an activity level of 32 W. To evaluate the influence of topography on thermal comfort, the relationship between PET values and station elevation was examined using linear regression and the coefficient of determination (R²) at monthly, seasonal, and annual scales. The results revealed a consistent inverse relationship between PET and elevation, indicating that increasing altitude reduces PET values and moderates heat stress conditions. The steepest negative slope (-0.01) was observed in May, reflecting the high sensitivity of bioclimatic conditions to elevation during the transition to the warm season. Furthermore, the highest explanatory power of elevation was recorded in March and May and during spring, with R² values reaching 0.69, demonstrating the strong influence of topography on the spatial distribution of thermal comfort. Although the explanatory power decreased slightly in summer, the relationship remained relatively strong, with an R² value of approximately 0.63. Seasonal PET patterns showed that winter was generally dominated by cold stress, particularly in high-elevation areas, whereas summer was characterized by moderate to strong heat stress in lowland regions. In contrast, spring and autumn provided the most favorable thermal conditions, with large areas falling within comfortable or slightly stressful thermal categories. Regional analysis highlighted substantial spatial variability, with Kerman exhibiting relatively stable and favorable thermal conditions, Yazd showing greater thermal variability, Isfahan being more affected by cold stress, and Fars experiencing higher summer heat stress. Overall, the findings demonstrate that tourism potential in the Central Plateau of Iran is strongly controlled by seasonal thermal conditions and elevation. Spring and autumn represent the most suitable periods for tourism activities, while high-elevation areas function as important climatic refuges during hot seasons. These results emphasize the importance of bioclimatic zoning and climate-sensitive tourism planning to enhance tourist comfort and support sustainable tourism development. https://www.ijgeophysics.ir/article_247002.html This study investigates long-term (1955-2022) changes in Sea Surface Temperature (SST) and upper-ocean Heat Content (OHC) in two adjacent but dynamically distinct basins: the semi-enclosed Persian Gulf and the open Gulf of Oman, connected to the Indian Ocean. Understanding heat storage mechanisms in basins with contrasting geometries is critical for regional climate predictions. Subsurface temperatures were extracted from the NOAA World Ocean Atlas 2023 at 0.25° resolution across seven decadal periods. Due to data continuity, analysis for the Persian Gulf was limited to 1955-2014, while for the Gulf of Oman it extended to 2022. SST data were obtained from the Climatic Research Unit at 1° resolution. OHC was computed for four depth intervals (0-25, 25-50, 50-75, and 75-100 m) using the standard thermodynamic equation, with linear trends estimated by Ordinary Least Squares. The findings reveal fundamental differences between the two basins. In the Persian Gulf, warming is strongly concentrated in the upper layers. The temperature trend in the 0-25 m layer is +0.052°C yr⁻¹, corresponding to an OHC increase of 1.62 W m⁻² per decade. With increasing depth, the warming rate diminishes rapidly, becoming weak below 75 m. This pattern reflects shallow depth, strong stratification, high evaporation, and limited mixing in this semi-enclosed basin. In contrast, the Gulf of Oman exhibits vertically coherent warming. Although the surface warming rate (0.024°C yr⁻¹) is half that of the Persian Gulf, positive temperature and OHC trends extend to 100 m depth. Lower temperature variability and high decadal correlations between SST and OHC across all depths indicate effective vertical mixing and strong connectivity with the Indian Ocean. Spatially, both basins exhibit a south-north OHC gradient, with maximum values in southern offshore regions and minimum values along northern coasts influenced by upwelling. Decadal correlations between SST and OHC in the Persian Gulf are high in upper layers but drop sharply below 75 m (r = 0.34), indicating decoupling of deep layers from surface signals. In the Gulf of Oman, correlations remain high (r > 0.9) throughout the water column. In conclusion, despite their proximity, these basins follow fundamentally different heat accumulation pathways. The Persian Gulf acts as an amplifier of short-term thermal variability with surface-focused warming, while the Gulf of Oman functions as a long-term heat reservoir with depth-penetrating warming. These differences, rooted in basin geometry and circulation regime, have direct implications for regional climate feedbacks, marine heat extremes, and sensitive ecosystems such as coral reefs. The study underscores the importance of analyzing both SST and OHC to understand ocean-atmosphere climate interactions in southwestern Asia. https://www.ijgeophysics.ir/article_247916.html Global warming has profoundly altered the global snow cycle, causing widespread reductions in snow cover, earlier snowmelt, and instability in seasonal timing. These transformations pose serious threats to water resources, food security, and ecosystem sustainability. In response, climate engineering particularly stratospheric aerosol injection (SAI), which mimics the cooling effects of large volcanic eruptions has been proposed as a potential complementary strategy to mitigate surface warming. This study evaluates the influence of the G6sulfur climate engineering scenario on key snow cycle variables, including surface snow mass, snowmelt flux, snow cover fraction, and snow depth. We analyzed outputs from six global climate models (CESM2-WACCM, CNRM-ESM2-1, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and UKESM1-0-LL) under the high-emission SSP5-8.5 scenario, both with and without the G6sulfur intervention. Future projections (2070–2099) were compared against baseline conditions (2015–2034). Under SSP5-8.5, snow variables exhibited significant declines: surface snow mass decreased by approximately 25%, snowmelt flux by 14%, snow cover fraction by 27%, and snow depth by 50%. Additionally, the timing of peak snow accumulation and melt shifted 1–2 months earlier relative to the baseline period. In contrast, the G6sulfur scenario substantially moderated these trends: snow mass loss was limited to ~11%, snowmelt flux to ~10%, snow cover decline to ~12%, and snow depth reduction to ~19%, while the temporal shift in peak occurrence was reduced to less than half a month. These findings demonstrate that climate engineering can effectively offset a portion of the adverse impacts of global warming on the snow cycle. However, climate interventions cannot fully reverse all hydrological changes, as residual warming and snowmelt dynamics continue to play a critical role in runoff generation at mid-to-high latitudes. Therefore, while SAI may serve as an effective complementary tool to partially restore hydrological conditions toward baseline levels, it cannot replace the primary strategy of reducing greenhouse gas emissions. https://www.ijgeophysics.ir/article_251189.html This case study aims at examining the waveguide formation by the subtropical jet (STJ) of the Northern Hemisphere and the way it may be influenced by the large-scale oscillations associated with ENSO, IOD, and QBO in the two selected months of February and November for the period 1940 to 2024. To this end, the refractive index diagnostic is employed to determine Rossby waveguides. Further, the formation of circumglobal Rossby wave trains is assessed using the variances of the bandpass filtered (2-6 days) meridional wind at 850 hPa pressure level. The analysis is guided by the principle that a strong, zonally oriented STJ can act as a waveguide. For both the formation of sufficiently strong STJ over the North Atlantic and the circumglobal wave trains, the establishment of the negative phase of the North Atlantic Oscillation (NAO) is shown to be essential. In this connection, a negative NAO helps to direct the transients in a zonal track across the southern part of North Atlantic. Results show that while moderate to strong El Niño events tend to provide the required negative NAO, in super El Niño events like those of 1982, 1998, and 2015, the positive NAO is developed and the circumglobal wave pattern is disrupted across the Atlantic sector. Also, when moderate to strong El Niño, the negative IOD and westerly shear of QBO all happen in the same month, especially in February, STJ is more likely to act as a zonal waveguide for Rossby waves. The formation of the circumglobal wave train could happen when only one of the three teleconnections was in favorable phase, as results for November 1995 suggest. In comparison between the two months, a stronger connection was found in February between the upper-tropospheric waveguide and the circumglobal wave train. This case study aims at examining the waveguide formation by the subtropical jet (STJ) of the Northern Hemisphere and the way it may be influenced by the large-scale oscillations associated with ENSO, IOD, and QBO in the two selected months of February and November for the period 1940 to 2024. To this end, the refractive index diagnostic is employed to determine Rossby waveguides. Further, the formation of circumglobal Rossby wave trains is assessed using the variances of the bandpass filtered (2-6 days) meridional wind at 850 hPa pressure level. The analysis is guided by the principle that a strong, zonally oriented STJ can act as a waveguide. For both the formation of sufficiently strong STJ over the North Atlantic and the circumglobal wave trains, the establishment of the negative phase of the North Atlantic Oscillation (NAO) is shown to be essential. In this connection, a negative NAO helps to direct the transients in a zonal track across the southern part of North Atlantic. https://www.ijgeophysics.ir/article_252698.html In the challenging world of wave propagation on materials, models built to describe phenomena on irregular inhomogeneous impedance boundaries are much rare in the literatures despite the huge importance they generally play in the transfer or deciphering of information from one system to another. Consequently, this study conceptualizes a wave-mechanics for the dispersion equation of Rayleigh wave in a rotating inhomogeneous material under magnetic effect and impedance-irregular boundary conditions which underscores scenarios of dispersion equations for homogeneous impedance and inhomogeneous impedance characterizations of the modeled problem. The irregular condition at the boundary of the rotating inhomogeneous material under magnetic effect is wavy (corrugated or grooved) in nature. This wavy or grooved-like phenomenon is expressed mathematically as a trigonometric series at the boundary of the material. Thus, using the stress-strain relation of a fibre-reinforced material and the Maxwell’s principle of electromagnetism, the dynamic equations for the model is derived. Through these, dispersions relations for both the homogeneous impedance and inhomogeneous impedance for an inhomogeneous fibre-reinforced material are derived analytically and presented graphically, especially after utilizing the normal mode solution approach whilst deriving, also, the fields’ distributions of the stresses and displacement components of the wave on the material. In addition, these graphics illustrates the impact of the contributing physical parameters of rotation, irregular parameters (wave number and amplitude of grooved boundary), inhomogeneity and magnetic field on the two dispersions relations; homogeneous impedance and inhomogeneous impedance of the fibre-reinforced inhomogeneous material, respectively. We observe that both dispersion relations were impacted by the contributing physical parameters. While the rotation of the medium decrease the dispersion relations when increased, the magnetic fields tend to push both dispersion relations in an upward trend when increased. The impact of the horizontal and vertical impedance on both dispersion relations cause a near resistant-like effect on the propagating wave on the inhomogeneous fibre-reinforced material. However, the dispersion relation due to the homogeneous impedance tend to have reduced amplitude of dispersion of the wave on the rotating grooved inhomogeneous material under magnetic effect as compared with the dispersion relation occasioned by the inhomogeneous impedance characterizations on the inhomogeneous fibre-reinforced medium. This work should be invaluable in some engineering applications where Rayleigh wave propagation could be utilized for non-destructive testing and the construction of actuators and sensors. Moreover, utilization of surface waves with some rotation and magnetic field play a host to influence reinforced materials such as in the forging and failure analysis of rotating machinery components especially in rotor blades, etc. https://www.ijgeophysics.ir/article_252699.html Ground-penetrating radar (GPR) is a geophysical imaging technique used to provide information about subsurface conditions without damaging the subsurface. In this research, subsurface conditions and detecting buried anomalies will be investigated in an old heritage building currently suffering from structural defects. This building is located in Baghdad, Iraq, in a challenging environment characterized by saline groundwater and highly conductive soil. A Mala GPR system equipped with a 250 MHz antenna was used to perform a systematic grid scan, resulting in two-dimensional images of the subsurface layers. The collected data underwent further processing through gain adjustment, noise reduction, and bandwidth selection. Radar data processing revealed significant subsurface features, such as an anomaly at a depth of approximately 1.9 meters and a pipe, presumed to be approximately 0.3 meters in diameter, buried at a depth of 0.4 meters. Areas with weak signal responses were identified, indicating the presence of voids caused by moisture or localized subsurface degradation. The application of signal processing increased the signal-to-noise ratio by 14.4 dB, which improved the interpretation of the collected data, despite the limited penetration depth of approximately two meters due to the high salinity of the soil. The results demonstrated the effectiveness of ground-penetrating radar in the non-destructive assessment of heritage buildings Ground-penetrating radar (GPR) is a geophysical imaging technique used to provide information about subsurface conditions without damaging the subsurface. In this research, subsurface conditions and detecting buried anomalies will be investigated in an old heritage building currently suffering from structural defects. This building is located in Baghdad, Iraq, in a challenging environment characterized by saline groundwater and highly conductive soil. A Mala GPR system equipped with a 250 MHz antenna was used to perform a systematic grid scan, resulting in two-dimensional images of the subsurface layers. The collected data underwent further processing through gain adjustment, noise reduction, and bandwidth selection. Radar data processing revealed significant subsurface features, such as an anomaly at a depth of approximately 1.9 meters and a pipe, presumed to be approximately 0.3 meters in diameter, buried at a depth of 0.4 meters. Areas with weak signal responses were identified, indicating the presence of voids caused by moisture or localized subsurface degradation. The application of signal processing increased the signal-to-noise ratio by 14.4 dB, which improved the interpretation of the collected data, despite the limited penetration depth of approximately two meters due to the high salinity of the soil. The results demonstrated the effectiveness of ground-penetrating radar in the non-destructive assessment of heritage buildings https://www.ijgeophysics.ir/article_248012.html Northern Tehran is situated within a tectonically active domain and ranks among the most seismically vulnerable regions of the country. The Mosha-Fasham Fault constitutes one of the principal seismogenic sources affecting this area. The study area of this research extends between longitudes 49°–54° E and latitudes 35°–37° N, within the Central Alborz Mountains. It is located in the southern part of the Alborz range, north of Tehran Province, and is characterized by mountainous topography, steep slopes, and deeply incised valleys. Owing to its particular tectonic setting, this area is among the most critical zones for seismotectonic and geomorphological investigations. Its geotectonic setting, structural architecture, and documented history of activity play a fundamental role in seismic hazard assessment for the capital. The high population density of Tehran further amplifies its seismic risk and potential socio-economic consequences. Accordingly, investigating temporal variations in seismicity, together with the geometric, kinematic, and present-day structural relationships of active seismogenic sources—particularly along the central and eastern segments of the fault system—is essential for deciphering the active tectonic regime and refining probabilistic seismic hazard evaluations. Given the considerable seismic potential of the Mosha-Fasham Fault, this study focuses on characterizing its seismic behavior and the prevailing stress regime. Seismicity analysis was conducted through estimation of maximum expected magnitude (M_max) and examination of spatiotemporal variations in the parameters of the Gutenberg-Richter frequency-magnitude relationship. The results indicate that temporal fluctuations of the b-value between 1990 and 2023 along the Mosha Fault zone reveal a systematic decrease in the b-value preceding moderate to strong earthquakes. Temporal variations of the b-value indicate that this parameter reached one of its relative minima by the end of 2023, which may reflect increased stress accumulation and a higher probability of larger-magnitude earthquakes. Such reductions in the slope of the Gutenberg-Richter distribution are interpreted as indicative of stress accumulation along the fault plane and increasing likelihood of forthcoming moderate to large seismic events. Spatial distribution maps of the b-value further demonstrate clustering of seismic events within zones characterized by reduced b-values. Estimation of maximum magnitude shows that the Gibowicz-Kijko-Bayes, Kijko-Sellevoll, Tate-Pisarenko, and non-parametric Gaussian approaches consistently yield M_max ≈ 7.6, whereas the Gaussian non-parametric method provides a slightly lower estimate of M_max ≈ 7.1. Overall, these findings provide critical insights into the seismotectonic behavior of the Mosha Fault system, highlighting the diagnostic significance of b-value variations and maximum magnitude estimates for assessing the probability of large seismic events and improving seismic hazard appraisal in northern Tehran; Therefore, incorporating these results into policy-making, urban planning, and seismic risk reduction strategies in the surrounding areas of this fault is essential. https://www.ijgeophysics.ir/article_248211.html Earthquakes are among the most significant triggering factors for landslides in susceptible regions, often affecting vast areas surrounding the epicenter. Landslides pose serious threats to human lives, infrastructure, and economic assets, making their identification and systematic study a critical necessity. On November 9, 2021, a dual earthquake sequence with moment magnitudes of 6.4 and 6.0 struck northwest of Bandar Abbas, southern Iran, affecting approximately 5,000 km². This region is particularly suitable for landslide investigation due to the presence of the Sirjan-Bandar Abbas highway, railway lines, numerous villages, and a densely populated area exposed to high landslide risks. This study aims to identify and analyze landslides triggered by the 2021 Bandar Abbas dual earthquakes using Synthetic Aperture Radar Interferometry (InSAR) and RGB color compositing techniques. Surface displacements were investigated through processing interferograms and coherence maps derived from satellite radar images acquired before, during, and after the earthquake events. The RGB color compositing method was further applied to enhance the visual detection of landslide-affected areas. To validate the InSAR and color compositing results, a comprehensive landslide susceptibility map was developed. This susceptibility map integrates multiple conditioning factors, including elevation, slope angle, aspect, topographic form, structural resistance, distance to active faults, distance to roads, distance to drainage networks, and vegetation cover. To ensure high accuracy in the susceptibility mapping, most of these thematic layers were generated using photogrammetric techniques based on historical aerial stereo image pairs acquired prior to the earthquake. The Structure from Motion (SfM) method was employed to extract detailed topographic and geomorphological information from these archival images. This approach provided a pre-earthquake baseline, allowing for a more reliable assessment of earthquake-induced changes. The final landslide inventory was compiled by cross-referencing the results from InSAR analysis, RGB color compositing, and the susceptibility map, followed by visual interpretation of pre- and post-event high-resolution imagery available in the Google Earth archive. Through this integrated methodology, a total of 24 debris slide-type landslides were successfully identified across the study area. The spatial distribution analysis reveals that the majority of these landslides are concentrated in high-risk zones, particularly in close proximity to road networks, river channels, and residential areas. Furthermore, nearly all identified landslides occur on slopes exceeding 20 degrees, indicating a strong topographic control on slope instability under seismic triggering. The findings of this study demonstrate that the combined use of InSAR, RGB color compositing, and SfM-based photogrammetry provides a robust framework for post-earthquake landslide detection, even in the absence of pre-event LiDAR or high-resolution topographic data. The resulting landslide inventory and susceptibility map can support hazard mitigation planning, infrastructure protection, and risk management in earthquake-prone mountainous regions. https://www.ijgeophysics.ir/article_248322.html Understanding the relationship between large-scale atmospheric circulation patterns, climate memory, and drought variability is critical for improving drought forecasting. This study aims to predict drought conditions in Khuzestan Province by integrating climate teleconnection indices and machine learning methods—specifically the Random Forest (RF) algorithm—while evaluating the impact of climate memory across various timescales. Monthly precipitation and air temperature data from 13 synoptic meteorological stations across Khuzestan Province were obtained from the Iran Meteorological Organization for the period 1982 to 2024. These data were used to calculate the Standardized Precipitation Evapotranspiration Index (SPEI) at four timescales (1, 3, 6, and 12-month periods) to represent short-term to long-term drought variability. To investigate the role of large-scale climate drivers, 37 climate variables—comprising teleconnection indices and synoptic ocean-atmosphere components—were incorporated as predictors into the Random Forest (RF) model, employing two time-lag scenarios: Lag1-3 and Lag1-6. Before implementing the RF model, a feature selection process was conducted to identify the most suitable input variables. The Variance Inflation Factor (VIF) was used to mitigate multicollinearity among predictors, ensuring that only the most influential and independent variables were retained. The dataset was then partitioned into two subsets: 80% for model training and 20% for testing and validation. Model performance was evaluated using the coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results demonstrated that prediction accuracy improves significantly with increasing SPEI timescales. The evaluation metrics (R², MAE, and RMSE) consistently confirmed that the best performance was achieved at the medium- and long-term drought scales (SPEI-6 and SPEI-12), with an R² of 0.426. A comparative analysis of two input lag scenarios (Lag1-3 and Lag1-6) revealed that while performance differences were negligible at short timescales, the Lag1-6 scenario exhibited higher predictive accuracy for medium- and long-term droughts, underscoring the significance of climate memory exceeding three months in the region. Furthermore, the RF model successfully reproduced the dominant spatial patterns of drought across Khuzestan Province with reasonable accuracy. Spatial distribution maps, derived from observed and predicted SPEI values using the Inverse Distance Weighting (IDW) interpolation method, indicate that the northern and northeastern parts of the province (including Safiabad, Dezfol, and Izeh) generally experience relatively more humid conditions. In contrast, the central and southwestern regions—particularly the Ahvaz–Bostan–Abadan axis—are characterized by lower SPEI values and more severe drought conditions. However, the RF model tended to dampen the range of variability during extreme events, leading to a slight underestimation of very severe drought periods. Additionally, the findings revealed that drought drivers vary depending on the timescale. At shorter timescales, atmospheric dynamical fluctuations, such as wind speed anomalies, play a more significant role. At longer timescales, large-scale teleconnection indices, specifically the El Niño–Southern Oscillation (ENSO) and DMI, become the dominant factors explaining drought fluctuations. Overall, this study demonstrates that integrating large-scale climate drivers, climate memory, and advanced machine learning provides an efficient framework for drought forecasting in Khuzestan Province, supporting improved water resource management and climate risk mitigation in southwestern Iran. https://www.ijgeophysics.ir/article_250767.html Introduction: In recent years, the water crisis and the depletion of water resources have become one of the main challenges in natural resource management in the country. Kermanshah Province has not been exempt from this trend and is facing a significant decline in groundwater resources and a critical situation regarding these resources. Statistical analyses indicate that out of the 23 study areas in the province, at least 13 plains are in a prohibited status, and two plains are in a critically prohibited status. The plains of Sahneh, Kangavar, Chamchamal, Mianrahan and Sonqor are among the areas where the critical status of groundwater resources is clearly evident. This concerning situation further underscores the necessity of a detailed study of the region's hydrogeological conditions. Material & Method: In this context, this research investigates land subsidence in five plains of Kermanshah Province (Sahneh, Kangavar, Chamchamal, Mianrahan, and Sonqor) using the Persistent Scatterer Interferometry (PSI) radar technique over the period from 2015 to 2023. To validate the results, data from the Global Navigation Satellite System (GNSS) and field observations were employed, and the regression coefficient and RMSE between GNSS and PSI data was calculated. Result and Discussion: Validation results showed that the regression coefficient between GNSS and PSI data for the Sonqor and Sahneh plains was 0.76 and 0.75, respectively. Furthermore, field visits to areas exhibiting subsidence provided corroborating evidence of land subsidence occurring in the studied plains and confirmed the accuracy of the research output. The findings also highlighted that the highest subsidence rate during the study period occurred in Kangavar Plain (10–20 mm), while the lowest rate was observed in Sonqor Plain (3–5 mm). Chamchamal Plain, with subsidence of 14–16 mm in its central and eastern areas; Sahneh Plain, with a maximum of 15 mm subsidence in its southern and central regions; and Mianrahan Plain, with 9 mm subsidence in its central and southern zones, ranked next in terms of subsidence magnitude. Additionally, the results confirmed a direct relationship (with a regression coefficient of 0.71) between groundwater level decline and subsidence rate across all studied plains. Furthermore, high density of exploitation wells, the presence of illegal wells, and the thickness of alluvial deposits were identified as factors influencing the intensity of subsidence. Conclusion: The results of this study demonstrate that the use of modern remote sensing techniques, particularly the PSI technique, can play a key role in identifying and monitoring vertical ground movements caused by subsidence due to groundwater depletion. Ultimately, the findings of this research can assist planners and decision-makers in the fields of water resources and environmental management to adopt effective management measures (based on a more precise understanding of subsidence hazards) to mitigate the subsidence trend and preserve groundwater resources in the studied plains. https://www.ijgeophysics.ir/article_251263.html We investigate the shallow crustal structure in NW Iran by utilizing the vertical-component of P waves derived from the autocorrelation of ambient seismic noise time series recorded by a temporary broadband seismic network that covers the southwestern Caspian coastal region, the western Alborz and Tarom mountains, and the Zanjan-Abhar region which lies on the northern margin of central Iran. The autocorrelation time series at each seismic station contain reflection pulses from seismic discontinuities. We use Bayesian statistics to invert the autocorrelation series into 1-D P-wave velocities for each of the stations. For this purpose, we employ the Markov Chain Monte Carlo method along with the Delayed Rejection Adaptive Metropolis algorithm (DRAM) to draw samples from a probability distribution of model parameters. A seismic image based on the station velocity models along a profile in NW Iran is presented. The image reveals the shallow crustal discontinuities and sedimentary cover structure of the study area and we interpret them in terms of the tectonic setting and geodynamic evolution of NW Iran during the Cenozoic. Our results suggest that the Tarom Valley adjacent to the Alborz range is covered with low-velocity sediments with a thickness of about 5 km. At the same time a thinning of higher-velocity layers beneath the shallow sediments is observed. This structure is consistent with the reported extension of the crust in the Tarom Valley during the Neogene, and confirms that the formation of the valley has a tectonic origin. In the southwestern margin of the South Caspian Basin, the thickness of the youngest sedimentary layers ranges from 8 to 12 km (i.e., Pliocene-Quaternary deposits), sitting on top of older sequences of 10 to 12 km in thickness. The sediment cover progressively thickens from the foothills of the Alborz towards the Caspian coast. These results imply significant sediment accumulation in the Caspian basin and a pronounced structural contrast with the neighboring region of Alborz and Talesh mountains. In the southern part of the study area, the Soltanieh and Tarom mountains both exhibit uplift of the upper crustal layers down to 10-15 km depth range. The uplift of these layers indicate the dominance of crustal compressional regime in the latter part of the Cenozoic in the Alborz and the northern margins of central Iran. The velocity model shows the underthrusting of central Iran under the Alborz along the Manjil Fault. At the same time no clear indication of underthrusting of the Caspian block southward under the northern Iran is discernible. https://www.ijgeophysics.ir/article_251349.html The Eynali mountain range, a prominent natural and cultural heritage site in Tabriz, faces significant geological challenges regarding the structural stability of its historic access routes. This study aims to conduct a comprehensive assessment of the structural stability at the field scale (1:10,000) and seismic response at the tectonic scale (1:25,000) for the two primary hiking and access paths leading to the Eynali site. Given the site’s proximity to the North Tabriz Fault (NTF), understanding the seismic susceptibility and structural integrity of these paths is crucial for developing effective mitigation, structural reinforcement strategies, or identifying safer alternative routes to preserve the site’s heritage. To achieve this, the research employs a multi-faceted methodological framework. Initially, a detailed identification of structural components—including fault geometries and fracture networks—was conducted along both access routes. A critical component of this data acquisition involved the Ultrasonic Pulse Velocity (UPV) Test, which provided reliable quantitative indicators of the physical and mechanical properties of rock masses along the paths. These laboratory-derived metrics were integrated with spatial geostatistical analyses, specifically utilizing variogram analysis of recent seismic event data. These datasets were subsequently normalized based on the tectonic scale of the study area to ensure consistency. The structural measurements covered in-situ rock blocks located in the walls and along the path floors to develop a high-resolution map of the structural positioning. This field-based qualitative validation approach was designed to bridge the gap between sparse data points and large-scale structural interpretations, significantly reducing the time and costs associated with extensive field surveys while ensuring the reliability of the seismic response models. The final analysis of structural and seismic instability reveals significant variations between the two routes. The results indicate that the highest risk of instability for Route 1 is primarily concentrated in the middle segments. In contrast, Route 2 exhibits the highest risk of instability in both the initial and middle segments. These findings are strongly correlated with the kinematics of active transverse fault zones associated with the North Tabriz Fault (NTF) system and the presence of loose rock blocks that are susceptible to displacement under seismic loading. The outcomes of this research provide a robust evidence-based foundation for decision-makers and urban planners. By identifying specific high-risk segments, the study advocates for targeted structural reinforcements or realignment of the access paths. Ultimately, this approach not only enhances the safety of visitors and the integrity of the Eynali heritage site but also provides a scalable model for seismic risk assessment in similar mountainous terrain situated in seismically active regions. https://www.ijgeophysics.ir/article_253433.html Following the preparation of fault-zonation maps for the city of Tehran, the first revision of the Tehran surface faulting hazard regulations was issued in 2020. In these regulations, hazard level is defined on the basis of empirical relationships as a function of fault length, such that faults shorter than 10 km are assigned a low hazard level. By presenting examples of severe deformation associated with short faults within Tehran, most of which are less than 3 km in length, the present study highlights the potential surface-faulting hazard posed by some of these short faults. Field surveys, trench investigations, and aerial photograph analysis were employed to examine the characteristics of these faults. According to internationally recognized codes and guidelines, faults are classified into three categories based on the nature of deformation within the fault zone: Well-defined, Distributed, and faults with uncertain deformation characteristics. Faults exhibiting Well-defined deformation commonly generate severe ground deformation and are therefore associated with higher levels of hazard. Our findings indicate that zones of intense deformation along short faults are not controlled by faulting mechanism and may develop under a range of kinematic settings. The focal mechanisms of microearthquakes and small-magnitude earthquakes in the Alborz region, together with regional geological investigations, indicate that compression and strike-slip faulting are the dominant deformation styles among the active faults of the study area. Accordingly, short normal faults and short right-lateral strike-slip faults cannot be interpreted as segments of a major seismogenic fault; rather, they should be regarded as subsidiary faults associated with a principal fault. In the regulations developed for the enforcement of construction controls within Tehran’s fault zones, the probability of surface rupture hazard is defined solely as a function of fault length. Under this framework, even short faults with high potential for surface rupture are classified within the “very low” hazard category. In practice, such faults are excluded from the regulatory framework; consequently, all buildings and urban infrastructure located on or across them remain without any specific technical measures for hazard mitigation. This study further demonstrates that, although fault rupture and displacement are generally correlated with fault length in earthquake-generating faults, subsidiary faults, particularly secondary faults, do not necessarily conform to this relationship because of their distinct kinematic characteristics. Despite their limited length and irrespective of conventional length-displacement scaling relationships, such faults may generate zones of intense deformation with displacements exceeding 1 m and, in some cases, reaching several meters. Therefore, given that the current regulations for Tehran define the probability of surface faulting hazard exclusively as a function of fault length, with hazard level decreasing as fault length diminishes, these provisions require reconsideration in future revisions. In this study, ArcGIS software was used for the preparation of base maps, digitization of faults and alluvial deposits, georeferencing of aerial photographs, and generation of photomosaics. In addition, graphical interpretations, figures, and maps were produced using Surfer, while FaultKin was employed for the kinematic analysis of fault planes, fractures, and bedding surfaces. https://www.ijgeophysics.ir/article_253595.html The Mw 5.5 Karakanja earthquake, which occurred on 24 January 2020 in Tajikistan, is a significant moderate seismic event within the tectonically active Pamir region. This area is characterized by the complex oblique convergence between the Indian and Eurasian plates, leading to intricate deformation patterns. This study comprehensively investigated the coseismic deformation field and detailed earthquake source parameters of the Karakanja earthquake using high-resolution Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data and a sophisticated Bayesian inversion methodology. Interferograms from ascending and descending satellite tracks revealed surface displacements along the satellite’s line-of-sight (LOS), with maximum values of approximately 4.7 cm (ascending) and 6.7 cm (descending). These measurements highlight substantial ground deformation concentrated around the causative fault. Applying the Bayesian inversion technique to these InSAR data allowed us to define the geometry and slip distribution of the causative fault. The inversion results indicate a preferred shallow fault plane with a modeled fault area of approximately 23 km². Key parameters include a centroid depth of 6.2 km, a strike of 29°, and a dip of 32.7°. Analysis of slip components revealed a dominant dip-slip component of 0.46 m and a strike-slip component of 0.15 m. This suggests a predominantly reverse faulting mechanism with a minor right-lateral component (rake angle ~72°), aligning well with the regional tectonic regime of oblique convergence. To rigorously assess model quality, statistical and spatial analyses of residuals were performed. The inversion yielded a root mean square error (RMSE) of 5 mm and an R² value of 0.92, indicating the model explains approximately 92% of the observed deformation. Residual analysis confirmed the absence of systematic bias, with high correlation (ρ=0.96) between observed and modeled displacements. Spatial autocorrelation analysis (Moran’s I = 0.14, p < 0.001) revealed weak but significant spatial autocorrelation in residuals, suggesting that while the uniform-slip model captures the main deformation pattern, it acknowledges minor small-scale complexities or residual atmospheric effects. Geostatistical analysis showed a spatial structure in residuals near the fault, indicating that the uniform-slip model inadequately represents local complexities. The estimated seismic moment was 2.25 × 10¹⁷ N·m, with a stress drop of ~5 MPa. These values align with empirical scaling relationships for continental crustal earthquakes. The modeled fault geometry and slip mechanism are fully consistent with the regional oblique convergence tectonics of the Pamir region. This study demonstrates the effectiveness of Bayesian inversion for determining source parameters of moderate earthquakes, while employing a multi-level validation framework (including spatial residual analysis and comparison with empirical relationships) to assess uncertainty and the "preferred" nature of the fault plane. https://www.ijgeophysics.ir/article_253597.html Multiple attenuation of oceanic seismic signals is a critical step in enhancing the quality, resolution, and interpretability of subsurface images. Surface-related multiples, generated by repeated reflections of seismic waves at the water surface, often interfere with primary reflections, producing noise and potentially distorting significant geological features. These multiples, if not properly identified and suppressed, can lead to inaccurate interpretations, reduce the reliability of seismic analyses, and hinder effective exploration and mapping of subsurface structures. Therefore, accurate detection, prediction, and attenuation of surface-related multiples are essential for producing high-quality seismic datasets that can be reliably used in geological and geophysical research, reservoir characterization, and resource assessment. The most commonly used techniques for suppressing surface-related multiples are Surface-Related Multiple Elimination (SRME) and Shallow Water Demultiple (SWD). SRME is a data-intensive approach that models and predicts surface-related multiples directly from recorded seismic data, without requiring detailed knowledge of subsurface structures. This method is particularly effective in complex geological settings and deep-water environments, where multiple generation is irregular, unpredictable, and highly variable. SWD, in contrast, is specifically optimized for shallow water conditions and exploits the unique wave propagation characteristics of these environments to efficiently suppress multiples. Both techniques possess distinct advantages and limitations, and their effectiveness depends on factors such as water depth, acquisition geometry, and the physical and geometrical characteristics of the recorded data. This study focuses on three-dimensional (3D) marine seismic data, providing a detailed review of the mathematical models, algorithmic frameworks, and performance of both SRME and SWD. Key operational parameters, including filter lengths, operator design, adaptive weighting, and iterative processing strategies, are carefully optimized to maximize multiple attenuation while preserving the fidelity of primary signals. Comparative analyses on real 3D datasets indicate that a well-integrated application of SRME and SWD significantly outperforms the use of either method individually. This combined approach not only enhances the signal-to-noise ratio but also improves the overall interpretability of seismic images, enabling more accurate identification and mapping of subsurface geological features. Such precision is vital for exploration, reservoir evaluation, and informed decision-making in marine environments. Overall, this research provides a practical and detailed guide for geophysicists, seismic data processors, and exploration professionals on how to effectively integrate SRME and SWD methodologies. Implementation of these strategies improves the final quality of seismic data, reduces artifacts caused by surface multiples, and enhances the reliability of subsurface interpretations. By adopting this recommended workflow, practitioners can achieve more precise seismic imaging, make better-informed exploration and development decisions, and fully utilize high-quality 3D marine seismic data for both scientific and industrial purposes. The study underscores the importance of comprehensive multiple attenuation workflows as a fundamental step toward producing reliable, interpretable, and high-fidelity seismic results in complex marine environments. https://www.ijgeophysics.ir/article_254397.html Seismic data are often contaminated by random noise, which reduces the signal-to-noise ratio (SNR), obscures weak reflections, and complicates interpretation and inversion. Effective noise attenuation is essential for improving the quality and reliability of seismic analysis. The Time-Frequency Peak Filter (TFPF) is an effective method for suppressing random noise without requiring prior knowledge of wavelet phase or an explicit velocity model. However, conventional TFPF uses a fixed analysis-window length, creating a trade-off between noise suppression and signal preservation. A long window can attenuate noise effectively but may distort reflection amplitudes, weaken high-frequency components, and reduce the resolution of closely spaced events. A short window preserves local signal details more effectively but can leave substantial residual noise. Because these characteristics vary throughout a seismic record, a fixed window may not be suitable for all regions. This study proposes an adaptive TFPF framework guided by the local Hurst exponent, which measures long-range dependence and temporal persistence. Coherent seismic reflections generally show greater continuity and persistence, whereas random noise tends to exhibit less predictable behavior. The local Hurst exponent is estimated using an optimized sliding-window scheme, allowing the method to track changes in data characteristics across the record. A transitional-zone correction is also applied to address ambiguity in areas where signal and noise coexist or where local estimates fall between clearly signal-dominated and noise-dominated values. This correction supports a smoother response as local data characteristics change. Instead of classifying data through a rigid binary threshold, the proposed method combines the outputs of short- and long-window TFPF operations using locally weighted blending. The weights vary according to the local Hurst estimates. In regions with coherent reflections and higher persistence, the blend favors the short-window output to preserve reflection amplitudes, high-frequency content, and waveform details. In regions with more random behavior, it gives greater weight to the long-window output to strengthen noise attenuation. This gradual combination is designed to reduce abrupt transitions, blocky artifacts, and discontinuities between neighboring regions. The method was evaluated using synthetic datasets and a two-dimensional field seismic record. Synthetic tests at different SNR levels assessed noise attenuation and signal preservation under varying contamination conditions. Results showed improved output SNR and effective suppression of random noise, while difference sections indicated limited leakage of the desired signal. Application to the field record demonstrated that the method reduced random noise while preserving the structural continuity, dip, and relative amplitudes of subsurface reflections. Mean amplitude spectra were also analyzed to assess changes in frequency content, indicating that essential broadband information was maintained. Overall, local-Hurst-guided adaptive TFPF adjusts the contribution of short- and long-window filtering according to local data characteristics. The results suggest that this approach can improve the balance between random-noise attenuation and preservation of important seismic attributes, supporting subsequent interpretation and inversion. Its performance depends on reliable local Hurst estimation and appropriate analysis-window parameters. Nevertheless, the synthetic and field tests demonstrate its potential as a practical approach for seismic data denoising while retaining features needed for geological interpretation and further quantitative analysis. https://www.ijgeophysics.ir/article_254427.html Passive seismic approaches have gained traction in hydrocarbon exploration following reports of low-frequency spectral anomalies that may be linked to fluid-bearing subsurface systems. Yet their underlying physics, spatial coherence, and temporal persistence remain contentious. Here we examine these issues using an eight-month continuous dataset recorded by a dense array of 106 three-component borehole stations deployed in the Dehdasht area of the Zagros sedimentary basin (SW Iran). Dehdasht is situated within the Zagros fold-and-thrust belt, where detachment folding and thrusting are accompanied by pronounced along-strike variability in structural style. This variability is strongly controlled by evaporite-dominated décollement levels, most notably within the Gachsaran Formation. Thick evaporitic successions and complex three-dimensional geometry substantially degrade conventional reflection-seismic imaging, motivating evaluation of passive seismic attributes as complementary observables. The array operated continuously during 2016–2017, with a dense network spanning ~400 km² at ~2 km station spacing. Each site comprised a triaxial borehole seismometer installed at ~10 m depth and recorded by a Spidernano data logger. After instrument-response removal, STA/LTA-based transient and event detection and excision, and selection of quiescent nighttime interval, we estimated power spectral densities (PSDs) using Welch’s method, computed band-integrated energies, and formed vertical-to-horizontal (Z/H) spectral ratios over 1–5 Hz. Spectral ratios were smoothed using Konno–Ohmachi logarithmic smoothing, and station-level statistics were summarized by the median of daily estimates. Absolute power maps show a strong spatial maximum co-located with the city of Dehdasht, with amplitudes ~2 orders of magnitude above surrounding levels, demonstrating pervasive anthropogenic contamination that precludes direct hydrocarbon interpretation. The tight correspondence between vertical and horizontal power patterns indicates that cultural sources energize the full three-component wavefield, consistent with broadband urban noise. By contrast, Z/H ratio maps exhibit laterally variable anomalies that recur across three consecutive ~80-day intervals, indicating quasi-stationary behavior with moderate temporal modulation. Frequency-resolved diagnostics show that elevated Z/H values are primarily associated with diminished horizontal-component energy in the 2–3 Hz band rather than enhanced vertical energy, favoring mechanisms that suppress or attenuate horizontal motion (e.g., site/near-surface effects) over direct reservoir-generated emission. The resulting anomaly distribution shows no compelling alignment with mapped structural traps or regional Zagros tectonic trends, implying that near-surface heterogeneity, site response, and/or cultural noise dominate the observed signatures. Although the repeatability of the Z/H anomalies confirms the presence of a stable wavefield component, their non-unique origin limits their utility as standalone hydrocarbon indicators. Overall, the study provides a practical framework for quantifying the repeatability of passive-seismic spectral anomalies and highlights the necessity of rigorously separating anthropogenic and site-related contributions from any deeper subsurface signal in structurally complex basins.