Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Survey of oil and gas geomicrobial anomalies in mud volcano Seyvan, around Marand city - East Azerbaijan Survey of oil and gas geomicrobial anomalies in mud volcano Seyvan, around Marand city - East Azerbaijan 133 148 102417 10.48308/esrj.2022.102417 FA Maryam Teymouri Department of Earth Science, Faculty of Natural Science, University of Tabriz, Iran Ali Kadkhodaei Department of Earth Science, Faculty of Natural Science, University of Tabriz, Iran Nasir Amel Department of Earth Science, Faculty of Natural Science, University of Tabriz, Iran 0000-0002-4051-188x Rahim Kadkhodaei Department of Earth Science, Faculty of Natural Science, University of Tabriz, Iran Gholamreza Zarrini Department of Animal Biology, Faculty of Natural Science, University of Tabriz, Iran Soghra Hatamzadeh Department of Earth Science, Faculty of Natural Science, University of Tabriz, Iran Journal Article 2024 08 01 Introduction Mud Volcano are one of strangest and most fascinating geomorphologic phenomena. They have important for a wide Spectrum of disciplines, including the oil industry (Stewart and Davies, 2009). Since the mud volcanoes originate from deeper depths of the earth, they act as regional indications for hydrocarbon exploration (Shnyukow and Yanko-Hombach, 2020). One of the recent methods of hydrocarbon exploration is geomicrobial exploration that is based on surface excavation technique to identify leaking gases relevant to hydrocarbon microseepage. According, they shows detection of seepage trend and their migration from subsurface oil reservoirs to surface environments. A direct and positive relationship has been observed between microbial populations and hydrocarbon concentrations in the soil of production various reservoirs in around the world. (Wanger et al, 2002). Materials and Methods The sampling site is located in the village of Seyvan, which is part of the central district of Marand County in the northwestern region of East Azerbaijan Province. The collected samples include water and soil samples. The samples were gathered from the site of a currently inactive mud volcano, as well as from the hill surrounding the mud volcano, based on its color and geomorphology, and from locations where water and gas were periodically and intermittently released. Each soil sample, weighing approximately 1 kilogram, was collected from a depth of 0.5 meters in plastic bags that had been sterilized in an autoclave beforehand, while the water samples were collected in glass containers. One of the acceptable and common methods for survey of microbial population of oil-eating bacteria to study of oil and gas geomicrobial anomaly in the Seyvan mud volcano is counting method on culture plates that is performed in the form of Plate Count. The Geomicrbi survey method includes collecting soil samples from the study area, packing, maintenance and storing samples in pre-sterilized sample bags in conditions without microbe and cold to sample preparation for culture, Analysis and separation and counting of hydrocarbon user bacteria such as methane, ethane, propane and butane oxidizers. The results of bacteria count results for each sample were calculated based on the number of bacteria colonies per gram soil or 1 ml liquid. Results and Discussion After the incubation period for assessing the total number of colonies formed by the target bacteria, each of the colonies formed at different dilutions of a sample was examined visually and under a stereomicroscope on culture medium plates. A light stereomicroscope is used to confirm the target microorganism and verify the characteristics of the colonies. Consequently, the target bacteria were distinguished from other potential bacteria, and the number of desired colonies on the culture medium was counted, with calculations made per gram of the soil sample examined. The assessments included distinguishing the target bacteria from other potential bacteria, counting the desired colonies, and calculating this per gram of the soil sample examined. To calculate the number of methanotrophic, ethanotrophic, and propanotrophic bacteria in each soil sample, the number of target bacteria counted on each culture medium plate is multiplied by the inverse of the dilution factor applied to each plate. Additionally, since 0.1 mL of each suspension was used for culturing, the resulting number is multiplied by 10. The microbial population is expressed as "Colony Forming Units (CFU) per milliliter of microbial suspension" (Liu et al, 2016). Inverse of dilution factor * 10 * number of colonies = cfu/mg (number of microbes in 1 milliliter of suspension) Finally, to calculate the number of microorganisms per gram of soil, the CFU/mL calculated for each microbial suspension is multiplied by 5, as 20 grams of the initial soil were suspended in 50 mL of serum. The final number represents the target microorganisms per gram of soil. Number of bacteria in 1 milligram * 10/50 (Table 1) 10/50 * number of bacteria in 1 milligram = number per gram of soil. Conclusion In order to determine population of methanotorphic, ethanotrophic and propanotrophic bacteria in the Seyvan mud volcanoes (Figure 1), a total of seven soil and mud samples together with two water samples were collected from 8 points of the study area. Due to the sampling points of Seyvan mud volcanoes in three points of the study area, methane and ethane oxidizing bacteria are present at the same time, those points include the main crater of mud volcanoes and in points from the hill which contain of mud volcanoes that from they were coming out water and gas periodic and intermittently. The only place where only ethanotrophic bacteria were present, was point sv2. while to confirm the presence of propanotroph bacteria, supplementary experiments are needed (Figure 2). Considering presence of methane and ethane user bacteria, two results can be taken, in the first place, methane can be of the type of biogenetic methane and the presence of ethane in the area can be due to the fusion of two methane affected by pressure. On the other hand, can be argued due to the limited number of methanotrophic and ethanotrophic bacterias that the presence of hydrocarbons is possible but it does not have economic value. Introduction Mud Volcano are one of strangest and most fascinating geomorphologic phenomena. They have important for a wide Spectrum of disciplines, including the oil industry (Stewart and Davies, 2009). Since the mud volcanoes originate from deeper depths of the earth, they act as regional indications for hydrocarbon exploration (Shnyukow and Yanko-Hombach, 2020). One of the recent methods of hydrocarbon exploration is geomicrobial exploration that is based on surface excavation technique to identify leaking gases relevant to hydrocarbon microseepage. According, they shows detection of seepage trend and their migration from subsurface oil reservoirs to surface environments. A direct and positive relationship has been observed between microbial populations and hydrocarbon concentrations in the soil of production various reservoirs in around the world. (Wanger et al, 2002). Materials and Methods The sampling site is located in the village of Seyvan, which is part of the central district of Marand County in the northwestern region of East Azerbaijan Province. The collected samples include water and soil samples. The samples were gathered from the site of a currently inactive mud volcano, as well as from the hill surrounding the mud volcano, based on its color and geomorphology, and from locations where water and gas were periodically and intermittently released. Each soil sample, weighing approximately 1 kilogram, was collected from a depth of 0.5 meters in plastic bags that had been sterilized in an autoclave beforehand, while the water samples were collected in glass containers. One of the acceptable and common methods for survey of microbial population of oil-eating bacteria to study of oil and gas geomicrobial anomaly in the Seyvan mud volcano is counting method on culture plates that is performed in the form of Plate Count. The Geomicrbi survey method includes collecting soil samples from the study area, packing, maintenance and storing samples in pre-sterilized sample bags in conditions without microbe and cold to sample preparation for culture, Analysis and separation and counting of hydrocarbon user bacteria such as methane, ethane, propane and butane oxidizers. The results of bacteria count results for each sample were calculated based on the number of bacteria colonies per gram soil or 1 ml liquid. Results and Discussion After the incubation period for assessing the total number of colonies formed by the target bacteria, each of the colonies formed at different dilutions of a sample was examined visually and under a stereomicroscope on culture medium plates. A light stereomicroscope is used to confirm the target microorganism and verify the characteristics of the colonies. Consequently, the target bacteria were distinguished from other potential bacteria, and the number of desired colonies on the culture medium was counted, with calculations made per gram of the soil sample examined. The assessments included distinguishing the target bacteria from other potential bacteria, counting the desired colonies, and calculating this per gram of the soil sample examined. To calculate the number of methanotrophic, ethanotrophic, and propanotrophic bacteria in each soil sample, the number of target bacteria counted on each culture medium plate is multiplied by the inverse of the dilution factor applied to each plate. Additionally, since 0.1 mL of each suspension was used for culturing, the resulting number is multiplied by 10. The microbial population is expressed as "Colony Forming Units (CFU) per milliliter of microbial suspension" (Liu et al, 2016). Inverse of dilution factor * 10 * number of colonies = cfu/mg (number of microbes in 1 milliliter of suspension) Finally, to calculate the number of microorganisms per gram of soil, the CFU/mL calculated for each microbial suspension is multiplied by 5, as 20 grams of the initial soil were suspended in 50 mL of serum. The final number represents the target microorganisms per gram of soil. Number of bacteria in 1 milligram * 10/50 (Table 1) 10/50 * number of bacteria in 1 milligram = number per gram of soil. Conclusion In order to determine population of methanotorphic, ethanotrophic and propanotrophic bacteria in the Seyvan mud volcanoes (Figure 1), a total of seven soil and mud samples together with two water samples were collected from 8 points of the study area. Due to the sampling points of Seyvan mud volcanoes in three points of the study area, methane and ethane oxidizing bacteria are present at the same time, those points include the main crater of mud volcanoes and in points from the hill which contain of mud volcanoes that from they were coming out water and gas periodic and intermittently. The only place where only ethanotrophic bacteria were present, was point sv2. while to confirm the presence of propanotroph bacteria, supplementary experiments are needed (Figure 2). Considering presence of methane and ethane user bacteria, two results can be taken, in the first place, methane can be of the type of biogenetic methane and the presence of ethane in the area can be due to the fusion of two methane affected by pressure. On the other hand, can be argued due to the limited number of methanotrophic and ethanotrophic bacterias that the presence of hydrocarbons is possible but it does not have economic value.

https://esrj.sbu.ac.ir/article_102417_300ba4a1d5320e3de54bd78eef1d923b.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Pattern identification and attenuation trend: development of nebkhas in Iran (Case study: Kerman province) Pattern identification and attenuation trend: development of nebkhas in Iran (Case study: Kerman province) 1 24 106136 10.48308/esrj.2025.239063.1267 FA Hazhir Mohammadi Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran 0009-0008-8758-8949 Mehran Maghsoudi Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran 0000-0002-4973-8327 Journal Article 2025 04 10 Introduction Nebkha is a distinctive type of desert landform that develops when aeolian (wind-blown) sand accumulates around a vegetative anchor, such as shrubs, grasses, or small bushes. This phenomenon predominantly occurs in arid and semi-arid environments, where vegetation is sparse and the wind plays a dominant role in shaping the landscape. Nebkhas are not only geomorphological indicators of wind dynamics and vegetation interaction but also serve as ecological microhabitats for various desert organisms. These sand mounds are formed by the progressive deposition of wind-transported particles around the base of plants, where the roughness provided by vegetation reduces wind speed, causing particles to settle. Over time, this process creates crescent-shaped or irregularly domed sand dunes that can reach from a few centimeters to several meters in height, depending on environmental variables. The vegetation, in turn, becomes increasingly buried as the nebkha grows, often leading to a complex interaction between plant physiology and sediment deposition. In arid regions like Kerman Province in Iran, nebkhas act as natural windbreaks, reducing the extent of wind erosion and helping to stabilize otherwise highly mobile desert surfaces.The ecological significance of nebkhas goes beyond their role in sand trapping. They promote soil development, enhance microbial activity in the rhizosphere, and support biodiversity in desert ecosystems. By creating microenvironments with higher moisture retention and organic matter accumulation, nebkhas help establish favorable niches for other plant species and even desert fauna. These functions make nebkhas critical components in the study of desertification, land degradation, and ecological resilience.The primary objective of the present study is to explore the spatial and temporal patterns of nebkha development in Kerman Province, focusing particularly on the morphological evolution of these landforms over recent decades. Kerman is an ideal case study due to its varied climatic conditions, significant wind activity, and presence of vegetation-sand interactions in multiple zones. The research also highlights how climate variability—including droughts and episodic rainfall—can lead to the degradation or regeneration of these formations. By combining satellite imagery, meteorological data, and remote sensing techniques, this study provides a comprehensive understanding of how nebkhas are formed, how they evolve over time, and what their current status indicates about broader environmental trends. Such investigations are crucial for land management practices, especially in regions vulnerable to desertification and ecological stress. Materials and Methods To analyze the formation, spatial distribution, and dynamic changes of nebkhas across the study area, a multidisciplinary approach was adopted involving meteorological observations, satellite image analysis, and geospatial data processing. The methodology was designed to provide both qualitative and quantitative insights into the environmental processes governing nebkha dynamics. Wind Data Analysis: The first step in the analysis involved collecting wind data—both speed and direction—from meteorological stations situated in Bam, Shahdad, and Kahnuj. Wind Rose and Sand Rose diagrams were constructed to visualize prevailing wind directions and intensities. These diagrams help identify dominant erosional forces and potential sediment transport pathways. The Sand Drift Potential (DPt) and Resultant Drift Potential (RDP) were calculated using standard equations in aeolian geomorphology, which quantify the potential for sand transport and its directional bias. This information is vital for understanding where nebkhas are most likely to form and how they might migrate over time. Remote Sensing and Image Analysis: To monitor temporal changes in nebkha distribution and morphology, a time series of Landsat 8 satellite images (30-meter resolution) was analyzed. Images from multiple years were preprocessed using atmospheric correction and geometric alignment to ensure consistency. The images were then imported into ImageJ, a powerful open-source software for image processing. In this software, negative imaging was applied to enhance contrast between vegetation and bare soil, making nebkhas more distinguishable from surrounding landforms. Image differencing techniques were used to detect spatial changes in nebkha features between different years. This technique involves subtracting pixel values of one image from another to highlight areas of change. In regions where pixel values significantly changed, either new nebkhas had formed, or existing ones had been degraded or displaced by active dunes. Automated Change Detection with Python: To improve the precision and reproducibility of the image analysis, custom Python scripts were developed. These scripts automated the pixel-level comparison across image sets, reducing human error and enabling more efficient processing of large datasets. The Python algorithms calculated pixel-by-pixel differences and flagged areas that showed significant morphological changes, enabling spatial mapping of nebkha dynamics over time. Climatic Data Assessment: In addition to wind data, the study analyzed long-term climatic data, including mean annual temperature and total annual precipitation over a 20-year period. These data were obtained from synoptic weather stations within and around Kerman Province. This climatic information helped assess how shifts in temperature and rainfall patterns correlate with the formation or deterioration of nebkhas. Vegetation Cover Mapping and NDVI Analysis: To evaluate the role of vegetation in nebkha stability, the Normalized Difference Vegetation Index (NDVI) was calculated from the Landsat images. NDVI is a widely used remote sensing index that reflects vegetation density and health. Higher NDVI values indicate dense, healthy vegetation, while lower values point to sparse or stressed vegetation. Land use maps were generated for different time points to identify zones of ecological degradation or improvement. These maps were then cross-referenced with nebkha locations to assess the relationship between vegetation dynamics and nebkha evolution. By integrating these diverse datasets and methods, the study provides a robust, multi-temporal analysis of nebkha development under the influence of climatic and geomorphological factors. Results and Discussion Wind Erosion Analysis: Wind dynamics play a pivotal role in both the formation and degradation of nebkhas. Based on the Wind Rose and Sand Rose analyses, distinct wind regimes were identified for each station: Station DPt RDP RDD RDP/DPt Bam 214.1 119.206 140° 0.0557 Shahdad 892.9 492.887 149° 0.552 Kahnuj 254.9 229.172 46° 0.899 The RDP/DPt ratio is critical in interpreting the unidirectionality of wind transport. In Bam, the low ratio suggests multidirectional winds, reducing net sediment transport in any specific direction. In contrast, Kahuna’s high ratio reflects a dominant wind direction, conducive to the linear accumulation of sand and nebkha formation. Shahdad represents an intermediate condition with substantial drift potential and a moderately focused directional bias. 2. Image Differencing and Spatial Changes in Nebkhas: Image processing in ImageJ revealed significant spatial and morphological changes in nebkha formations between different time intervals. Key observations include: Growth and densification of existing nebkhas in zones with recent rainfall and vegetation regeneration. Disappearance of smaller or younger nebkhas in areas increasingly affected by mobile sand encroachment. In some transitional zones, fragmentation of nebkhas was evident, possibly due to fluctuating vegetation health and inconsistent sand supply. These findings underscore the dual nature of environmental drivers: while certain areas demonstrate resilience and regeneration, others continue to degrade under the combined pressure of wind erosion and drought.3. Climatic Trends and Vegetation Dynamics: Analysis of the 20-year climatic data revealed a prolonged drought period lasting approximately 14 years, followed by three consecutive years of above-average rainfall. According to reports from the Kerman Meteorological Organization, this recent precipitation influx significantly improved soil moisture content and facilitated vegetative growth along the desert margins. This change is clearly reflected in the NDVI analysis, where notable increases in vegetation cover were observed, particularly in the northern and northeastern sectors of the province. These improved conditions led to the re-emergence of nebkhas in previously degraded zones. However, in southern areas with less precipitation, vegetation recovery was minimal, and many smaller nebkhas failed to survive. The study thereby confirms the hypothesis that climate—particularly rainfall—acts both as a degrading and restorative force in desert geomorphology. Conclusion The comprehensive investigation into the dynamics of nebkhas in Kerman Province highlights their significant geomorphological and ecological roles in arid landscapes. Nebkhas are not static landforms; they are dynamic systems shaped by the interplay of wind, vegetation, and climate. The study demonstrated that regions with high wind drift potential and sufficient vegetative cover are more likely to sustain large and persistent nebkhas. Conversely, areas subject to extreme drought and active sand encroachment show nebkha degradation and eventual loss. Key takeaways from the research include: Morphological Patterns: Four primary nebkha patterns were observed—linear, fan-shaped, dense/sparse, and anthropogenic. These patterns reflect local wind regimes and vegetation types. Wind and Sand Dynamics: The DPt and RDP values at the studied stations revealed varying degrees of susceptibility to wind erosion. Kahnuj had the most unidirectional winds, ideal for nebkha formation, while Bam had the least focused winds. Climate Variability: Despite a long-term drought, the recent increase in rainfall played a crucial role in reviving degraded areas and promoting nebkha regrowth. This underscores the importance of episodic climatic events in maintaining desert landforms. Technological Integration: The use of satellite imagery, NDVI analysis, and automated pixel-level change detection provided a robust and replicable methodology for future studies on aeolian landforms. Ultimately, nebkhas serve as early indicators of ecological health in desert regions. Monitoring their evolution offers valuable insights into broader environmental processes, including desertification, climate adaptation, and landscape resilience. Continued research and satellite-based observation will be vital for managing and conserving these unique desert features under conditions of increasing climatic uncertainty Introduction Nebkha is a distinctive type of desert landform that develops when aeolian (wind-blown) sand accumulates around a vegetative anchor, such as shrubs, grasses, or small bushes. This phenomenon predominantly occurs in arid and semi-arid environments, where vegetation is sparse and the wind plays a dominant role in shaping the landscape. Nebkhas are not only geomorphological indicators of wind dynamics and vegetation interaction but also serve as ecological microhabitats for various desert organisms. These sand mounds are formed by the progressive deposition of wind-transported particles around the base of plants, where the roughness provided by vegetation reduces wind speed, causing particles to settle. Over time, this process creates crescent-shaped or irregularly domed sand dunes that can reach from a few centimeters to several meters in height, depending on environmental variables. The vegetation, in turn, becomes increasingly buried as the nebkha grows, often leading to a complex interaction between plant physiology and sediment deposition. In arid regions like Kerman Province in Iran, nebkhas act as natural windbreaks, reducing the extent of wind erosion and helping to stabilize otherwise highly mobile desert surfaces.The ecological significance of nebkhas goes beyond their role in sand trapping. They promote soil development, enhance microbial activity in the rhizosphere, and support biodiversity in desert ecosystems. By creating microenvironments with higher moisture retention and organic matter accumulation, nebkhas help establish favorable niches for other plant species and even desert fauna. These functions make nebkhas critical components in the study of desertification, land degradation, and ecological resilience.The primary objective of the present study is to explore the spatial and temporal patterns of nebkha development in Kerman Province, focusing particularly on the morphological evolution of these landforms over recent decades. Kerman is an ideal case study due to its varied climatic conditions, significant wind activity, and presence of vegetation-sand interactions in multiple zones. The research also highlights how climate variability—including droughts and episodic rainfall—can lead to the degradation or regeneration of these formations. By combining satellite imagery, meteorological data, and remote sensing techniques, this study provides a comprehensive understanding of how nebkhas are formed, how they evolve over time, and what their current status indicates about broader environmental trends. Such investigations are crucial for land management practices, especially in regions vulnerable to desertification and ecological stress. Materials and Methods To analyze the formation, spatial distribution, and dynamic changes of nebkhas across the study area, a multidisciplinary approach was adopted involving meteorological observations, satellite image analysis, and geospatial data processing. The methodology was designed to provide both qualitative and quantitative insights into the environmental processes governing nebkha dynamics. Wind Data Analysis: The first step in the analysis involved collecting wind data—both speed and direction—from meteorological stations situated in Bam, Shahdad, and Kahnuj. Wind Rose and Sand Rose diagrams were constructed to visualize prevailing wind directions and intensities. These diagrams help identify dominant erosional forces and potential sediment transport pathways. The Sand Drift Potential (DPt) and Resultant Drift Potential (RDP) were calculated using standard equations in aeolian geomorphology, which quantify the potential for sand transport and its directional bias. This information is vital for understanding where nebkhas are most likely to form and how they might migrate over time. Remote Sensing and Image Analysis: To monitor temporal changes in nebkha distribution and morphology, a time series of Landsat 8 satellite images (30-meter resolution) was analyzed. Images from multiple years were preprocessed using atmospheric correction and geometric alignment to ensure consistency. The images were then imported into ImageJ, a powerful open-source software for image processing. In this software, negative imaging was applied to enhance contrast between vegetation and bare soil, making nebkhas more distinguishable from surrounding landforms. Image differencing techniques were used to detect spatial changes in nebkha features between different years. This technique involves subtracting pixel values of one image from another to highlight areas of change. In regions where pixel values significantly changed, either new nebkhas had formed, or existing ones had been degraded or displaced by active dunes. Automated Change Detection with Python: To improve the precision and reproducibility of the image analysis, custom Python scripts were developed. These scripts automated the pixel-level comparison across image sets, reducing human error and enabling more efficient processing of large datasets. The Python algorithms calculated pixel-by-pixel differences and flagged areas that showed significant morphological changes, enabling spatial mapping of nebkha dynamics over time. Climatic Data Assessment: In addition to wind data, the study analyzed long-term climatic data, including mean annual temperature and total annual precipitation over a 20-year period. These data were obtained from synoptic weather stations within and around Kerman Province. This climatic information helped assess how shifts in temperature and rainfall patterns correlate with the formation or deterioration of nebkhas. Vegetation Cover Mapping and NDVI Analysis: To evaluate the role of vegetation in nebkha stability, the Normalized Difference Vegetation Index (NDVI) was calculated from the Landsat images. NDVI is a widely used remote sensing index that reflects vegetation density and health. Higher NDVI values indicate dense, healthy vegetation, while lower values point to sparse or stressed vegetation. Land use maps were generated for different time points to identify zones of ecological degradation or improvement. These maps were then cross-referenced with nebkha locations to assess the relationship between vegetation dynamics and nebkha evolution. By integrating these diverse datasets and methods, the study provides a robust, multi-temporal analysis of nebkha development under the influence of climatic and geomorphological factors. Results and Discussion Wind Erosion Analysis: Wind dynamics play a pivotal role in both the formation and degradation of nebkhas. Based on the Wind Rose and Sand Rose analyses, distinct wind regimes were identified for each station: Station DPt RDP RDD RDP/DPt Bam 214.1 119.206 140° 0.0557 Shahdad 892.9 492.887 149° 0.552 Kahnuj 254.9 229.172 46° 0.899 The RDP/DPt ratio is critical in interpreting the unidirectionality of wind transport. In Bam, the low ratio suggests multidirectional winds, reducing net sediment transport in any specific direction. In contrast, Kahuna’s high ratio reflects a dominant wind direction, conducive to the linear accumulation of sand and nebkha formation. Shahdad represents an intermediate condition with substantial drift potential and a moderately focused directional bias. 2. Image Differencing and Spatial Changes in Nebkhas: Image processing in ImageJ revealed significant spatial and morphological changes in nebkha formations between different time intervals. Key observations include: Growth and densification of existing nebkhas in zones with recent rainfall and vegetation regeneration. Disappearance of smaller or younger nebkhas in areas increasingly affected by mobile sand encroachment. In some transitional zones, fragmentation of nebkhas was evident, possibly due to fluctuating vegetation health and inconsistent sand supply. These findings underscore the dual nature of environmental drivers: while certain areas demonstrate resilience and regeneration, others continue to degrade under the combined pressure of wind erosion and drought.3. Climatic Trends and Vegetation Dynamics: Analysis of the 20-year climatic data revealed a prolonged drought period lasting approximately 14 years, followed by three consecutive years of above-average rainfall. According to reports from the Kerman Meteorological Organization, this recent precipitation influx significantly improved soil moisture content and facilitated vegetative growth along the desert margins. This change is clearly reflected in the NDVI analysis, where notable increases in vegetation cover were observed, particularly in the northern and northeastern sectors of the province. These improved conditions led to the re-emergence of nebkhas in previously degraded zones. However, in southern areas with less precipitation, vegetation recovery was minimal, and many smaller nebkhas failed to survive. The study thereby confirms the hypothesis that climate—particularly rainfall—acts both as a degrading and restorative force in desert geomorphology. Conclusion The comprehensive investigation into the dynamics of nebkhas in Kerman Province highlights their significant geomorphological and ecological roles in arid landscapes. Nebkhas are not static landforms; they are dynamic systems shaped by the interplay of wind, vegetation, and climate. The study demonstrated that regions with high wind drift potential and sufficient vegetative cover are more likely to sustain large and persistent nebkhas. Conversely, areas subject to extreme drought and active sand encroachment show nebkha degradation and eventual loss. Key takeaways from the research include: Morphological Patterns: Four primary nebkha patterns were observed—linear, fan-shaped, dense/sparse, and anthropogenic. These patterns reflect local wind regimes and vegetation types. Wind and Sand Dynamics: The DPt and RDP values at the studied stations revealed varying degrees of susceptibility to wind erosion. Kahnuj had the most unidirectional winds, ideal for nebkha formation, while Bam had the least focused winds. Climate Variability: Despite a long-term drought, the recent increase in rainfall played a crucial role in reviving degraded areas and promoting nebkha regrowth. This underscores the importance of episodic climatic events in maintaining desert landforms. Technological Integration: The use of satellite imagery, NDVI analysis, and automated pixel-level change detection provided a robust and replicable methodology for future studies on aeolian landforms. Ultimately, nebkhas serve as early indicators of ecological health in desert regions. Monitoring their evolution offers valuable insights into broader environmental processes, including desertification, climate adaptation, and landscape resilience. Continued research and satellite-based observation will be vital for managing and conserving these unique desert features under conditions of increasing climatic uncertainty

https://esrj.sbu.ac.ir/article_106136_6b957f2167a12d2fbeeb14cbe12a59f5.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Investigating and analyzing of urban climate comfort in present and future conditions based on climate change scenarios and regression methods in a coastal area Investigating and analyzing of urban climate comfort in present and future conditions based on climate change scenarios and regression methods in a coastal area 25 51 106132 10.48308/esrj.2025.240411.1281 FA Sedigheh Lotfi Department of Geography, Faculty of Humanities and Social Sciences, University of Mazandaran, Babolsar, Iran 0000-0001-6467-0642 Sedigheh Barakahnpour Ahmadi Department of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran 0009-0006-5968-9115 Journal Article 2025 06 16 Introduction The increase in temperature caused by human activities and the type of structure of the urban environment has an effect on the heat island. Extreme weather changes the climatic comfort in the human habitat, which causes the period of climatic comfort to exist with different characteristics in different regions and affect human health and comfort as well as the pattern of tourism development. Considering the effect of global warming and changes in climatic parameters in today's urban development and the need to create ecological stability between nature and human artifacts, discussion and investigation on the effect of micro climate factors on environmental and thermal comfort in urban spaces have been proposed as one of the important factors in quality urban spaces. The northern regions of Iran have a vast territory and diverse topographical and climatic conditions. Meanwhile, the southern regions of the Caspian Sea are rich in natural landforms and various tourism resources. With the warming of the global climate, the study of the comfort of the tourism climate in these areas should be given more attention and become one of the important subjects of the study of the climate of human habitation as well as the study of the resources of the tourism climate. However, the current research on climate comfort in Iran, especially in the northern regions of Iran, is very little, and few studies have been conducted on the analysis of comfort conditions under severe weather in the present and future time period and the investigation of possible changes. Therefore, in this study, the conditions of climatic comfort and human comfort were studied with regard to global warming and climate change scenarios in the southern regions of the Caspian Sea. Materials and Methods The study area includes Babolsar city one of the coastal cities of Mazandaran province, northern Iran, which is located between the southern shores of the Caspian Sea and the Alborz Mountain at 52º 39́ and 30̋ of longitude and 36º and 43́ of latitude and has an area of about 248.6 km2. Due to the low altitude of this area (-21 meters below sea level), the summers are hot and humid (often sultry), the winters are mild and humid, and frost is rare. The long-term average temperature and annual precipitation in this city are 17.8 °C and 939 mm, respectively. Due to its vicinity to the Caspian Sea, tourism services have been developed in this city and it is one of the best tourist destinations (about 6 million tourists per year) in Iran. Climate change and global warming have increasingly caused extreme events and stresses, and when investigating the conditions and patterns of changes in climate and tourism data time series, in addition to examining the average data, the extreme values of the data time series that cause extreme discomfort and stress should also be considered. Therefore, The main purpose of this study is to investigate and identify the temporal patterns and trends of thermal discomfort based on different comfort-discomfort indices and affected by changes in climatic parameters in the historical period of 1987-2016 and based on future climate scenarios (SSP126, SSP245 and SSP585) from the GFDL-ESM4 climate model in two periods of 2020-2059 (near future) and 2060-2099 (far future) in the coastal and tourist-friendly city of Babolsar, located in the northern regions of Iran and the southern shores of the Caspian Sea. For this purpose, different values (especially extreme values) of daily temperature, relative humidity and wind speed were analyzed in different seasons. Then the comfort-discomfort indices including Effective Temperature Index (ET), Temperature and Humidity Index (THI), and Beiker Bioclimatic Index (Cp) were calculated for all the scenarios and time periods studied and the trend of changes was investigated on different values of these indices (especially limit tails of data) as well as the climate variables studied in the historical period and based on different future scenarios using the method quantile regression. Results and Discussion The study of the trends in climate parameters and climate comfort indices has shown that in the historical period, temperature and wind speed have increased in all seasons, but relative humidity has decreased. According to the optimistic scenario, various values of temperature and wind speed will increase in the near future period (2020-2059) (with a slope of 0.54°C per decade and 0.03 m/s); while for the distant future period, only a significant increase in winter (0.4°C per decade) will still exist. However, relative humidity will increase in the near future in spring and summer, but in the distant future, relative humidity values will decrease in spring and winter but increase in summer. However, no significant trends will be observed based on this scenario in the autumn. Therefore, it can be said if optimistic conditions are established for the future period, we can witness no change in high temperatures and an increase in days without heat stress, especially in summer (reduction of air humidity). Also, since the wind plays a role in cooling the environment, increasing the wind speed based on this scenario along with a small change in temperature can reduce thermal stress. According to the average scenario, the temperature will increase in both periods (0.5, 0.44, 0.5 and 0.3 °C per decade, respectively), while the relative humidity in the spring and winter seasons will increase (1.6% and 2.2% per decade) but will decrease in summer and autumn in the near future (-1.4% and -0.3% per decade, respectively). However, a decreasing trend for wind speed in winter (-0.34 m/s per decade) and an increasing trend in spring and autumn (far future) (0.18 and 0.05 m/s per decade) will be observed. But according to the pessimistic scenario, temperature values will increase in all seasons (respectively 0.7, 0.5, 0.75 and 1 °C per decade) while the relative humidity in winter will increase (1.8 % per decade), but it decreases in other seasons (spring, summer and autumn, -0.7, -2.5 and -0.6 % per decade, respectively). However, a possible increase for wind speed will occur in summer, autumn and winter (0.1, 0.07 and 0.3 m/s per decade, respectively) (mainly in the far future). Therefore, if the climatic conditions continue in the same way or move in the direction of more production of CO2 and greenhouse gases, it is possible to witness an increase in the number of days with climatic discomfort in the near future. Climatic comfort indices (ET, THI and Cp) have increased in all seasons in the historical period. According to the optimistic scenario, ET and THI indices will increase in the near future only in spring, summer and winter, while there will be no change in autumn. But in the far future, a significant increase will occur in most seasons (except summer) for this index. The lack of increase (for ET and THI) and decrease (for Cp) changes in summer based on optimistic conditions indicates no increase in thermal discomfort conditions in this hot season of the year. However, based on the average and pessimistic scenario, the ET and THI indices will increase significantly, but the Cp index will decrease significantly (increase in hot and hot conditions). The intensity of changes will be greater for high values of ET and THI, which indicates a noticeable increase in the number of days with high stress and thermal stress, and towards the pessimistic scenario and the far future, the number of hot and warm days will increase. Based on two average and pessimistic scenarios, in the near future, the number of hot and warm days and uncomfortable conditions will increase in most cases, but in the far future, in addition to increasing the number of days with heat discomfort, the number of days with cold conditions and comfortable conditions will also decrease and the days will tend to get warmer. Conclusion The results of the study have shown that the period of discomfort will occur based on and under the influence of changes in meteorological and climatological variables affecting each discomfort index, and awareness of the weather conditions associated with periods of high thermal discomfort levels will help to management and reduction heat stress through the development of early warning systems. The number of days with high heat stress conditions (hot and sultry conditions) will potentially increase towards pessimistic scenarios, and the possibility of human discomfort due to high heat (and sometimes high relative humidity) not only will increase in the warm months of the year, but also in cold months, while cold and cool conditions decrease. The results of the study on a seasonal scale have shown that discomfort periods will occur based on and influenced by changes in meteorological and climatological variables affecting each discomfort index, and knowledge of the weather conditions associated with periods of high thermal discomfort levels will help manage and reduce heat stress through the development of early warning systems. Therefore, the results of this study are very important due to the importance and direct impact of climatic parameters on human health, and city officials should design and create environmental strategies to reduce the effects of heat in the city. However, due to the lack of meteorological stations with a high concentration in the region, the analysis was inevitably carried out on one station but on different time scales, and this issue has limited the results. Therefore, in future studies, it is suggested to carry out investigations on a spatial-temporal scale and with several climate models. Introduction The increase in temperature caused by human activities and the type of structure of the urban environment has an effect on the heat island. Extreme weather changes the climatic comfort in the human habitat, which causes the period of climatic comfort to exist with different characteristics in different regions and affect human health and comfort as well as the pattern of tourism development. Considering the effect of global warming and changes in climatic parameters in today's urban development and the need to create ecological stability between nature and human artifacts, discussion and investigation on the effect of micro climate factors on environmental and thermal comfort in urban spaces have been proposed as one of the important factors in quality urban spaces. The northern regions of Iran have a vast territory and diverse topographical and climatic conditions. Meanwhile, the southern regions of the Caspian Sea are rich in natural landforms and various tourism resources. With the warming of the global climate, the study of the comfort of the tourism climate in these areas should be given more attention and become one of the important subjects of the study of the climate of human habitation as well as the study of the resources of the tourism climate. However, the current research on climate comfort in Iran, especially in the northern regions of Iran, is very little, and few studies have been conducted on the analysis of comfort conditions under severe weather in the present and future time period and the investigation of possible changes. Therefore, in this study, the conditions of climatic comfort and human comfort were studied with regard to global warming and climate change scenarios in the southern regions of the Caspian Sea. Materials and Methods The study area includes Babolsar city one of the coastal cities of Mazandaran province, northern Iran, which is located between the southern shores of the Caspian Sea and the Alborz Mountain at 52º 39́ and 30̋ of longitude and 36º and 43́ of latitude and has an area of about 248.6 km2. Due to the low altitude of this area (-21 meters below sea level), the summers are hot and humid (often sultry), the winters are mild and humid, and frost is rare. The long-term average temperature and annual precipitation in this city are 17.8 °C and 939 mm, respectively. Due to its vicinity to the Caspian Sea, tourism services have been developed in this city and it is one of the best tourist destinations (about 6 million tourists per year) in Iran. Climate change and global warming have increasingly caused extreme events and stresses, and when investigating the conditions and patterns of changes in climate and tourism data time series, in addition to examining the average data, the extreme values of the data time series that cause extreme discomfort and stress should also be considered. Therefore, The main purpose of this study is to investigate and identify the temporal patterns and trends of thermal discomfort based on different comfort-discomfort indices and affected by changes in climatic parameters in the historical period of 1987-2016 and based on future climate scenarios (SSP126, SSP245 and SSP585) from the GFDL-ESM4 climate model in two periods of 2020-2059 (near future) and 2060-2099 (far future) in the coastal and tourist-friendly city of Babolsar, located in the northern regions of Iran and the southern shores of the Caspian Sea. For this purpose, different values (especially extreme values) of daily temperature, relative humidity and wind speed were analyzed in different seasons. Then the comfort-discomfort indices including Effective Temperature Index (ET), Temperature and Humidity Index (THI), and Beiker Bioclimatic Index (Cp) were calculated for all the scenarios and time periods studied and the trend of changes was investigated on different values of these indices (especially limit tails of data) as well as the climate variables studied in the historical period and based on different future scenarios using the method quantile regression. Results and Discussion The study of the trends in climate parameters and climate comfort indices has shown that in the historical period, temperature and wind speed have increased in all seasons, but relative humidity has decreased. According to the optimistic scenario, various values of temperature and wind speed will increase in the near future period (2020-2059) (with a slope of 0.54°C per decade and 0.03 m/s); while for the distant future period, only a significant increase in winter (0.4°C per decade) will still exist. However, relative humidity will increase in the near future in spring and summer, but in the distant future, relative humidity values will decrease in spring and winter but increase in summer. However, no significant trends will be observed based on this scenario in the autumn. Therefore, it can be said if optimistic conditions are established for the future period, we can witness no change in high temperatures and an increase in days without heat stress, especially in summer (reduction of air humidity). Also, since the wind plays a role in cooling the environment, increasing the wind speed based on this scenario along with a small change in temperature can reduce thermal stress. According to the average scenario, the temperature will increase in both periods (0.5, 0.44, 0.5 and 0.3 °C per decade, respectively), while the relative humidity in the spring and winter seasons will increase (1.6% and 2.2% per decade) but will decrease in summer and autumn in the near future (-1.4% and -0.3% per decade, respectively). However, a decreasing trend for wind speed in winter (-0.34 m/s per decade) and an increasing trend in spring and autumn (far future) (0.18 and 0.05 m/s per decade) will be observed. But according to the pessimistic scenario, temperature values will increase in all seasons (respectively 0.7, 0.5, 0.75 and 1 °C per decade) while the relative humidity in winter will increase (1.8 % per decade), but it decreases in other seasons (spring, summer and autumn, -0.7, -2.5 and -0.6 % per decade, respectively). However, a possible increase for wind speed will occur in summer, autumn and winter (0.1, 0.07 and 0.3 m/s per decade, respectively) (mainly in the far future). Therefore, if the climatic conditions continue in the same way or move in the direction of more production of CO2 and greenhouse gases, it is possible to witness an increase in the number of days with climatic discomfort in the near future. Climatic comfort indices (ET, THI and Cp) have increased in all seasons in the historical period. According to the optimistic scenario, ET and THI indices will increase in the near future only in spring, summer and winter, while there will be no change in autumn. But in the far future, a significant increase will occur in most seasons (except summer) for this index. The lack of increase (for ET and THI) and decrease (for Cp) changes in summer based on optimistic conditions indicates no increase in thermal discomfort conditions in this hot season of the year. However, based on the average and pessimistic scenario, the ET and THI indices will increase significantly, but the Cp index will decrease significantly (increase in hot and hot conditions). The intensity of changes will be greater for high values of ET and THI, which indicates a noticeable increase in the number of days with high stress and thermal stress, and towards the pessimistic scenario and the far future, the number of hot and warm days will increase. Based on two average and pessimistic scenarios, in the near future, the number of hot and warm days and uncomfortable conditions will increase in most cases, but in the far future, in addition to increasing the number of days with heat discomfort, the number of days with cold conditions and comfortable conditions will also decrease and the days will tend to get warmer. Conclusion The results of the study have shown that the period of discomfort will occur based on and under the influence of changes in meteorological and climatological variables affecting each discomfort index, and awareness of the weather conditions associated with periods of high thermal discomfort levels will help to management and reduction heat stress through the development of early warning systems. The number of days with high heat stress conditions (hot and sultry conditions) will potentially increase towards pessimistic scenarios, and the possibility of human discomfort due to high heat (and sometimes high relative humidity) not only will increase in the warm months of the year, but also in cold months, while cold and cool conditions decrease. The results of the study on a seasonal scale have shown that discomfort periods will occur based on and influenced by changes in meteorological and climatological variables affecting each discomfort index, and knowledge of the weather conditions associated with periods of high thermal discomfort levels will help manage and reduce heat stress through the development of early warning systems. Therefore, the results of this study are very important due to the importance and direct impact of climatic parameters on human health, and city officials should design and create environmental strategies to reduce the effects of heat in the city. However, due to the lack of meteorological stations with a high concentration in the region, the analysis was inevitably carried out on one station but on different time scales, and this issue has limited the results. Therefore, in future studies, it is suggested to carry out investigations on a spatial-temporal scale and with several climate models.

https://esrj.sbu.ac.ir/article_106132_e2e00e92ccef9a3dd943cb7c2a6a462b.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Determining the immigrant acceptance capacity in counties of Guilan province with ecosystem service sustainability approach Determining the immigrant acceptance capacity in counties of Guilan province with ecosystem service sustainability approach 52 70 105828 10.48308/esrj.2025.238156.1244 FA Mohammad Javad Amiri Department of Environmental Education and Systems, Faculty of Environment, University of Tehran, Irans 0000-0003-1748-9036 Touraj Nasrabadi Department of Planning and Management of environment and HSE, Faculty of Environment, University of Tehran, Iran Yaser Mojaver Sheikhan Kish International Campus, University of Tehran, Iran Journal Article 2025 01 11 Introduction Human existence relies heavily on ecosystems. However, in recent decades, humans have altered land cover at unprecedented rates, causing a decline in the quality of ecosystem services. These disruptions can have far-reaching and often irreversible consequences for both local and global environmental conditions. From 1996 to 2006, Guilan Province shifted from being a region that primarily sent migrants to one that began to accept them. Various factors, including climate change and water resource depletion in other regions of the country, may further escalate migration to Guilan. Such migration can result in profound changes to the province’s land cover, including deforestation, increased strain on ecosystems, and a reduction in ecosystem services. This research examines the capacity of each county within Guilan Province to accept immigrants. By calculating this capacity, the study evaluates and compares the current immigrant acceptance status of counties with the index determined in the analysis. Materials and Methods To track changes in land cover, satellite imagery from Landsat 5 and 9 was used for the years 1996, 2006, 2016, and 2023. Furthermore, criteria were identified through library research and semi-structured interviews. Using the Analytic Hierarchy Process (AHP), the immigrant acceptance capacity for each county was calculated. Results and Discussion Analysis of satellite images from 1996 to 2023 reveals a 99% increase in Build-up land cover, amounting to an expansion of 25,614 ha. During the same period, forest cover—the province’s most extensive and ecologically significant land cover for providing ecosystem services—declined by 107,069 ha. This reduction accounts for 7.74% of Guilan Province’s total area. Over these 37 years, the province’s population grew by 409,902, and 385,069 migrants arrived in Guilan between 2006 and 2016. A strong inverse correlation (-0.99) was observed between the population growth rate and the forest cover reduction rate, underscoring the close relationship between demographic changes and forest cover loss. To develop population and migration management plans that align with the sustainability of ecosystem services and the region’s ecological capacity, five ecological criteria were identified at the county scale. These criteria were derived from Guilan Province’s Territorial Management Document (2017). Additionally, following the findings of Ronchi and imposing restrictions on forest cover alteration to sustain ecosystem services, mountainous and foothill areas—which host over 95% of the province’s forest cover—were excluded from settlement development planning. Consequently, the ratio of each county’s plain area to the province’s total plain area was established as a sixth criterion. With identified ecological and ecosystem service criteria, the Analytic Hierarchy Process was employed to calculate the capacity index for accepting immigrants in each county. A comparison between these indexes and actual immigrant acceptance rates revealed significant ecological pressure discrepancies across some counties. Rasht County demonstrated the highest ecological pressure and discrepancy. Between 2006 and 2016, Rasht accommodated 36.76% of all migrants to the province, while the study determined its immigrant acceptance capacity to be 16%. This indicates that the county absorbed 20.76% more migrants than its obtained ecological capacity. In contrast, Bandar Anzali exhibited the most favorable immigrant acceptance conditions with minimal ecological strain. With an immigrant acceptance capacity of 12.5%, only 6.34% of the province’s migrants settled in Bandar Anzali during the same period, reflecting a positive alignment with its ecological capacity. Conclusion Since 2006, migrants arriving in Guilan have played a significant role in the region’s population growth. As migration to Guilan is expected to increase in the coming years, the associated demographic changes will undoubtedly impact land cover and ecosystem services. These developments underscore the critical need for population-migration planning based on ecological constraints and the vulnerabilities of ecosystem services. By calculating the immigrant acceptance capacity for each county, it becomes possible to identify the ecological pressure caused by migration. This analysis compares the current number of incoming migrants in each county with the index derived for that county. For counties like Rasht, which bear a heavier ecological burden, implementing policies to regulate and manage incoming migration in the years ahead is strongly recommended. Additionally, improving the ecological indicators used to calculate the immigrant acceptance capacity will help mitigate further environmental damages and prevent a reduction in the county’s ecological carrying capacity. On the other hand, attempts to improve the indicators used to calculate the immigrant acceptance capacity might result in increased ecological stability for counties like Bandar Anzali, where the number of arriving migrants is less than the calculated index. Long-term sustainability of ecosystem services and ecological stability would be preserved with the help of such actions. Introduction Human existence relies heavily on ecosystems. However, in recent decades, humans have altered land cover at unprecedented rates, causing a decline in the quality of ecosystem services. These disruptions can have far-reaching and often irreversible consequences for both local and global environmental conditions. From 1996 to 2006, Guilan Province shifted from being a region that primarily sent migrants to one that began to accept them. Various factors, including climate change and water resource depletion in other regions of the country, may further escalate migration to Guilan. Such migration can result in profound changes to the province’s land cover, including deforestation, increased strain on ecosystems, and a reduction in ecosystem services. This research examines the capacity of each county within Guilan Province to accept immigrants. By calculating this capacity, the study evaluates and compares the current immigrant acceptance status of counties with the index determined in the analysis. Materials and Methods To track changes in land cover, satellite imagery from Landsat 5 and 9 was used for the years 1996, 2006, 2016, and 2023. Furthermore, criteria were identified through library research and semi-structured interviews. Using the Analytic Hierarchy Process (AHP), the immigrant acceptance capacity for each county was calculated. Results and Discussion Analysis of satellite images from 1996 to 2023 reveals a 99% increase in Build-up land cover, amounting to an expansion of 25,614 ha. During the same period, forest cover—the province’s most extensive and ecologically significant land cover for providing ecosystem services—declined by 107,069 ha. This reduction accounts for 7.74% of Guilan Province’s total area. Over these 37 years, the province’s population grew by 409,902, and 385,069 migrants arrived in Guilan between 2006 and 2016. A strong inverse correlation (-0.99) was observed between the population growth rate and the forest cover reduction rate, underscoring the close relationship between demographic changes and forest cover loss. To develop population and migration management plans that align with the sustainability of ecosystem services and the region’s ecological capacity, five ecological criteria were identified at the county scale. These criteria were derived from Guilan Province’s Territorial Management Document (2017). Additionally, following the findings of Ronchi and imposing restrictions on forest cover alteration to sustain ecosystem services, mountainous and foothill areas—which host over 95% of the province’s forest cover—were excluded from settlement development planning. Consequently, the ratio of each county’s plain area to the province’s total plain area was established as a sixth criterion. With identified ecological and ecosystem service criteria, the Analytic Hierarchy Process was employed to calculate the capacity index for accepting immigrants in each county. A comparison between these indexes and actual immigrant acceptance rates revealed significant ecological pressure discrepancies across some counties. Rasht County demonstrated the highest ecological pressure and discrepancy. Between 2006 and 2016, Rasht accommodated 36.76% of all migrants to the province, while the study determined its immigrant acceptance capacity to be 16%. This indicates that the county absorbed 20.76% more migrants than its obtained ecological capacity. In contrast, Bandar Anzali exhibited the most favorable immigrant acceptance conditions with minimal ecological strain. With an immigrant acceptance capacity of 12.5%, only 6.34% of the province’s migrants settled in Bandar Anzali during the same period, reflecting a positive alignment with its ecological capacity. Conclusion Since 2006, migrants arriving in Guilan have played a significant role in the region’s population growth. As migration to Guilan is expected to increase in the coming years, the associated demographic changes will undoubtedly impact land cover and ecosystem services. These developments underscore the critical need for population-migration planning based on ecological constraints and the vulnerabilities of ecosystem services. By calculating the immigrant acceptance capacity for each county, it becomes possible to identify the ecological pressure caused by migration. This analysis compares the current number of incoming migrants in each county with the index derived for that county. For counties like Rasht, which bear a heavier ecological burden, implementing policies to regulate and manage incoming migration in the years ahead is strongly recommended. Additionally, improving the ecological indicators used to calculate the immigrant acceptance capacity will help mitigate further environmental damages and prevent a reduction in the county’s ecological carrying capacity. On the other hand, attempts to improve the indicators used to calculate the immigrant acceptance capacity might result in increased ecological stability for counties like Bandar Anzali, where the number of arriving migrants is less than the calculated index. Long-term sustainability of ecosystem services and ecological stability would be preserved with the help of such actions.

https://esrj.sbu.ac.ir/article_105828_6a71825dbf6d876764b845e0fd664e0b.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Classification of the new raster-based method for Iranian regional climate Classification of the new raster-based method for Iranian regional climate 71 90 105883 10.48308/esrj.2025.238444.1253 FA Mahmoud Ahmadi Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran 0000-0003-2574-5140 Mohammad Kamangar Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran 0000-0003-3005-8388 Journal Article 2025 01 23 Introduction Climate is a fundamental component of the Earth system that governs a wide array of environmental, ecological, and socio-economic processes. The diversity of climatic elements—including temperature, precipitation, humidity, wind, and solar radiation—plays a pivotal role in shaping regional and global climatic patterns. This variability results in the emergence of distinct climatic zones, each with unique environmental and socio-economic characteristics. Historically, the study of climate has intrigued scholars, scientists, and policy-makers alike, dating back to ancient civilizations that sought to understand weather phenomena to improve agricultural practices, navigation, and settlement planning. In contemporary times, with the escalation of global climate change, the importance of accurately understanding and classifying regional climates has grown exponentially. However, the diverse nature of climatic elements, coupled with their spatial and temporal variability, presents significant challenges in conducting integrated and simultaneous analyses. These challenges are further intensified in regional studies that span large geographical extents, diverse topographical features, and variable data quality from multiple stations or monitoring systems. Consequently, climate classification has emerged as an essential scientific tool, aiming to simplify this complexity by categorizing regions into coherent climatic zones based on statistical and environmental indicators. Such classifications are foundational for various applications, including ecological zoning, water resource management, urban planning, and climate adaptation strategies. In countries like Iran, where climatic diversity is pronounced due to a wide range of elevation zones, proximity to seas, and interaction with different atmospheric circulation systems, the need for accurate and region-specific climate classification becomes even more critical. Previous studies on Iran’s climate have employed traditional classification systems such as Köppen-Geiger, De Martonne, or Emberger indices, which, while useful, often fall short in capturing localized microclimatic variations and the dynamic influence of topographic features. Moreover, these classifications typically rely on long-term averages and may not incorporate recent trends linked to global climate change. This study addresses these gaps by adopting a data-driven and spatially explicit approach to classify the Iranian climate using long-term meteorological observations. By integrating advanced geostatistical methods and clustering techniques, this research aims to delineate coherent climatic zones across the country that reflect both macro- and micro-climatic influences. The outcomes not only contribute to the refinement of climatic classification in Iran but also serve as a crucial baseline for evaluating future climate trends and guiding decision-making in sectors such as agriculture, water resource management, tourism, and urban development. Materials and Methods The study area encompasses the entire territory of Iran, situated in the southwest of Asia and characterized by its complex topography, including mountain ranges such as Alborz and Zagros, vast deserts like Dasht-e Kavir and Dasht-e Lut, and coastal zones along the Caspian Sea, Persian Gulf, and the Sea of Oman. Classification of the new raster-based method for Iranian regional climate Ahmadi and Kamangar / 72 This geomorphological diversity significantly influences the distribution of climatic variables across the country. The research utilized ground-based meteorological data collected from 92 synoptic stations maintained by the Iran Meteorological Organization (IRIMO), covering a 40-year period from 1980 to 2019. The selected parameters included daily minimum temperature, maximum temperature, total precipitation, and relative humidity—each being a critical determinant of climatic conditions. To create continuous climatic surfaces from the discrete point data, the CoKriging interpolation technique was employed. This geostatistical method allows for the estimation of spatially distributed climatic variables by considering both the primary and secondary variables, thereby improving the accuracy of spatial predictions. The interpolation results were validated using cross-validation techniques to ensure reliability and minimize spatial bias. For classification, a multivariate clustering approach was adopted. Initially, all variables were normalized to ensure uniformity in the scale and to avoid dominance by any single variable. Then, the Euclidean distance metric was used to calculate the dissimilarity matrix among the observations. Hierarchical clustering with Ward's linkage method was applied, which minimizes the variance within each cluster. To determine the optimal number of clusters (i.e., climatic zones), various validity indices such as the Davies-Bouldin Index and the Silhouette Score were evaluated. Spatial analysis and visualization were performed in GIS environments using tools such as ArcGIS and QGIS, allowing for the integration of climatic data with elevation models, land cover maps, and hydrographic networks. This facilitated a nuanced understanding of the spatial relationships between climate zones and physiographic features. Results and Discussion The spatial distribution of climatic variables across Iran reveals substantial heterogeneity that reflects the interplay between latitude, elevation, proximity to water bodies, and the influence of prevailing wind patterns and atmospheric systems. The analysis showed that: Temperature: Maximum temperature values exhibit a clear gradient from north to south and from west to east. The southern and southeastern regions, including Sistan-Baluchestan, Kerman, and parts of Khuzestan, experience the highest maximum temperatures, often exceeding 45°C in summer. In contrast, the northwestern provinces such as West Azerbaijan and Kurdistan, influenced by higher elevations and continental air masses, record the lowest maximum temperatures, with winter temperatures frequently dropping below zero. Precipitation: Precipitation patterns are largely dictated by elevation and the presence of orographic barriers. The highest precipitation occurs along the southern Caspian coast and the western slopes of the Zagros Mountains. These regions benefit from moist air masses from the Caspian Sea and the Mediterranean, respectively. In contrast, central and southeastern Iran are characterized by hyper-arid conditions, with annual rainfall often below 100 mm, making them among the driest areas in the world. Humidity: Relative humidity is markedly higher in coastal regions—particularly the northern Caspian belt—due to maritime influences. In inland desert regions, low humidity values correspond with high evaporation rates and limited vegetation cover, intensifying aridity. Through cluster analysis, Iran was categorized into ten major climatic zones, each with distinctive climatic characteristics. These include: Western Caspian Coastal – High rainfall (>1100 mm), mild winters, and moderate summers. Zagros Highlands – Moderate rainfall and cold winters; high topographic variability. Northwestern Cold – Characterized by long, cold winters and moderate precipitation. Central Arid – Low precipitation (<100 mm), large temperature range. Eastern Highlands – Moderate elevation, low humidity, relatively cooler than adjacent deserts. Southeastern Arid – High temperatures (>25°C annual mean), minimal rainfall. Kerman-Sistan Semi-Arid – Transitional zone with variable rainfall and high temperature extremes. Southern Coastal – Maritime influence from the Persian Gulf; hot and humid summers. Khorasan Semi-Humid – Influenced by northern air masses; moderate rainfall. Central Plateau Margin – A zone of transition with mixed climatic signatures. Elevation and topographic complexity emerged as major determinants in shaping climatic diversity. The Alborz and Zagros Mountain ranges act as significant barriers, redirecting air flows and creating rain shadows that contribute to the development of microclimates. These features explain why even regions at similar latitudes can have vastly different climates, as is the case in southern Iran, where some areas are hot and humid while others are hot and arid. The classification also highlighted the influence of large-scale atmospheric systems such as the Subtropical Jet Stream, Mediterranean cyclones, and Indian Monsoon incursions, which periodically affect parts of Iran, contributing to seasonal rainfall variability and interannual extremes. Conclusion This study presents a novel, data-driven framework for the climatic classification of Iran based on long-term observational records and advanced spatial analysis. By integrating ground-based meteorological data from 92 synoptic stations over a 40-year period and employing geostatistical and clustering techniques, the research successfully delineated ten distinct climatic zones across the country. This classification reflects not only large-scale atmospheric circulation patterns but also the pronounced impact of topography, elevation, and proximity to water bodies, which contribute to the development of localized microclimates. Among the identified zones, the central-eastern arid region emerged as the most extensive, covering nearly one-third of the country, characterized by hot, dry conditions and minimal annual precipitation (~90 mm). In stark contrast, the western Caspian coastal zone, the smallest in spatial extent (~0.44% of Iran's area), was found to be the most humid and rain-rich region, receiving more than 1137 mm of precipitation annually. These sharp contrasts illustrate the climatic heterogeneity of Iran, driven by elevation gradients, wind systems, and land-sea interactions. Moreover, the study identified temperature and precipitation gradients across the country: temperature increases generally from north to south and west to east, while precipitation shows a reverse gradient, increasing from south to north and from east to west. The southeastern region was identified as the hottest zone, with average annual temperatures exceeding 25°C, whereas the northwestern highlands of Azerbaijan and Kurdistan exhibited the coldest conditions, with annual mean temperatures around 12°C. One of the major achievements of this study lies in overcoming the limitations of classical climate classification methods by using high-resolution spatial modeling techniques. The use of CoKriging interpolation minimized the errors typically associated with point-based station data, and the clustering method enabled the recognition of transitional climatic zones that are often overlooked in rigid classification systems like Köppen or Emberger. The implications of this work are far-reaching. Accurate identification of climatic zones provides a foundation for climate-informed decision-making in key sectors such as agriculture, water management, urban development, health, and disaster risk reduction. For example, agricultural planning can benefit from knowing the precise climatic needs of crops, urban infrastructure can be designed to better withstand local climatic stressors, and water resource allocations can be tailored to match the precipitation and evaporation patterns of each zone. Furthermore, this classification provides a baseline for monitoring future climate change. As global temperatures rise and precipitation patterns shift, tracking how and where Iran’s climatic zones evolve will be crucial for building adaptive capacity and resilience in both natural ecosystems and human systems. From a scientific perspective, the approach adopted here—combining long-term ground observations with spatial modeling and multivariate clustering—offers a replicable and scalable method for climatic classification in other topographically and climatically diverse regions. It can also serve as a base layer for more complex environmental modeling, such as hydrological simulations, ecological niche modeling, and climate change impact assessments. Introduction Climate is a fundamental component of the Earth system that governs a wide array of environmental, ecological, and socio-economic processes. The diversity of climatic elements—including temperature, precipitation, humidity, wind, and solar radiation—plays a pivotal role in shaping regional and global climatic patterns. This variability results in the emergence of distinct climatic zones, each with unique environmental and socio-economic characteristics. Historically, the study of climate has intrigued scholars, scientists, and policy-makers alike, dating back to ancient civilizations that sought to understand weather phenomena to improve agricultural practices, navigation, and settlement planning. In contemporary times, with the escalation of global climate change, the importance of accurately understanding and classifying regional climates has grown exponentially. However, the diverse nature of climatic elements, coupled with their spatial and temporal variability, presents significant challenges in conducting integrated and simultaneous analyses. These challenges are further intensified in regional studies that span large geographical extents, diverse topographical features, and variable data quality from multiple stations or monitoring systems. Consequently, climate classification has emerged as an essential scientific tool, aiming to simplify this complexity by categorizing regions into coherent climatic zones based on statistical and environmental indicators. Such classifications are foundational for various applications, including ecological zoning, water resource management, urban planning, and climate adaptation strategies. In countries like Iran, where climatic diversity is pronounced due to a wide range of elevation zones, proximity to seas, and interaction with different atmospheric circulation systems, the need for accurate and region-specific climate classification becomes even more critical. Previous studies on Iran’s climate have employed traditional classification systems such as Köppen-Geiger, De Martonne, or Emberger indices, which, while useful, often fall short in capturing localized microclimatic variations and the dynamic influence of topographic features. Moreover, these classifications typically rely on long-term averages and may not incorporate recent trends linked to global climate change. This study addresses these gaps by adopting a data-driven and spatially explicit approach to classify the Iranian climate using long-term meteorological observations. By integrating advanced geostatistical methods and clustering techniques, this research aims to delineate coherent climatic zones across the country that reflect both macro- and micro-climatic influences. The outcomes not only contribute to the refinement of climatic classification in Iran but also serve as a crucial baseline for evaluating future climate trends and guiding decision-making in sectors such as agriculture, water resource management, tourism, and urban development. Materials and Methods The study area encompasses the entire territory of Iran, situated in the southwest of Asia and characterized by its complex topography, including mountain ranges such as Alborz and Zagros, vast deserts like Dasht-e Kavir and Dasht-e Lut, and coastal zones along the Caspian Sea, Persian Gulf, and the Sea of Oman. Classification of the new raster-based method for Iranian regional climate Ahmadi and Kamangar / 72 This geomorphological diversity significantly influences the distribution of climatic variables across the country. The research utilized ground-based meteorological data collected from 92 synoptic stations maintained by the Iran Meteorological Organization (IRIMO), covering a 40-year period from 1980 to 2019. The selected parameters included daily minimum temperature, maximum temperature, total precipitation, and relative humidity—each being a critical determinant of climatic conditions. To create continuous climatic surfaces from the discrete point data, the CoKriging interpolation technique was employed. This geostatistical method allows for the estimation of spatially distributed climatic variables by considering both the primary and secondary variables, thereby improving the accuracy of spatial predictions. The interpolation results were validated using cross-validation techniques to ensure reliability and minimize spatial bias. For classification, a multivariate clustering approach was adopted. Initially, all variables were normalized to ensure uniformity in the scale and to avoid dominance by any single variable. Then, the Euclidean distance metric was used to calculate the dissimilarity matrix among the observations. Hierarchical clustering with Ward's linkage method was applied, which minimizes the variance within each cluster. To determine the optimal number of clusters (i.e., climatic zones), various validity indices such as the Davies-Bouldin Index and the Silhouette Score were evaluated. Spatial analysis and visualization were performed in GIS environments using tools such as ArcGIS and QGIS, allowing for the integration of climatic data with elevation models, land cover maps, and hydrographic networks. This facilitated a nuanced understanding of the spatial relationships between climate zones and physiographic features. Results and Discussion The spatial distribution of climatic variables across Iran reveals substantial heterogeneity that reflects the interplay between latitude, elevation, proximity to water bodies, and the influence of prevailing wind patterns and atmospheric systems. The analysis showed that: Temperature: Maximum temperature values exhibit a clear gradient from north to south and from west to east. The southern and southeastern regions, including Sistan-Baluchestan, Kerman, and parts of Khuzestan, experience the highest maximum temperatures, often exceeding 45°C in summer. In contrast, the northwestern provinces such as West Azerbaijan and Kurdistan, influenced by higher elevations and continental air masses, record the lowest maximum temperatures, with winter temperatures frequently dropping below zero. Precipitation: Precipitation patterns are largely dictated by elevation and the presence of orographic barriers. The highest precipitation occurs along the southern Caspian coast and the western slopes of the Zagros Mountains. These regions benefit from moist air masses from the Caspian Sea and the Mediterranean, respectively. In contrast, central and southeastern Iran are characterized by hyper-arid conditions, with annual rainfall often below 100 mm, making them among the driest areas in the world. Humidity: Relative humidity is markedly higher in coastal regions—particularly the northern Caspian belt—due to maritime influences. In inland desert regions, low humidity values correspond with high evaporation rates and limited vegetation cover, intensifying aridity. Through cluster analysis, Iran was categorized into ten major climatic zones, each with distinctive climatic characteristics. These include: Western Caspian Coastal – High rainfall (>1100 mm), mild winters, and moderate summers. Zagros Highlands – Moderate rainfall and cold winters; high topographic variability. Northwestern Cold – Characterized by long, cold winters and moderate precipitation. Central Arid – Low precipitation (<100 mm), large temperature range. Eastern Highlands – Moderate elevation, low humidity, relatively cooler than adjacent deserts. Southeastern Arid – High temperatures (>25°C annual mean), minimal rainfall. Kerman-Sistan Semi-Arid – Transitional zone with variable rainfall and high temperature extremes. Southern Coastal – Maritime influence from the Persian Gulf; hot and humid summers. Khorasan Semi-Humid – Influenced by northern air masses; moderate rainfall. Central Plateau Margin – A zone of transition with mixed climatic signatures. Elevation and topographic complexity emerged as major determinants in shaping climatic diversity. The Alborz and Zagros Mountain ranges act as significant barriers, redirecting air flows and creating rain shadows that contribute to the development of microclimates. These features explain why even regions at similar latitudes can have vastly different climates, as is the case in southern Iran, where some areas are hot and humid while others are hot and arid. The classification also highlighted the influence of large-scale atmospheric systems such as the Subtropical Jet Stream, Mediterranean cyclones, and Indian Monsoon incursions, which periodically affect parts of Iran, contributing to seasonal rainfall variability and interannual extremes. Conclusion This study presents a novel, data-driven framework for the climatic classification of Iran based on long-term observational records and advanced spatial analysis. By integrating ground-based meteorological data from 92 synoptic stations over a 40-year period and employing geostatistical and clustering techniques, the research successfully delineated ten distinct climatic zones across the country. This classification reflects not only large-scale atmospheric circulation patterns but also the pronounced impact of topography, elevation, and proximity to water bodies, which contribute to the development of localized microclimates. Among the identified zones, the central-eastern arid region emerged as the most extensive, covering nearly one-third of the country, characterized by hot, dry conditions and minimal annual precipitation (~90 mm). In stark contrast, the western Caspian coastal zone, the smallest in spatial extent (~0.44% of Iran's area), was found to be the most humid and rain-rich region, receiving more than 1137 mm of precipitation annually. These sharp contrasts illustrate the climatic heterogeneity of Iran, driven by elevation gradients, wind systems, and land-sea interactions. Moreover, the study identified temperature and precipitation gradients across the country: temperature increases generally from north to south and west to east, while precipitation shows a reverse gradient, increasing from south to north and from east to west. The southeastern region was identified as the hottest zone, with average annual temperatures exceeding 25°C, whereas the northwestern highlands of Azerbaijan and Kurdistan exhibited the coldest conditions, with annual mean temperatures around 12°C. One of the major achievements of this study lies in overcoming the limitations of classical climate classification methods by using high-resolution spatial modeling techniques. The use of CoKriging interpolation minimized the errors typically associated with point-based station data, and the clustering method enabled the recognition of transitional climatic zones that are often overlooked in rigid classification systems like Köppen or Emberger. The implications of this work are far-reaching. Accurate identification of climatic zones provides a foundation for climate-informed decision-making in key sectors such as agriculture, water management, urban development, health, and disaster risk reduction. For example, agricultural planning can benefit from knowing the precise climatic needs of crops, urban infrastructure can be designed to better withstand local climatic stressors, and water resource allocations can be tailored to match the precipitation and evaporation patterns of each zone. Furthermore, this classification provides a baseline for monitoring future climate change. As global temperatures rise and precipitation patterns shift, tracking how and where Iran’s climatic zones evolve will be crucial for building adaptive capacity and resilience in both natural ecosystems and human systems. From a scientific perspective, the approach adopted here—combining long-term ground observations with spatial modeling and multivariate clustering—offers a replicable and scalable method for climatic classification in other topographically and climatically diverse regions. It can also serve as a base layer for more complex environmental modeling, such as hydrological simulations, ecological niche modeling, and climate change impact assessments.

https://esrj.sbu.ac.ir/article_105883_b0cb22763cbfdc7c02d27fbfe89e6d86.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Magnetite and pyrite chemistry of the Kuh-e-Kapout porphyry copper deposit, southern part of the Urumieh-Dokhtar magmatic arc, north of Bam, Kerman province Magnetite and pyrite chemistry of the Kuh-e-Kapout porphyry copper deposit, southern part of the Urumieh-Dokhtar magmatic arc, north of Bam, Kerman province 91 112 106133 10.48308/esrj.2025.239628.1273 FA Alireza Zarasvandi Department of Geology, Faculty of Earth Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0000-0001-9821-6747 Nasim Haghighat Jou , Department of Geology, Faculty of Earth Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran Nader Taghipour Faculty of Earth Science, Damghan University, Damghan, Iran Mohsen Rezaei Department of Geology, Faculty of Earth Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran Mohammad Amiri Hoseini Golgohar Mining and Industrial Company, Kerman, Iran Journal Article 2025 04 26 Introduction Porphyry deposits are commonly associated with calc-alkaline to alkaline magmas (Seedorff et al, 2005). Oxidized magmas are essential for efficiently transporting copper, gold, molybdenum, and sulfur from the metasomatized mantle to the upper crust (Richards, 2015). Hydrothermal fluids released from magmas, particularly those of intermediate composition, form a series of magnetite-bearing quartz veins and potassic alteration complexes within and around intrusive rocks (Holliday and Cooke, 2007). Magnetite is an indicator mineral for porphyry deposits (Cooke et al, 2020). The presence of primary magnetite as phenocrysts or groundmass phases indicates the oxidized state of the magmas. The formation of hydrothermal magnetite in porphyry deposits involves the reaction between ferrous iron and water or sulfate, and leads to a decrease in pH and oxygen fugacity in the fluid, which causes pyrite precipitation (Sun et al, 2013). Studies on this mineral found that hydrothermal magnetites have high values of the Mg-Mn factor, in contrast, igneous magnetite can be distinguished by high values of Co, Ni, and V (Nadoll et al, 2012). Some studies focusing on the chemistry of trace elements of magnetite have indicating the factors affecting compositional differences in iron oxide and mineralization (Pisiak et al, 2017). The role of pyrite as an indicator of fluid composition changes in porphyry deposits has also been used (Reich et al, 2013). Therefore, changes in the hydrothermal fluid's physicochemical conditions and the mineral assemblages' thermodynamic stability cause in changes in the trace element content of the ore and gangue sulfides (here, the pyrite). The aim of this study is to investigate the trace element composition of igneous and hydrothermal magnetite and pyrite using electron microprobe analysis (EMPA) in the potassic, propylitic, and phyllic alteration zones and to investigate changes in the formation conditions of these minerals in the Kuh-e-Kapout porphyry copper deposit. Materials and Methods In order to petrological, mineralogical and chemical mineral studies, sampling was carried out from the cores of the quartz diorite unit (samples number NT-6-729 and NT-3-583) and microdiorite (sample number NT-4-638). At this stage, deep magnetite samples were selected from the potassic zone; also, a representative sample for electron microprobe analysis was selected from the surface microdiorite sample in the propylitic zone (NT-S-26). After preparing thin-polished sections of the samples, the samples were analyzed at the University of Leuven, Austria, with an EMPA Superprobe Jeol JXA 8200 electron microscope to determine the chemistry of magnetite. BSE images of the samples were also obtained using the same device. The measured values were calculated as weight percent using the Integrated Jeol Software of the aforementioned device. In order to perform EMPA studies on pyrite samples from quartz + pyrite veins, sample NT-3-195 of the quartz-diorite in phyllic alteration zone was used. Standards of pyrite were used to measuring Cu, Fe, and S. For other elements such as Au, Te, Zn, Ag, Se, As, and Co, the Jeol JXA 8200 device was used. The same device (Jeol JXA 8200) was used to prepare the WDS elemental map of pyrite and magnetite minerals. Results and Discussion Based on the data obtained from magnetite analysis, the amounts of Fe, Ti, V, and Al in the samples are higher than other elements, and the amounts of Cu, Mo, Ni, Ca, Co, Pt, Mg, and Pt are often lower than the detection limit. The Fe values range is between 40.88 to 68.92 wt%. The Fe average value (weight percent) in the fertile quartz diorite samples are 67.244, which is approximately similar to other porphyry copper deposits in the Urumieh-Dokhtar magmatic arc in Iran. The highest Al value is 0.318, and the lowest is 0.087 wt%, the highest V value is 2.722 wt%, the lowest is 0.181 wt%, and about Ti, the highest value is 20.66 wt%, and the lowest is 0.060 wt%. The microdiorite sample point the average V values in quartz diorite samples from the Kuh-e-Kapout porphyry copper deposit is 0.229 wt%, which is in good agreement with the V values of other porphyry copper deposits in the Urumieh-Dokhtar magmatic arc. NT-S-26 An4 simultaneously shows the highest titanium content (20.661 wt%) and the lowest iron content (40.880 wt%). Studied pyrite minerals contain a wide range of trace elements in their structure, Cu, Fe, As, S, Zn, and Pb are more important. The average weight percentage of sulfur is 21.54, and iron is 44.46. The average value of arsenic is 0.0193, and the average value of copper is 0.0063. The range of variation in the amounts of iron and sulfur, which are the main components of pyrite, is from 46.07-46.95 and 53.56-54.74 wt%, respectively. The amounts of Co, Os, Ti, and Pt elements are below the detection limit in most points. The amounts of major elements, iron, and sulfur, in the pyrites of the Kuh-e-Kapout copper deposit, are similar to other porphyry copper deposits in the Urumieh-Dokhtar magmatic arc. In the vanadium versus titanium diagram, the magnetite samples of quartz diorite are located in the hydrothermal and magmatic magnetites zones with a tendency towards the magmatic zone, and the microdiorite sample is located at the border of the magmatic magnetite zone. Using the V/Ti-Fe diagram, determined that the quartz-diorite samples reequilibrated, which could be due to the development of the potassic alteration zone and the presence of continuous stages of hydrothermal fluid exsolved during this alteration. The results of the magnetite data plot on the Mg+Al+Si vs. Ti diagram for both magnetite series indicate that no significant interaction between the magmatic fluid and the wall rock occurred during the formation and crystallization of magnetite. Studies shown that the amount of vanadium in magnetite is one of the most important indicators for measuring the oxygen fugacity (fO2) of magma or hydrothermal fluid during magnetite crystallization (Nadoll et al, 2014; Knipping et al, 2015). At higher fO2 conditions, the magnetites do not have high levels of vanadium (Canil and Lacourse, 2020). This change is due to the preference of V+3 in the magnetite structure over V+4 and V+5 in the under reduced conditions. Vanadium levels can also be affected by temperature and the reaction of the hydrothermal fluid and wall rock during magnetite crystallization (Knipping et al, 2015; Zarasvandi et al, 2023b). The petrography of the Kuh-e-Kapout magnetites and the chemistry of this mineral in the potassic zone indicate that mineralization occurred under oxidizing conditions. This observation is reflected in the occurrence of hematite rims in magnetite grains. This rim ty pically indicates magmatism with oxidizing conditions in the magnetite-hematite buffer zone (Liang et al, 2009). On the other hand, the abundance and presence of scattered magnetite grains in the groundmass and copper mineralization veins are significant. Also, the scattered occurrence and paragenesis of anhydrite with magnetite as a sulfate mineral indicate the occurrence of magmatism with high oxygen fugacity. Pyrite is a common mineral in a wide range of hydrothermal deposits. Its deposition can effectively control the segregation of a wide range of economically and environmental importance trace elements (Large et al, 2009). While As is a structurally limited element in pyrite, Cu and Au can occur both in solid solution and as micro to nanoscale chalcopyrite and Au (or Au-tellurides) in pyrite. The averages concentration of arsenic in pyrite samples is 0.019%, and the nearly positive correlation between S and As in pyrite samples indicates that arsenic is not substituted sulfur, which may indicate the presence of an oxidizing environment in which arsenic is present as As+3. The lack of definitive correlation between Fe and Cu in pyrite samples indicates the lack of Cu+2 substitution in the pyrite structure at the Fe+2 site, which suggests that much of the Cu in pyrite is structurally replaces Fe in octahedral sites, which could be due to the presence of As, Sb, and Co in the pyrite structure. Conclusion In this study, the chemistry of magnetite and pyrite minerals of the Kuh-e-Kapout porphyry copper deposit were studied for the first time. Studies have been done on two series of quartz diorite and microdiorite dike-like intrusions. Studying on these minerals is important to understanding the physicochemical conditions of the deposit formation during the crystallization of magnetite and sulfide minerals. The data obtained from the EPMA showed that the minerals have a good agreement with other porphyry copper deposits of the Urumieh Dokhtar magmatic arc in terms of elemental abundance. The magnetites studied in Kuh-e-Kapout porphyry copper deposit are magmatic type and re-equilibrated and have high temperatures (more than 500 ° C). Studies on magnetite chemistry indicate that in tetms of genesis, Kuh-e-Kapout deposit is located in the porphyry deposit area. A comparison of the two magmatic systems of quartz diorite and microdiorite specifically shows a more fertile quartz diorite magmatism in the potassic zone and an isothermal system with weak mineralization evidence in the propylitic zone for the microdiorite intrusion. In this magmatic-hydrothermal system, the abundant occurrence of anhydrite and martitization of magnetite is evidence of a high fO2 condition in the magmatic system of the region. The results of study on pyrite mineral indicate the presence of As+3 in the pyrite structure in the form of dispersed micro to nanoparticles, which is consistent with the oxidant conditions of mineral formation. The occurrence of copper in pyrite is in the form of replaces Fe in octahedral sites and in the form of chalcopyrite micro particles. Introduction Porphyry deposits are commonly associated with calc-alkaline to alkaline magmas (Seedorff et al, 2005). Oxidized magmas are essential for efficiently transporting copper, gold, molybdenum, and sulfur from the metasomatized mantle to the upper crust (Richards, 2015). Hydrothermal fluids released from magmas, particularly those of intermediate composition, form a series of magnetite-bearing quartz veins and potassic alteration complexes within and around intrusive rocks (Holliday and Cooke, 2007). Magnetite is an indicator mineral for porphyry deposits (Cooke et al, 2020). The presence of primary magnetite as phenocrysts or groundmass phases indicates the oxidized state of the magmas. The formation of hydrothermal magnetite in porphyry deposits involves the reaction between ferrous iron and water or sulfate, and leads to a decrease in pH and oxygen fugacity in the fluid, which causes pyrite precipitation (Sun et al, 2013). Studies on this mineral found that hydrothermal magnetites have high values of the Mg-Mn factor, in contrast, igneous magnetite can be distinguished by high values of Co, Ni, and V (Nadoll et al, 2012). Some studies focusing on the chemistry of trace elements of magnetite have indicating the factors affecting compositional differences in iron oxide and mineralization (Pisiak et al, 2017). The role of pyrite as an indicator of fluid composition changes in porphyry deposits has also been used (Reich et al, 2013). Therefore, changes in the hydrothermal fluid's physicochemical conditions and the mineral assemblages' thermodynamic stability cause in changes in the trace element content of the ore and gangue sulfides (here, the pyrite). The aim of this study is to investigate the trace element composition of igneous and hydrothermal magnetite and pyrite using electron microprobe analysis (EMPA) in the potassic, propylitic, and phyllic alteration zones and to investigate changes in the formation conditions of these minerals in the Kuh-e-Kapout porphyry copper deposit. Materials and Methods In order to petrological, mineralogical and chemical mineral studies, sampling was carried out from the cores of the quartz diorite unit (samples number NT-6-729 and NT-3-583) and microdiorite (sample number NT-4-638). At this stage, deep magnetite samples were selected from the potassic zone; also, a representative sample for electron microprobe analysis was selected from the surface microdiorite sample in the propylitic zone (NT-S-26). After preparing thin-polished sections of the samples, the samples were analyzed at the University of Leuven, Austria, with an EMPA Superprobe Jeol JXA 8200 electron microscope to determine the chemistry of magnetite. BSE images of the samples were also obtained using the same device. The measured values were calculated as weight percent using the Integrated Jeol Software of the aforementioned device. In order to perform EMPA studies on pyrite samples from quartz + pyrite veins, sample NT-3-195 of the quartz-diorite in phyllic alteration zone was used. Standards of pyrite were used to measuring Cu, Fe, and S. For other elements such as Au, Te, Zn, Ag, Se, As, and Co, the Jeol JXA 8200 device was used. The same device (Jeol JXA 8200) was used to prepare the WDS elemental map of pyrite and magnetite minerals. Results and Discussion Based on the data obtained from magnetite analysis, the amounts of Fe, Ti, V, and Al in the samples are higher than other elements, and the amounts of Cu, Mo, Ni, Ca, Co, Pt, Mg, and Pt are often lower than the detection limit. The Fe values range is between 40.88 to 68.92 wt%. The Fe average value (weight percent) in the fertile quartz diorite samples are 67.244, which is approximately similar to other porphyry copper deposits in the Urumieh-Dokhtar magmatic arc in Iran. The highest Al value is 0.318, and the lowest is 0.087 wt%, the highest V value is 2.722 wt%, the lowest is 0.181 wt%, and about Ti, the highest value is 20.66 wt%, and the lowest is 0.060 wt%. The microdiorite sample point the average V values in quartz diorite samples from the Kuh-e-Kapout porphyry copper deposit is 0.229 wt%, which is in good agreement with the V values of other porphyry copper deposits in the Urumieh-Dokhtar magmatic arc. NT-S-26 An4 simultaneously shows the highest titanium content (20.661 wt%) and the lowest iron content (40.880 wt%). Studied pyrite minerals contain a wide range of trace elements in their structure, Cu, Fe, As, S, Zn, and Pb are more important. The average weight percentage of sulfur is 21.54, and iron is 44.46. The average value of arsenic is 0.0193, and the average value of copper is 0.0063. The range of variation in the amounts of iron and sulfur, which are the main components of pyrite, is from 46.07-46.95 and 53.56-54.74 wt%, respectively. The amounts of Co, Os, Ti, and Pt elements are below the detection limit in most points. The amounts of major elements, iron, and sulfur, in the pyrites of the Kuh-e-Kapout copper deposit, are similar to other porphyry copper deposits in the Urumieh-Dokhtar magmatic arc. In the vanadium versus titanium diagram, the magnetite samples of quartz diorite are located in the hydrothermal and magmatic magnetites zones with a tendency towards the magmatic zone, and the microdiorite sample is located at the border of the magmatic magnetite zone. Using the V/Ti-Fe diagram, determined that the quartz-diorite samples reequilibrated, which could be due to the development of the potassic alteration zone and the presence of continuous stages of hydrothermal fluid exsolved during this alteration. The results of the magnetite data plot on the Mg+Al+Si vs. Ti diagram for both magnetite series indicate that no significant interaction between the magmatic fluid and the wall rock occurred during the formation and crystallization of magnetite. Studies shown that the amount of vanadium in magnetite is one of the most important indicators for measuring the oxygen fugacity (fO2) of magma or hydrothermal fluid during magnetite crystallization (Nadoll et al, 2014; Knipping et al, 2015). At higher fO2 conditions, the magnetites do not have high levels of vanadium (Canil and Lacourse, 2020). This change is due to the preference of V+3 in the magnetite structure over V+4 and V+5 in the under reduced conditions. Vanadium levels can also be affected by temperature and the reaction of the hydrothermal fluid and wall rock during magnetite crystallization (Knipping et al, 2015; Zarasvandi et al, 2023b). The petrography of the Kuh-e-Kapout magnetites and the chemistry of this mineral in the potassic zone indicate that mineralization occurred under oxidizing conditions. This observation is reflected in the occurrence of hematite rims in magnetite grains. This rim ty pically indicates magmatism with oxidizing conditions in the magnetite-hematite buffer zone (Liang et al, 2009). On the other hand, the abundance and presence of scattered magnetite grains in the groundmass and copper mineralization veins are significant. Also, the scattered occurrence and paragenesis of anhydrite with magnetite as a sulfate mineral indicate the occurrence of magmatism with high oxygen fugacity. Pyrite is a common mineral in a wide range of hydrothermal deposits. Its deposition can effectively control the segregation of a wide range of economically and environmental importance trace elements (Large et al, 2009). While As is a structurally limited element in pyrite, Cu and Au can occur both in solid solution and as micro to nanoscale chalcopyrite and Au (or Au-tellurides) in pyrite. The averages concentration of arsenic in pyrite samples is 0.019%, and the nearly positive correlation between S and As in pyrite samples indicates that arsenic is not substituted sulfur, which may indicate the presence of an oxidizing environment in which arsenic is present as As+3. The lack of definitive correlation between Fe and Cu in pyrite samples indicates the lack of Cu+2 substitution in the pyrite structure at the Fe+2 site, which suggests that much of the Cu in pyrite is structurally replaces Fe in octahedral sites, which could be due to the presence of As, Sb, and Co in the pyrite structure. Conclusion In this study, the chemistry of magnetite and pyrite minerals of the Kuh-e-Kapout porphyry copper deposit were studied for the first time. Studies have been done on two series of quartz diorite and microdiorite dike-like intrusions. Studying on these minerals is important to understanding the physicochemical conditions of the deposit formation during the crystallization of magnetite and sulfide minerals. The data obtained from the EPMA showed that the minerals have a good agreement with other porphyry copper deposits of the Urumieh Dokhtar magmatic arc in terms of elemental abundance. The magnetites studied in Kuh-e-Kapout porphyry copper deposit are magmatic type and re-equilibrated and have high temperatures (more than 500 ° C). Studies on magnetite chemistry indicate that in tetms of genesis, Kuh-e-Kapout deposit is located in the porphyry deposit area. A comparison of the two magmatic systems of quartz diorite and microdiorite specifically shows a more fertile quartz diorite magmatism in the potassic zone and an isothermal system with weak mineralization evidence in the propylitic zone for the microdiorite intrusion. In this magmatic-hydrothermal system, the abundant occurrence of anhydrite and martitization of magnetite is evidence of a high fO2 condition in the magmatic system of the region. The results of study on pyrite mineral indicate the presence of As+3 in the pyrite structure in the form of dispersed micro to nanoparticles, which is consistent with the oxidant conditions of mineral formation. The occurrence of copper in pyrite is in the form of replaces Fe in octahedral sites and in the form of chalcopyrite micro particles.

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Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Investigations of sulfide-gold mineralization and fluid inclusions in the Nabijan area, southwest of Kaleibar, East-Azarbaidjan province Investigations of sulfide-gold mineralization and fluid inclusions in the Nabijan area, southwest of Kaleibar, East-Azarbaidjan province 113 132 106134 10.48308/esrj.2025.232378.1190 FA Fatemeh Arbati Gonbari Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran Ghahraman Sohrabi Department of geology, Faculty of Sciences, University of Mohaghegh Ardabili, Ardabil, Iran 0000-0002-1676-3155 Seyed Ghafoor Alavi Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran 0000-0003-2210-6829 Ali Asghar Calagari Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran 0009-0004-4959-5866 Journal Article 2024 04 27 Introduction The Nabijan region is located 20 km southwest of Kalybar in East Azerbaijan Province and in northwest Iran. According to the divisions of Iranian structural zones (Aghanabati, 2004), this region is considered a part of the western part of the Alborz-Azerbaijan magmatic belt and is metallogenically located in the Ahar-Arasbaran metallogenic zone (Castro et al, 2013). The Ahar-Arasbaran metallogenic zone is one of the most important and richest metallogenic zones, especially for gold, copper and molybdenum, in the northwest and Iran (Jamali et al, 2012). So far, numerous geological studies have been carried out by various researchers on geochemistry, mineralogy and alteration in the Arasbaran zone and around the Nabijan region. Based on exploration studies conducted by Shekouei (2003), a promising area for gold and copper elements has been introduced in Nabi Jan. Based on the aforementioned studies (trenching and boring) by the Geological and Mineral Exploration Organization of the country, about 300 thousand tons of gold ore have been estimated in silica zones and veins with an average grade of 1.37 grams per ton. Previous researchers have conducted studies on geology, geochemistry, mineralization and alteration in the Nabi Jan region. In this paper, an attempt has been made to describe the geological, mineralization and alteration characteristics of the Nabi Jan region, and to conduct new studies for the first time based on fluid intermediates in quartz-sulfide veins - veinlets in order to determine the physicochemical conditions of mineralizing fluids and the genesis of the deposit. Materials and Methods This research consists of two parts: field and laboratory investigations. In field studies, mineralized veins were identified and their relationship with host rocks and alteration zones was investigated, and samples were taken for laboratory studies. In this regard, 60 samples were collected from host rock units and mineralizing outcrops. During laboratory studies, 20 thin sections and 5 polished sections were prepared and subjected to petrographic and mineralographic studies at the University of Tabriz. In order to understand the physicochemical nature of the mineralizing fluid and to investigate the process of chemical and temperature changes of mineralizing fluids during ore deposition, petrographic and thermometric studies of fluid intercalations were carried out on 5 samples containing quartz crystals cognate with sulfide mineralization and gold (taken from quartz veins). Thermometric measurements were performed using a Linkam THMSG600 fluid interface device connected to an OLYMPUS BX-51 microscope with LD-LensX40 and equipped with a TMS94 thermal controller and LNP cooler at Payam Noor University of Tabriz. The temperature range of the device is -190 (by liquid nitrogen) to +600 (by electrical energy) °C. Calibration of the device during the heating stage was performed with an accuracy of ±0.6 °C at +414 °C (melting temperature of cesium nitrate) and ±0.2 °C at -94.3 °C (melting temperature of n-hexane). The salinity of the fluid interfaces was calculated in terms of weight percent equivalent to common salt (wt% NaCl eq.) using the melting temperature of the last ice piece (Tmice) and using the equation (Hall et al, 1988). Geology and Mineralization The study area is part of the Lesser Caucasus-Arasbaran metallogenic zone. Magmatism in this metallogenic zone began during the Late Cretaceous and continued into the Cenozoic and Quaternary. Mineralization in this zone is mostly related to Cenozoic magmatic rocks. Cenozoic magmatic activities in the Arasbaran zone have led to the formation of alkaline to calc-alkaline plutons with porphyry, skarn, and epithermal mineral systems. The exposed rocks in the Nabi Jan area mostly consist of Cretaceous volcanic and sedimentary units that have been intruded by Oligocene intrusive masses with quartz-monzodiorite composition. Intrusive masses with dioritic to monzodiorite composition are the main factor of epithermal gold-silver mineralization in the Nabi Jan area and its surroundings from around the villages of Paigham and Alawiq to Jundshafq and Marzrud. Alteration zones around gold veins and zones in the Nabijan region include silicic, phyllic, and propolite types that extend to dimensions of 1 to 20 meters. Mineralization in the studied area has occurred in the form of stockwork and quartz veinlets-veins within the quartz monzodiorite host rock, which include sulfide minerals (pyrite, chalcopyrite, galena, and sphalerite) and native gold. Pyrite and chalcopyrite have been transformed into iron hydroxides (goethite and limonite) as a result of supergene processes. Quartz crystals within the quartz veinlets-veins exhibit comb and void-filling textures. Also, bipyramidal shape is common in crystalline quartz in this region. Based on the studies of the fluids involved, the homogenization temperature and salinity values of the fluid interlayers vary between 170 and 282 °C and 3.27 to 8.51% by weight of table salt, respectively. Based on the findings of the fluid interlayers, the boiling process is the most important process in the deposition of sulfide minerals and gold, and sulfide complexes have played a major role in the transport of ore elements. Based on the geological characteristics, mineralogical, structure, texture, and homogenization temperature and salinity values of the fluid interlayers, the Nabijan sulfide-gold mineralization can be classified as a low-sulfidation epithermal deposit. Results and Discussion Based on the studies conducted, the rock units exposed in the Nabijan region include Cretaceous volcanic and sedimentary rocks and Oligocene intrusive rocks. The Cretaceous volcanic and sedimentary rocks are cut by the Oligocene quartz-monzodiorite intrusion. The main minerals of this rock unit are plagioclase, potassium feldspar, and quartz, and its accessory minerals are biotite, amphibole, dark minerals, and rarely clinopyroxene. In the altered areas, most hornblens have been transformed into biotite and biotites into chlorite. The activity of hydrothermal fluids resulting from the intrusion has caused the formation of various siliceous, phyllic, and propolite alterations in the host rock. As a result of the passage of hydrothermal fluids along the fractured and faulted zones, while altering the quartz-monzodiorite host rock, it has caused the formation of stony zones and silica veins-veins. Gold-bearing silica veins and veins contain pyrite and small amounts of chalcopyrite, sphalerite and galena. Natural gold of 2 to 5 microns is observed in the altered parts of pyrite to iron hydroxide (goethite) in polished sections. Petrographic and thermometric studies of fluid intercalations were carried out inside coarse quartz crystals. Quartz crystals in these vein-veins show co-growth and twinning with sulfide minerals and gold. Fluid intercalations have polyhedral, elongated, acicular, spherical and negative crystal shapes and are observed as primary, secondary and pseudo-secondary intercalations from a paragenetic point of view. The size of the fluid interlayers varies from 10 to 24 microns. The studied fluid interlayers can be divided into 4 types: (1) two-phase liquid-vapor (L+V), (2) two-phase vapor-liquid (V+L), (3) single-phase vapor (V), and (4) single-phase liquid (L). The frequency of two-phase liquid-rich interlayers is higher than the other interlayers. Thermometric studies were carried out on two-phase liquid-rich interlayers during two cooling and heating stages. The range of homogenization temperatures for two-phase liquid-rich interlayers was between 170 and 282 °C. Based on the values of these temperatures, the salinity of the two-phase liquid-rich interlayers ranges from 3.27 to 8.51 with an average of 75.5% by weight equivalent to table salt. Based on the bivariate plot of salinity versus homogenization temperature, the points corresponding to the thermometry findings of the fluid intercalations at Nabijan show a trend of approximately threefold increase in salinity (from 3.2% to 8.5%) accompanied by a significant decrease in temperature (from 282°C to 170°C), which is somewhat similar to the boiling trend. Also, the presence of single-phase liquid intercalations could indicate that the activity of hydrothermal fluids continued down to temperatures below 70°C. The ice melting temperatures range from -2 to -5.5°C, which correspond to salinities between 3.27 and 8.51% by weight of table salt. The homogenization temperature-salinity trend is consistent with the boiling of ore-forming fluids. It seems that the deposition of sulfides and gold occurred during the same boiling process. Considering the average homogenization temperatures and salinities of the fluid interlayers and using the diagram of fluid interlayer pressure (during homogenization) versus homogenization temperature, the minimum fluid pressure during the deposition of waste and ore minerals was about 25 bar. Considering the occurrence of fluid boiling, this pressure should be considered hydrostatic, which is equivalent to a depth of about 250 meters. This depth can be considered as the lowest depth of sulfide and gold mineralization in the Nabijan area. According to the temperature-salinity diagram (Wilkinson, 2001), sulfide-gold mineralization in the Nabijan area is in the epithermal range. Conclusion Mineralization in the Nabijan area occurred in the form of stockwork and quartz veinlets within the quartz monzodiorite host rock. The epithermal gold mineralization agent is most likely a buried intrusive mass from which only the silica veins and veinlets originating from it were able to cut the quartz monzodiorite mass. Silicic, phyllic and propolite alterations have developed around the quartz veinlets. The quartz veinlets contain sulfide mineralization (pyrite, chalcopyrite, galena and sphalerite) and native gold, and the quartz crystals within these veinlets show comb and void-filling textures. The homogenization temperature of the two-phase fluid interlayers present in the mineralized quartz is in the range of 170 to 282 with the highest frequency between 170 and 210 °C. The geological characteristics, structure, and texture of mineralization and alteration zones, along with microthermometric findings in the Nabi Jan area, are consistent with low-temperature epithermal gold mineralization. Introduction The Nabijan region is located 20 km southwest of Kalybar in East Azerbaijan Province and in northwest Iran. According to the divisions of Iranian structural zones (Aghanabati, 2004), this region is considered a part of the western part of the Alborz-Azerbaijan magmatic belt and is metallogenically located in the Ahar-Arasbaran metallogenic zone (Castro et al, 2013). The Ahar-Arasbaran metallogenic zone is one of the most important and richest metallogenic zones, especially for gold, copper and molybdenum, in the northwest and Iran (Jamali et al, 2012). So far, numerous geological studies have been carried out by various researchers on geochemistry, mineralogy and alteration in the Arasbaran zone and around the Nabijan region. Based on exploration studies conducted by Shekouei (2003), a promising area for gold and copper elements has been introduced in Nabi Jan. Based on the aforementioned studies (trenching and boring) by the Geological and Mineral Exploration Organization of the country, about 300 thousand tons of gold ore have been estimated in silica zones and veins with an average grade of 1.37 grams per ton. Previous researchers have conducted studies on geology, geochemistry, mineralization and alteration in the Nabi Jan region. In this paper, an attempt has been made to describe the geological, mineralization and alteration characteristics of the Nabi Jan region, and to conduct new studies for the first time based on fluid intermediates in quartz-sulfide veins - veinlets in order to determine the physicochemical conditions of mineralizing fluids and the genesis of the deposit. Materials and Methods This research consists of two parts: field and laboratory investigations. In field studies, mineralized veins were identified and their relationship with host rocks and alteration zones was investigated, and samples were taken for laboratory studies. In this regard, 60 samples were collected from host rock units and mineralizing outcrops. During laboratory studies, 20 thin sections and 5 polished sections were prepared and subjected to petrographic and mineralographic studies at the University of Tabriz. In order to understand the physicochemical nature of the mineralizing fluid and to investigate the process of chemical and temperature changes of mineralizing fluids during ore deposition, petrographic and thermometric studies of fluid intercalations were carried out on 5 samples containing quartz crystals cognate with sulfide mineralization and gold (taken from quartz veins). Thermometric measurements were performed using a Linkam THMSG600 fluid interface device connected to an OLYMPUS BX-51 microscope with LD-LensX40 and equipped with a TMS94 thermal controller and LNP cooler at Payam Noor University of Tabriz. The temperature range of the device is -190 (by liquid nitrogen) to +600 (by electrical energy) °C. Calibration of the device during the heating stage was performed with an accuracy of ±0.6 °C at +414 °C (melting temperature of cesium nitrate) and ±0.2 °C at -94.3 °C (melting temperature of n-hexane). The salinity of the fluid interfaces was calculated in terms of weight percent equivalent to common salt (wt% NaCl eq.) using the melting temperature of the last ice piece (Tmice) and using the equation (Hall et al, 1988). Geology and Mineralization The study area is part of the Lesser Caucasus-Arasbaran metallogenic zone. Magmatism in this metallogenic zone began during the Late Cretaceous and continued into the Cenozoic and Quaternary. Mineralization in this zone is mostly related to Cenozoic magmatic rocks. Cenozoic magmatic activities in the Arasbaran zone have led to the formation of alkaline to calc-alkaline plutons with porphyry, skarn, and epithermal mineral systems. The exposed rocks in the Nabi Jan area mostly consist of Cretaceous volcanic and sedimentary units that have been intruded by Oligocene intrusive masses with quartz-monzodiorite composition. Intrusive masses with dioritic to monzodiorite composition are the main factor of epithermal gold-silver mineralization in the Nabi Jan area and its surroundings from around the villages of Paigham and Alawiq to Jundshafq and Marzrud. Alteration zones around gold veins and zones in the Nabijan region include silicic, phyllic, and propolite types that extend to dimensions of 1 to 20 meters. Mineralization in the studied area has occurred in the form of stockwork and quartz veinlets-veins within the quartz monzodiorite host rock, which include sulfide minerals (pyrite, chalcopyrite, galena, and sphalerite) and native gold. Pyrite and chalcopyrite have been transformed into iron hydroxides (goethite and limonite) as a result of supergene processes. Quartz crystals within the quartz veinlets-veins exhibit comb and void-filling textures. Also, bipyramidal shape is common in crystalline quartz in this region. Based on the studies of the fluids involved, the homogenization temperature and salinity values of the fluid interlayers vary between 170 and 282 °C and 3.27 to 8.51% by weight of table salt, respectively. Based on the findings of the fluid interlayers, the boiling process is the most important process in the deposition of sulfide minerals and gold, and sulfide complexes have played a major role in the transport of ore elements. Based on the geological characteristics, mineralogical, structure, texture, and homogenization temperature and salinity values of the fluid interlayers, the Nabijan sulfide-gold mineralization can be classified as a low-sulfidation epithermal deposit. Results and Discussion Based on the studies conducted, the rock units exposed in the Nabijan region include Cretaceous volcanic and sedimentary rocks and Oligocene intrusive rocks. The Cretaceous volcanic and sedimentary rocks are cut by the Oligocene quartz-monzodiorite intrusion. The main minerals of this rock unit are plagioclase, potassium feldspar, and quartz, and its accessory minerals are biotite, amphibole, dark minerals, and rarely clinopyroxene. In the altered areas, most hornblens have been transformed into biotite and biotites into chlorite. The activity of hydrothermal fluids resulting from the intrusion has caused the formation of various siliceous, phyllic, and propolite alterations in the host rock. As a result of the passage of hydrothermal fluids along the fractured and faulted zones, while altering the quartz-monzodiorite host rock, it has caused the formation of stony zones and silica veins-veins. Gold-bearing silica veins and veins contain pyrite and small amounts of chalcopyrite, sphalerite and galena. Natural gold of 2 to 5 microns is observed in the altered parts of pyrite to iron hydroxide (goethite) in polished sections. Petrographic and thermometric studies of fluid intercalations were carried out inside coarse quartz crystals. Quartz crystals in these vein-veins show co-growth and twinning with sulfide minerals and gold. Fluid intercalations have polyhedral, elongated, acicular, spherical and negative crystal shapes and are observed as primary, secondary and pseudo-secondary intercalations from a paragenetic point of view. The size of the fluid interlayers varies from 10 to 24 microns. The studied fluid interlayers can be divided into 4 types: (1) two-phase liquid-vapor (L+V), (2) two-phase vapor-liquid (V+L), (3) single-phase vapor (V), and (4) single-phase liquid (L). The frequency of two-phase liquid-rich interlayers is higher than the other interlayers. Thermometric studies were carried out on two-phase liquid-rich interlayers during two cooling and heating stages. The range of homogenization temperatures for two-phase liquid-rich interlayers was between 170 and 282 °C. Based on the values of these temperatures, the salinity of the two-phase liquid-rich interlayers ranges from 3.27 to 8.51 with an average of 75.5% by weight equivalent to table salt. Based on the bivariate plot of salinity versus homogenization temperature, the points corresponding to the thermometry findings of the fluid intercalations at Nabijan show a trend of approximately threefold increase in salinity (from 3.2% to 8.5%) accompanied by a significant decrease in temperature (from 282°C to 170°C), which is somewhat similar to the boiling trend. Also, the presence of single-phase liquid intercalations could indicate that the activity of hydrothermal fluids continued down to temperatures below 70°C. The ice melting temperatures range from -2 to -5.5°C, which correspond to salinities between 3.27 and 8.51% by weight of table salt. The homogenization temperature-salinity trend is consistent with the boiling of ore-forming fluids. It seems that the deposition of sulfides and gold occurred during the same boiling process. Considering the average homogenization temperatures and salinities of the fluid interlayers and using the diagram of fluid interlayer pressure (during homogenization) versus homogenization temperature, the minimum fluid pressure during the deposition of waste and ore minerals was about 25 bar. Considering the occurrence of fluid boiling, this pressure should be considered hydrostatic, which is equivalent to a depth of about 250 meters. This depth can be considered as the lowest depth of sulfide and gold mineralization in the Nabijan area. According to the temperature-salinity diagram (Wilkinson, 2001), sulfide-gold mineralization in the Nabijan area is in the epithermal range. Conclusion Mineralization in the Nabijan area occurred in the form of stockwork and quartz veinlets within the quartz monzodiorite host rock. The epithermal gold mineralization agent is most likely a buried intrusive mass from which only the silica veins and veinlets originating from it were able to cut the quartz monzodiorite mass. Silicic, phyllic and propolite alterations have developed around the quartz veinlets. The quartz veinlets contain sulfide mineralization (pyrite, chalcopyrite, galena and sphalerite) and native gold, and the quartz crystals within these veinlets show comb and void-filling textures. The homogenization temperature of the two-phase fluid interlayers present in the mineralized quartz is in the range of 170 to 282 with the highest frequency between 170 and 210 °C. The geological characteristics, structure, and texture of mineralization and alteration zones, along with microthermometric findings in the Nabi Jan area, are consistent with low-temperature epithermal gold mineralization.

https://esrj.sbu.ac.ir/article_106134_9e9292e59a7847298e109836d36ae5a2.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Determination of petrophysical rock types and permeability using machine learning methods in a heterogeneous reservoir, southwest of the Iran Determination of petrophysical rock types and permeability using machine learning methods in a heterogeneous reservoir, southwest of the Iran 149 167 105473 10.48308/esrj.2025.232122.1181 FA Fatemeh Bahrami Department of Petroleum Geology and Sedimentary Basins, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran Iman Zahmatkesh Department of Petroleum Geology and Sedimentary Basins, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0000-0002-4419-5255 Journal Article 2024 05 17 Introduction The study and evaluation of hydrocarbon reservoirs, as vital arteries for energy supply in today's world, are of paramount importance. Petroleum engineers and geologists are constantly striving to understand these complex subsurface systems more accurately and comprehensively. These efforts are particularly significant for carbonate reservoirs, due to their unique characteristics and specific challenges. Carbonate reservoirs, owing to complex diagenetic processes, inherent heterogeneities, and extensive fracture structures, are among the most intricate types of hydrocarbon reservoirs. These complexities make accurate evaluation of petrophysical properties and prediction of their production behavior a major challenge. In this context, identifying and determining petrophysical rock types and their characteristics, including porosity, permeability, fluid saturation, and pore size distribution, play a key role in understanding reservoir behavior and optimizing production processes. Rock types, as fundamental building blocks of the reservoir, exhibit relatively homogeneous petrophysical properties within a defined volume of rock. Identifying and differentiating these types enables more accurate reservoir modeling and prediction of its behavior under different production conditions. However, precise determination of rock types in carbonate reservoirs, due to the high diversity of textures, structures, and diagenetic processes, requires the use of advanced and integrated methods. Traditional reservoir evaluation methods rely primarily on core data and well log information. Core data provides valuable information about the physical and chemical properties of the reservoir rock, but its preparation and analysis are costly and time-consuming, and it is usually limited to a small number of wells in the oil field. Well log information provides broader coverage in the field, but its interpretation requires specialized knowledge and experience, and its accuracy may be affected by various factors. For this reason, the use of modern techniques such as machine learning and clustering has received increasing attention as a powerful tool for analyzing reservoir data and extracting valuable information from it. In this study, with the aim of overcoming the limitations of traditional methods and improving the accuracy of carbonate reservoir evaluation, self-organizing maps (SOM) have been used to cluster well log data and identify electrofacies. Using unsupervised learning algorithms, this method is able to identify hidden patterns in the data and classify similar data into separate groups. Electrofacies, as distinct petrophysical units in the reservoir, exhibit relatively uniform log characteristics and can be considered as representatives of rock types. By matching the identified electrofacies with core data and geological information, a more accurate model of the distribution of rock types in the reservoir can be created and its petrophysical properties can be estimated with greater precision. The ultimate goal of this study is to provide an efficient and reliable method for evaluating carbonate reservoirs using machine learning techniques and improving the accuracy of predicting their production behavior. Materials and Methods In this study, an integrated approach comprising machine learning-based clustering methods and artificial neural networks was employed to determine petrophysical rock types and estimate permeability in the Bangestan reservoir, located in southwestern Iran. This approach, utilizing well log data and core information, enables more accurate and efficient identification of reservoir characteristics. The dataset used in this research includes information from nine wells in the Ahvaz oil field. Among these, five wells have core data (including porosity and permeability information) and have been used as reference wells for training and validating the models. The well logs used in this study include density (RHOB), neutron (NPHI), effective porosity (PHIE), sonic transit time (DT), and gamma (GR) logs. These logs, due to their wide coverage and sensitivity to petrophysical changes, have been selected as the main inputs for clustering and permeability estimation algorithms. To determine petrophysical rock types, the Self-Organizing Maps (SOM) clustering method was used. This method, using an unsupervised learning algorithm, is able to classify similar data into separate groups. In this study, well log data from reference wells were input into the SOM network, and after training the network, the data were divided into 25 initial clusters. Then, by analyzing the petrophysical characteristics of each cluster and matching them with core information and hydraulic flow units, similar clusters were merged and, finally, five distinct petrophysical rock types were identified. Hydraulic flow units, as a criterion for evaluating reservoir quality, were determined using the logarithm of the flow zone indicator (Log FZI) method. This method, using core porosity and permeability data, enables the separation of flow units with different hydraulic characteristics. Matching the identified electrofacies with hydraulic flow units, as a validation method, helped ensure the accuracy and precision of the clustering. To estimate permeability in the studied reservoir, artificial neural networks (ANN) were used. This method, using a supervised learning algorithm, is able to learn the relationship between input data (well logs) and output data (core permeability) and, based on that, estimate permeability in other parts of the reservoir. In this study, a multi-layer perceptron neural network with a hidden layer was used. The reference well data were divided into training and testing sets. The training set was used to train the network and adjust its weights, and the testing set was used to evaluate the network's performance and determine the accuracy of permeability estimation. Permeability was estimated in two separate ways: 1) permeability estimation for the entire reservoir interval regardless of rock types, and 2) permeability estimation for each of the identified rock types separately. Comparing the results of these two methods allows for evaluating the impact of data clustering on the accuracy of permeability estimation. To evaluate the performance of the clustering and permeability estimation models, various statistical measures were used. To evaluate clustering accuracy, the Silhouette index was used, and to evaluate permeability estimation accuracy, the correlation coefficient (R) and root mean squared error (RMSE) measures were used. These measures enable comparison of the performance of different models and determination of the best model for permeability estimation in the studied reservoir. Results and Discussion In this study, we successfully identified five distinct petrophysical rock types in the Bangestan reservoir using machine learning methods. These rock types were accurately determined using the SOM clustering algorithm and matching with core data and hydraulic flow units. The clustering results showed that each of these rock types has unique petrophysical characteristics that affect fluid flow behavior in the reservoir. Rock types 1 and 2 had the best reservoir quality, rock type 3 had the medium reservoir quality and rock types 4 and 5 had the lowest reservoir quality. Matching the identified electrofacies with hydraulic flow units (FZI) showed a high correlation between the two. This correlation indicates that the SOM clustering method was well able to separate flow units with different hydraulic characteristics. The results of permeability estimation using artificial neural networks (ANN) showed that this method is able to estimate permeability with acceptable accuracy. Comparison of permeability estimation results with core data showed that the correlation coefficient (R) between the estimated values and the actual values is around 0.9804. Also, the root mean square error (RMSE) is around 0.0778. Comparing the results of permeability estimation for the entire reservoir interval with the results of permeability estimation for each of the rock types separately showed that data clustering has a positive effect on the accuracy of permeability estimation. In other words, permeability estimation for each of the rock types separately has higher accuracy than permeability estimation for the entire reservoir interval. This result shows that considering the petrophysical characteristics of each of the rock types can help improve the accuracy of permeability estimation models. The results of this study show that the use of machine learning methods can help improve the accuracy and efficiency of carbonate reservoir evaluation. The SOM clustering method, as a powerful tool for identifying petrophysical rock types, enables more accurate reservoir modeling and prediction of its production behavior. Also, artificial neural networks (ANN), as an efficient method for estimating permeability, enable quantitative evaluation of reservoir characteristics and optimization of production processes. However, it should be noted that the results of this study are limited to the Bangestan reservoir in the Ahvaz oil field and may not be generalizable to other carbonate reservoirs. To generalize the results of this study to other reservoirs, more studies and examination of data related to those reservoirs are needed. Conclusion In this study, an integrated machine learning-based approach was presented for identifying petrophysical rock types and estimating permeability in the Bangestan carbonate reservoir. Using the SOM clustering algorithm, five distinct rock types were identified, each with unique petrophysical characteristics and flow behaviors. ANN models, trained separately for each rock type, were able to estimate permeability with acceptable accuracy. The results showed that data clustering and considering the petrophysical characteristics of each rock type significantly improved the accuracy of permeability estimation. This approach can be used as an efficient tool for evaluating carbonate reservoirs and optimizing production processes. This study significantly enhances our understanding of the Bangestan reservoir characteristics and can serve as a foundation for developing more advanced models in hydrocarbon reservoir evaluation. The findings of this research may also contribute to optimizing production processes and managing oil resources, paving the way for future studies in this field. Introduction The study and evaluation of hydrocarbon reservoirs, as vital arteries for energy supply in today's world, are of paramount importance. Petroleum engineers and geologists are constantly striving to understand these complex subsurface systems more accurately and comprehensively. These efforts are particularly significant for carbonate reservoirs, due to their unique characteristics and specific challenges. Carbonate reservoirs, owing to complex diagenetic processes, inherent heterogeneities, and extensive fracture structures, are among the most intricate types of hydrocarbon reservoirs. These complexities make accurate evaluation of petrophysical properties and prediction of their production behavior a major challenge. In this context, identifying and determining petrophysical rock types and their characteristics, including porosity, permeability, fluid saturation, and pore size distribution, play a key role in understanding reservoir behavior and optimizing production processes. Rock types, as fundamental building blocks of the reservoir, exhibit relatively homogeneous petrophysical properties within a defined volume of rock. Identifying and differentiating these types enables more accurate reservoir modeling and prediction of its behavior under different production conditions. However, precise determination of rock types in carbonate reservoirs, due to the high diversity of textures, structures, and diagenetic processes, requires the use of advanced and integrated methods. Traditional reservoir evaluation methods rely primarily on core data and well log information. Core data provides valuable information about the physical and chemical properties of the reservoir rock, but its preparation and analysis are costly and time-consuming, and it is usually limited to a small number of wells in the oil field. Well log information provides broader coverage in the field, but its interpretation requires specialized knowledge and experience, and its accuracy may be affected by various factors. For this reason, the use of modern techniques such as machine learning and clustering has received increasing attention as a powerful tool for analyzing reservoir data and extracting valuable information from it. In this study, with the aim of overcoming the limitations of traditional methods and improving the accuracy of carbonate reservoir evaluation, self-organizing maps (SOM) have been used to cluster well log data and identify electrofacies. Using unsupervised learning algorithms, this method is able to identify hidden patterns in the data and classify similar data into separate groups. Electrofacies, as distinct petrophysical units in the reservoir, exhibit relatively uniform log characteristics and can be considered as representatives of rock types. By matching the identified electrofacies with core data and geological information, a more accurate model of the distribution of rock types in the reservoir can be created and its petrophysical properties can be estimated with greater precision. The ultimate goal of this study is to provide an efficient and reliable method for evaluating carbonate reservoirs using machine learning techniques and improving the accuracy of predicting their production behavior. Materials and Methods In this study, an integrated approach comprising machine learning-based clustering methods and artificial neural networks was employed to determine petrophysical rock types and estimate permeability in the Bangestan reservoir, located in southwestern Iran. This approach, utilizing well log data and core information, enables more accurate and efficient identification of reservoir characteristics. The dataset used in this research includes information from nine wells in the Ahvaz oil field. Among these, five wells have core data (including porosity and permeability information) and have been used as reference wells for training and validating the models. The well logs used in this study include density (RHOB), neutron (NPHI), effective porosity (PHIE), sonic transit time (DT), and gamma (GR) logs. These logs, due to their wide coverage and sensitivity to petrophysical changes, have been selected as the main inputs for clustering and permeability estimation algorithms. To determine petrophysical rock types, the Self-Organizing Maps (SOM) clustering method was used. This method, using an unsupervised learning algorithm, is able to classify similar data into separate groups. In this study, well log data from reference wells were input into the SOM network, and after training the network, the data were divided into 25 initial clusters. Then, by analyzing the petrophysical characteristics of each cluster and matching them with core information and hydraulic flow units, similar clusters were merged and, finally, five distinct petrophysical rock types were identified. Hydraulic flow units, as a criterion for evaluating reservoir quality, were determined using the logarithm of the flow zone indicator (Log FZI) method. This method, using core porosity and permeability data, enables the separation of flow units with different hydraulic characteristics. Matching the identified electrofacies with hydraulic flow units, as a validation method, helped ensure the accuracy and precision of the clustering. To estimate permeability in the studied reservoir, artificial neural networks (ANN) were used. This method, using a supervised learning algorithm, is able to learn the relationship between input data (well logs) and output data (core permeability) and, based on that, estimate permeability in other parts of the reservoir. In this study, a multi-layer perceptron neural network with a hidden layer was used. The reference well data were divided into training and testing sets. The training set was used to train the network and adjust its weights, and the testing set was used to evaluate the network's performance and determine the accuracy of permeability estimation. Permeability was estimated in two separate ways: 1) permeability estimation for the entire reservoir interval regardless of rock types, and 2) permeability estimation for each of the identified rock types separately. Comparing the results of these two methods allows for evaluating the impact of data clustering on the accuracy of permeability estimation. To evaluate the performance of the clustering and permeability estimation models, various statistical measures were used. To evaluate clustering accuracy, the Silhouette index was used, and to evaluate permeability estimation accuracy, the correlation coefficient (R) and root mean squared error (RMSE) measures were used. These measures enable comparison of the performance of different models and determination of the best model for permeability estimation in the studied reservoir. Results and Discussion In this study, we successfully identified five distinct petrophysical rock types in the Bangestan reservoir using machine learning methods. These rock types were accurately determined using the SOM clustering algorithm and matching with core data and hydraulic flow units. The clustering results showed that each of these rock types has unique petrophysical characteristics that affect fluid flow behavior in the reservoir. Rock types 1 and 2 had the best reservoir quality, rock type 3 had the medium reservoir quality and rock types 4 and 5 had the lowest reservoir quality. Matching the identified electrofacies with hydraulic flow units (FZI) showed a high correlation between the two. This correlation indicates that the SOM clustering method was well able to separate flow units with different hydraulic characteristics. The results of permeability estimation using artificial neural networks (ANN) showed that this method is able to estimate permeability with acceptable accuracy. Comparison of permeability estimation results with core data showed that the correlation coefficient (R) between the estimated values and the actual values is around 0.9804. Also, the root mean square error (RMSE) is around 0.0778. Comparing the results of permeability estimation for the entire reservoir interval with the results of permeability estimation for each of the rock types separately showed that data clustering has a positive effect on the accuracy of permeability estimation. In other words, permeability estimation for each of the rock types separately has higher accuracy than permeability estimation for the entire reservoir interval. This result shows that considering the petrophysical characteristics of each of the rock types can help improve the accuracy of permeability estimation models. The results of this study show that the use of machine learning methods can help improve the accuracy and efficiency of carbonate reservoir evaluation. The SOM clustering method, as a powerful tool for identifying petrophysical rock types, enables more accurate reservoir modeling and prediction of its production behavior. Also, artificial neural networks (ANN), as an efficient method for estimating permeability, enable quantitative evaluation of reservoir characteristics and optimization of production processes. However, it should be noted that the results of this study are limited to the Bangestan reservoir in the Ahvaz oil field and may not be generalizable to other carbonate reservoirs. To generalize the results of this study to other reservoirs, more studies and examination of data related to those reservoirs are needed. Conclusion In this study, an integrated machine learning-based approach was presented for identifying petrophysical rock types and estimating permeability in the Bangestan carbonate reservoir. Using the SOM clustering algorithm, five distinct rock types were identified, each with unique petrophysical characteristics and flow behaviors. ANN models, trained separately for each rock type, were able to estimate permeability with acceptable accuracy. The results showed that data clustering and considering the petrophysical characteristics of each rock type significantly improved the accuracy of permeability estimation. This approach can be used as an efficient tool for evaluating carbonate reservoirs and optimizing production processes. This study significantly enhances our understanding of the Bangestan reservoir characteristics and can serve as a foundation for developing more advanced models in hydrocarbon reservoir evaluation. The findings of this research may also contribute to optimizing production processes and managing oil resources, paving the way for future studies in this field.

https://esrj.sbu.ac.ir/article_105473_a748a0e335be081a7d0334e25755359e.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 Evolution and genesis of the Bardeh-Rash iron deposit in northwest Baneh (Northwestern Sanandaj–Sirjan Zone): Based on geological, mineralogical, and geochemical studies Evolution and genesis of the Bardeh-Rash iron deposit in northwest Baneh (Northwestern Sanandaj–Sirjan Zone): Based on geological, mineralogical, and geochemical studies 168 192 106135 10.48308/esrj.2025.239603.1271 FA Zahra Nazari Rahigh Departments of Geology, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran Hosseinali Tajeddin Departments of Geology, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran 0009-0006-9641-0101 Rastad Ebrahim Departments of Geology, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran Mohammadreza Afshoon Departments of Geology, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran Journal Article 2025 04 25 Introduction The Bardeh-Rash iron mineralisation is located approximately 30 km northwest of Baneh and 7 km north of Bardeh Rash village, within the Sanandaj–Sirjan tectonic zone. This NW–SE trending belt is one of the most significant metallogenic provinces in Iran, hosting a wide spectrum of iron deposits, including volcanic-sedimentary, skarn-type, and IOCG (iron oxide–copper–gold) deposits (Nabatian et al, 2015). Tectonic evolution associated with the opening and closure of the Neotethys Ocean and the interplay of extensional and compressional regimes during the Triassic to Jurassic led to intense regional metamorphism and deformation (Saki, 2010). Recent investigations in the northwestern segment of the Sanandaj–Sirjan zone have led to the identification of several iron occurrences and deposits. Notable examples include the Gurgur, Halab, Kosaj and Mianaj iron deposits (Pourmohammadi et al, 2019), as well as the Ghaluzendan and Qaderabad iron deposits (Karimi et al, 2021), all hosted within metamorphosed equivalents of the Kahar Formation. The Bardeh-Rash deposit, discovered by locals in 2016, represents one of the previously unstudied iron occurrences in this region. The mineralisation is stratiform in nature, hosted within upper Precambrian metarhyolitic tuff units and exhibits concordant layering and foliation with its metamorphosed volcanic host rocks. The primary objective of this study is to provide a comprehensive geological, mineralogical, geochemical, and genetic framework for the Bardeh Rash iron deposit, aiming to propose a viable exploration model for similar geological settings. Materials and Methods This research comprises both field-based and laboratory investigations. During fieldwork, a 5 km² area was systematically mapped at a scale of 1:5000, wherein lithological units, structural features, and mineralised horizons were carefully delineated. Over 100 rock samples, including both barren and ore-bearing lithologies, were collected. Structural measurements of bedding, foliation, and geometry of ore horizons were recorded to reconstruct the geometry of the mineralised system. In the laboratory, 12 thin sections were prepared for petrographic analysis and 33 polished thin sections for ore microscopy. For geochemical analysis, 31 representative samples were selected; 21 were analysed via XRF at Tarbiat Modares University and 10 by ICP-MS at ZarAzma Analytical Laboratories. Results and Discussion Field and laboratory investigations reveal that iron mineralisation occurs in three distinct stratiform horizons within metarhyolitic tuff units. Horizon I is hosted in dark grey metarhyolite and comprises banded hematite with an outcrop length of \~20 m and thickness ranging from 10 to 40 cm. Horizon II, the main ore body, occurs in light grey metarhyolite, is \~200 m long and 0.5 to 2.5 m thick, and hosts massive, banded, and disseminated hematite textures. Horizon III is composed of magnetite and hematite lenses hosted in dark green metarhyolite, with dimensions of \~30 m in length and 0.2 to 1.5 m in thickness. The metallic assemblage, primarily hematite and magnetite with minor pyrite, occurs in banded, massive, and disseminated textures, accompanied by gangue minerals such as quartz and barite within the altered metavolcanic host rocks. Evidence of metamorphism and deformation—including foliation, folding, boudinage, and S–C fabrics—is well developed in both ore bodies and host rocks. Geochemical analyses indicate that the host rocks are metarhyolitic tuffs of calc-alkaline affinity, formed in a volcanic arc setting on an active continental margin. Tectonomagmatic discrimination diagrams (e.g., Nb/Yb vs. Th/Yb) confirm a subduction-related magmatic source. Enrichment in large ion lithophile elements (LILEs) such as K, Rb, and Ba, together with positive Ba anomalie, implies a significant role for crustal contamination. REE patterns show light REE enrichment and positive Eu anomalies, indicative of oxidising conditions during ore formation. Mineralogical and paragenetic data suggest four main stages of deposit evolution: Syn-volcanic mineralisation with deposition of banded and disseminated hematite and magnetite during tuff emplacement. Regional metamorphism under greenschist facies, resulting in the formation of sericite, chlorite, and recrystallisation of pre-existing phases. Tectonic deformation, leading to the development of boudinage structures, folding, faulting, and pressure shadow features. Supergene alteration, during which surface weathering led to oxidation of primary sulfide and oxide phases and the formation of iron hydroxides. These features are consistent with other volcanogenic–sedimentary iron deposits in Iran, such as those in the Bukan and Takab districts, supporting a sedimentary–hydrothermal origin. Conclusion The Bardeh-Rash iron deposit is interpreted as a deformed and metamorphosed volcanic-sedimentary system that formed contemporaneously with late Precambrian rhyolitic volcanic activity. Subsequent metamorphic overprint and deformation significantly modified the primary textures and structures. Stratiform and lensoidal geometry of the ore horizons, coupled with enrichment in LILEs and anomalies in Ta and Ba, support a sedimentary–hydrothermal genesis within a subduction-related volcanic arc setting. REE patterns further confirm oxidising conditions and a genetic link between the host rocks and mineralisation. The recognition of four distinct mineralisation stages provides a robust framework for understanding the ore-forming processes. The results of this study contribute to the metallogenic models of iron in the Sanandaj–Sirjan zone and offer valuable insights for exploration strategies in analogous tectonic and geological settings. Introduction The Bardeh-Rash iron mineralisation is located approximately 30 km northwest of Baneh and 7 km north of Bardeh Rash village, within the Sanandaj–Sirjan tectonic zone. This NW–SE trending belt is one of the most significant metallogenic provinces in Iran, hosting a wide spectrum of iron deposits, including volcanic-sedimentary, skarn-type, and IOCG (iron oxide–copper–gold) deposits (Nabatian et al, 2015). Tectonic evolution associated with the opening and closure of the Neotethys Ocean and the interplay of extensional and compressional regimes during the Triassic to Jurassic led to intense regional metamorphism and deformation (Saki, 2010). Recent investigations in the northwestern segment of the Sanandaj–Sirjan zone have led to the identification of several iron occurrences and deposits. Notable examples include the Gurgur, Halab, Kosaj and Mianaj iron deposits (Pourmohammadi et al, 2019), as well as the Ghaluzendan and Qaderabad iron deposits (Karimi et al, 2021), all hosted within metamorphosed equivalents of the Kahar Formation. The Bardeh-Rash deposit, discovered by locals in 2016, represents one of the previously unstudied iron occurrences in this region. The mineralisation is stratiform in nature, hosted within upper Precambrian metarhyolitic tuff units and exhibits concordant layering and foliation with its metamorphosed volcanic host rocks. The primary objective of this study is to provide a comprehensive geological, mineralogical, geochemical, and genetic framework for the Bardeh Rash iron deposit, aiming to propose a viable exploration model for similar geological settings. Materials and Methods This research comprises both field-based and laboratory investigations. During fieldwork, a 5 km² area was systematically mapped at a scale of 1:5000, wherein lithological units, structural features, and mineralised horizons were carefully delineated. Over 100 rock samples, including both barren and ore-bearing lithologies, were collected. Structural measurements of bedding, foliation, and geometry of ore horizons were recorded to reconstruct the geometry of the mineralised system. In the laboratory, 12 thin sections were prepared for petrographic analysis and 33 polished thin sections for ore microscopy. For geochemical analysis, 31 representative samples were selected; 21 were analysed via XRF at Tarbiat Modares University and 10 by ICP-MS at ZarAzma Analytical Laboratories. Results and Discussion Field and laboratory investigations reveal that iron mineralisation occurs in three distinct stratiform horizons within metarhyolitic tuff units. Horizon I is hosted in dark grey metarhyolite and comprises banded hematite with an outcrop length of \~20 m and thickness ranging from 10 to 40 cm. Horizon II, the main ore body, occurs in light grey metarhyolite, is \~200 m long and 0.5 to 2.5 m thick, and hosts massive, banded, and disseminated hematite textures. Horizon III is composed of magnetite and hematite lenses hosted in dark green metarhyolite, with dimensions of \~30 m in length and 0.2 to 1.5 m in thickness. The metallic assemblage, primarily hematite and magnetite with minor pyrite, occurs in banded, massive, and disseminated textures, accompanied by gangue minerals such as quartz and barite within the altered metavolcanic host rocks. Evidence of metamorphism and deformation—including foliation, folding, boudinage, and S–C fabrics—is well developed in both ore bodies and host rocks. Geochemical analyses indicate that the host rocks are metarhyolitic tuffs of calc-alkaline affinity, formed in a volcanic arc setting on an active continental margin. Tectonomagmatic discrimination diagrams (e.g., Nb/Yb vs. Th/Yb) confirm a subduction-related magmatic source. Enrichment in large ion lithophile elements (LILEs) such as K, Rb, and Ba, together with positive Ba anomalie, implies a significant role for crustal contamination. REE patterns show light REE enrichment and positive Eu anomalies, indicative of oxidising conditions during ore formation. Mineralogical and paragenetic data suggest four main stages of deposit evolution: Syn-volcanic mineralisation with deposition of banded and disseminated hematite and magnetite during tuff emplacement. Regional metamorphism under greenschist facies, resulting in the formation of sericite, chlorite, and recrystallisation of pre-existing phases. Tectonic deformation, leading to the development of boudinage structures, folding, faulting, and pressure shadow features. Supergene alteration, during which surface weathering led to oxidation of primary sulfide and oxide phases and the formation of iron hydroxides. These features are consistent with other volcanogenic–sedimentary iron deposits in Iran, such as those in the Bukan and Takab districts, supporting a sedimentary–hydrothermal origin. Conclusion The Bardeh-Rash iron deposit is interpreted as a deformed and metamorphosed volcanic-sedimentary system that formed contemporaneously with late Precambrian rhyolitic volcanic activity. Subsequent metamorphic overprint and deformation significantly modified the primary textures and structures. Stratiform and lensoidal geometry of the ore horizons, coupled with enrichment in LILEs and anomalies in Ta and Ba, support a sedimentary–hydrothermal genesis within a subduction-related volcanic arc setting. REE patterns further confirm oxidising conditions and a genetic link between the host rocks and mineralisation. The recognition of four distinct mineralisation stages provides a robust framework for understanding the ore-forming processes. The results of this study contribute to the metallogenic models of iron in the Sanandaj–Sirjan zone and offer valuable insights for exploration strategies in analogous tectonic and geological settings.

https://esrj.sbu.ac.ir/article_106135_0628c0f305576249f866b2ff542dd076.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 3 2025 08 23 An insight into the Pila Spi Formation and the speleothems of KunaBa Cave in this formation located in Iraqi Kurdistan based on isotopic findings An insight into the Pila Spi Formation and the speleothems of KunaBa Cave in this formation located in Iraqi Kurdistan based on isotopic findings 193 210 105844 10.48308/esrj.2025.239466.1269 FA Ali Lotfi Bakhsh Department of Geology, Faculty of Sciences, University of Mohaghegh Ardabili, Ardabil, Iran 0000-0003-2780-5303 Journal Article 2025 04 18 Introduction Caves are a product of karstification, during which relatively soluble rocks such as limestone are dissolved by downward-penetrating meteoric waters that have interacted with a soil horizon containing high levels of CO2. Speleothems are secondary carbonates formed in caves, such as stalactites and stalagmites. Speleothems, which are predominantly calcite in composition, form when carbonate-saturated groundwater percolates downward into a cave at a CO2 partial pressure higher than the cave atmosphere and becomes supersaturated with respect to calcium carbonate by degassing or evaporation (Harmon et al, 2004). Their carbon and oxygen isotope compositions are among the most important tracers in paleoclimate studies and reconstruction of the paleogeological environment (Valley and Cole, 2001). Due to the simple geometry, relatively rapid growth rate, and tendency to precipitate near isotopic equilibrium with dripwater, stalagmites are the subject of most isotopic studies. Oxygen isotopes reflect the δ18O of the meteoric water dripping into the cave and the temperature dependence of the water-calcite isotopic fractionation. Materials and Methods The KunaBa Cave is located in northeastern Iraq, in the Sulaymaniyah Governorate of the Kurdistan Region, northwest of the Darbandikhan city. This area is located on the High Folded Zagros Belt, based on the subdivision of Iraqi structural units (Fouad and Sissakian, 2011). The development of this belt began in the Late Cretaceous with the subduction of the Arabian Plate margin crust from the Campanian to the Paleocene and culminated in the Neogene with the continental collision between the Arabian block and central Iran (Saura et al, 2015). The Pila Spi Formation sequence, which forms the two limbs of the Golan anticline, represents the upper part of the stratigraphic supersequence of the Arabian Plate, deposited in the Middle and Late Eocene on an uplifted zone during the final stage of subduction and closure of the remnants of the Neotethys Ocean (Al-Banna et al, 2015).During the field observation, samples were taken from the Pila Spi Formation and speleothems, including two well-layered stalagmites, from inside the KunaBa Cave. XRD analysis was performed on one sample to determine the mineralogical composition of the speleothems. In order to determine the δD of the stalagmite-forming fluid, two samples of the fluid inclusins were analyzed using the Cavity-Ring-Down spectroscopy method. To determine the carbon and oxygen isotope values, 14 samples from the Pila Spi Formation and 2 samples from two distinct layers of each stalagmite were analyzed. For dating, the isotopic values and ratios of U and Th were determined in 2 stalagmite samples using thermal ionization mass spectrometry (TIMS). Results and Discussion The Pila Spi Formation consists of two units in its type section. The upper unit, 57 m thick, consists of white crystalline layered bituminous limestone with bands of pale green marl or chalky marl containing chert nodules with good fossil traces. The lower part, which is 28 meters thick, consists of well-bedded white or porous bituminous limestone with weak fossil traces. The petrographic study of carbonate units in the Pila Spi Formation shows the presence of skeletal and non-skeletal grains. The main carbonate matrix of the Pila Spi Formation is carbonate mud (micrite), which has been heavily dolomitized and transformed into microspar by neomorphism. The abundance of micrite and benthic foraminifera in the facies of the Pila Spi Formation indicates its deposition in a shallow marine environment (Ali and Mohamed, 2013). KunaBa cave is located on the Golan Anticline at 45°38′47″E and 35°09′32″N. This anticline, which is composed of the Pila Spi Formation, is a narrow structure about 1 km wide and 10 km long with a northeast-southwest trend. The entrance to the KunaBa cave is very narrow and small, but it then opens into halls covered with beautiful deposits including stalactites, stalagmites and limestone waterfalls. There is no clear information about the main passages of this cave, less than one kilometer of which has been explored. XRD analysis revealed that the speleothems were composed of calcite. The samples have a layered structure in microscopic thin sections, indicating annual calcite deposition. Analysis of stable carbon and oxygen isotope ratios is a widely used method in paleoenvironmental studies, as these ratios reflect the depositional environment and usually vary across stratigraphic boundaries (Guo et al, 2010). In seawater, the amount of δ18O increases with increasing salinity (Wang et al, 2014). Because 16O preferentially evaporates and becomes atmospheric precipitation, the remaining seawater, which is now higher in salinity, becomes enriched in 18O. Using the empirical equation of Keith and Weber (1964) (Z = 2.048 × (δ13C(PDB) + 50) + 0.498 × (δ18O(PDB) + 50)), which is a criterion for distinguishing between marine and non-marine carbonates using δ13C and δ18O values in limestones, it was determined that the Pila Spi Formation is of marine origin (Z > 120). The δ13C and δ18O values of the Pila Spi Formation samples are negative, with mean values of −0.34‰ and −0.5‰, respectively. The oxygen isotope values in this Formation are heavier than those in marine carbonate sediments. The heavier oxygen isotope values in the Pila Spi Formation could be due to brine associated with an evaporite basin, during which the oxygen isotopic content of the basin becomes heavier than that of seawater. There is a significant correlation between the δ13C and δ18O values of carbonate rocks in a closed saline environment. The more closed the system, the higher the correlation coefficient (Wang et al, 2014). The correlation coefficient in the carbonate of the Pila Spi Formation (r = 0.921) indicates a remarkably strong correlation and a closed system. Oxygen and carbon isotopes provide the primary basis for reconstructing the temperature or precipitation history of a site from speleothems. When the movement of air and water in a cave is relatively slow, a thermal equilibrium is established between the temperature of the bedrock and the cave air (Bradley, 2015). As a result when speleothems are deposited under isotopic equilibrium conditions, the δ18O of speleothem calcite reflects both changes in the δ18O of its dripwater and changes in cave air temperature. As a result, this principle can be used to reconstruct cave air temperature, which in many caves is related to the annual surface air temperature (Wigley and Brown, 1976). Paleotemperature determinations based on isotopic studies are only reliable if calcite (or aragonite) precipitates in isotopic equilibrium with the dripping water. This can be assessed by determining whether δ18O values are constant throughout a growth layer. If the values are different for the same layer, it indicates that the sediment has been affected by evaporation, not just slow CO2 degassing, and this changes the simple temperature-dependent fractionation relationship (Bradley, 2015). The acceptable limit for speleothems deposited in isotopic equilibrium is 0.5‰ for δ18O variations and a maximum of 0.7 for the linear correlation coefficient between δ18O and δ13C along a layer (Lauritzen, 1995; Linge et al, 2001). The results of the isotopic analysis of the stalagmites indicate that they formed under equilibrium conditions and during a slow CO2 degassing process. The key advantage of speleothems in the field of paleoclimate studies is the possibility of accurately dating them to half a million years using U–Th-based methods (Cheng et al, 2013). The age of the stalagmites has been estimated to be 30 ± 1 and 25 ± 1 thousand years using the values and isotopic ratios of U and Th in two stalagmites. The ages of the samples were calculated using Isoplot/Ex (version 3.0) (Ludwig, 2003), a plotting and regression program designed for radioisotope data. Conclusion Given that stalagmites formed under isotopic equilibrium conditions, their oxygen isotope data can be used to determine the cave temperature at two time intervals obtained from the U–Th results. For this purpose, the Sharp equation (2007) was used, which is based on the oxygen isotope fractionation between speleothem (δ18Oc) and dripwater (δ18Ow) based on the ambient temperature (T, °C): T (°C) =15.75 – 4.3(δ18Ocalcite(PDB) – δ18Owater(SMOW)) + 0.14(δ18Ocalcite(PDB) – δ18Owater(SMOW))2 Since this equation contains two unknowns (T and δ18Owater) and only one measured value (δ18Ocalcite), isotopic data from the water droplets at the time of stalagmite formation are needed to obtain the temperature. The δD value of the fluid inclusions was used to calculate its δ18O value using the equation δD = 7.68 Í δ18O + 6.26 (Affolter et al, 2025). Since the δ18O content of the fluid inclusion may have undergone isotopic exchange with the surrounding calcite, the δD values obtained from the fluids inclusions in the two stalagmites were −54.21 and −57.17, respectively. Thus, the δ18O values of the fluid of the two stalagmites were determined to be −7.87 and −8.26, respectively. By inserting the values into the Sharpe's equation, the cave temperature during the formation time of the two stalagmites was obtained as 10.9 and 12.1, respectively. Currently, the average annual air temperature in the Darbandikhan region has been recorded as 22.41°C over the past two decades between 2000 and 2020 (Kalloshy and Sharbazhery, 2023). The global average temperature during this period was 0.72°C (NOAA, 2024). It seems that despite limited data, the calculated annual mean temperatures between 25 and 30 thousand years ago for the study area are in acceptable with the global mean temperature of about -8°C (Petit et al, 1999) in these two time periods. Introduction Caves are a product of karstification, during which relatively soluble rocks such as limestone are dissolved by downward-penetrating meteoric waters that have interacted with a soil horizon containing high levels of CO2. Speleothems are secondary carbonates formed in caves, such as stalactites and stalagmites. Speleothems, which are predominantly calcite in composition, form when carbonate-saturated groundwater percolates downward into a cave at a CO2 partial pressure higher than the cave atmosphere and becomes supersaturated with respect to calcium carbonate by degassing or evaporation (Harmon et al, 2004). Their carbon and oxygen isotope compositions are among the most important tracers in paleoclimate studies and reconstruction of the paleogeological environment (Valley and Cole, 2001). Due to the simple geometry, relatively rapid growth rate, and tendency to precipitate near isotopic equilibrium with dripwater, stalagmites are the subject of most isotopic studies. Oxygen isotopes reflect the δ18O of the meteoric water dripping into the cave and the temperature dependence of the water-calcite isotopic fractionation. Materials and Methods The KunaBa Cave is located in northeastern Iraq, in the Sulaymaniyah Governorate of the Kurdistan Region, northwest of the Darbandikhan city. This area is located on the High Folded Zagros Belt, based on the subdivision of Iraqi structural units (Fouad and Sissakian, 2011). The development of this belt began in the Late Cretaceous with the subduction of the Arabian Plate margin crust from the Campanian to the Paleocene and culminated in the Neogene with the continental collision between the Arabian block and central Iran (Saura et al, 2015). The Pila Spi Formation sequence, which forms the two limbs of the Golan anticline, represents the upper part of the stratigraphic supersequence of the Arabian Plate, deposited in the Middle and Late Eocene on an uplifted zone during the final stage of subduction and closure of the remnants of the Neotethys Ocean (Al-Banna et al, 2015).During the field observation, samples were taken from the Pila Spi Formation and speleothems, including two well-layered stalagmites, from inside the KunaBa Cave. XRD analysis was performed on one sample to determine the mineralogical composition of the speleothems. In order to determine the δD of the stalagmite-forming fluid, two samples of the fluid inclusins were analyzed using the Cavity-Ring-Down spectroscopy method. To determine the carbon and oxygen isotope values, 14 samples from the Pila Spi Formation and 2 samples from two distinct layers of each stalagmite were analyzed. For dating, the isotopic values and ratios of U and Th were determined in 2 stalagmite samples using thermal ionization mass spectrometry (TIMS). Results and Discussion The Pila Spi Formation consists of two units in its type section. The upper unit, 57 m thick, consists of white crystalline layered bituminous limestone with bands of pale green marl or chalky marl containing chert nodules with good fossil traces. The lower part, which is 28 meters thick, consists of well-bedded white or porous bituminous limestone with weak fossil traces. The petrographic study of carbonate units in the Pila Spi Formation shows the presence of skeletal and non-skeletal grains. The main carbonate matrix of the Pila Spi Formation is carbonate mud (micrite), which has been heavily dolomitized and transformed into microspar by neomorphism. The abundance of micrite and benthic foraminifera in the facies of the Pila Spi Formation indicates its deposition in a shallow marine environment (Ali and Mohamed, 2013). KunaBa cave is located on the Golan Anticline at 45°38′47″E and 35°09′32″N. This anticline, which is composed of the Pila Spi Formation, is a narrow structure about 1 km wide and 10 km long with a northeast-southwest trend. The entrance to the KunaBa cave is very narrow and small, but it then opens into halls covered with beautiful deposits including stalactites, stalagmites and limestone waterfalls. There is no clear information about the main passages of this cave, less than one kilometer of which has been explored. XRD analysis revealed that the speleothems were composed of calcite. The samples have a layered structure in microscopic thin sections, indicating annual calcite deposition. Analysis of stable carbon and oxygen isotope ratios is a widely used method in paleoenvironmental studies, as these ratios reflect the depositional environment and usually vary across stratigraphic boundaries (Guo et al, 2010). In seawater, the amount of δ18O increases with increasing salinity (Wang et al, 2014). Because 16O preferentially evaporates and becomes atmospheric precipitation, the remaining seawater, which is now higher in salinity, becomes enriched in 18O. Using the empirical equation of Keith and Weber (1964) (Z = 2.048 × (δ13C(PDB) + 50) + 0.498 × (δ18O(PDB) + 50)), which is a criterion for distinguishing between marine and non-marine carbonates using δ13C and δ18O values in limestones, it was determined that the Pila Spi Formation is of marine origin (Z > 120). The δ13C and δ18O values of the Pila Spi Formation samples are negative, with mean values of −0.34‰ and −0.5‰, respectively. The oxygen isotope values in this Formation are heavier than those in marine carbonate sediments. The heavier oxygen isotope values in the Pila Spi Formation could be due to brine associated with an evaporite basin, during which the oxygen isotopic content of the basin becomes heavier than that of seawater. There is a significant correlation between the δ13C and δ18O values of carbonate rocks in a closed saline environment. The more closed the system, the higher the correlation coefficient (Wang et al, 2014). The correlation coefficient in the carbonate of the Pila Spi Formation (r = 0.921) indicates a remarkably strong correlation and a closed system. Oxygen and carbon isotopes provide the primary basis for reconstructing the temperature or precipitation history of a site from speleothems. When the movement of air and water in a cave is relatively slow, a thermal equilibrium is established between the temperature of the bedrock and the cave air (Bradley, 2015). As a result when speleothems are deposited under isotopic equilibrium conditions, the δ18O of speleothem calcite reflects both changes in the δ18O of its dripwater and changes in cave air temperature. As a result, this principle can be used to reconstruct cave air temperature, which in many caves is related to the annual surface air temperature (Wigley and Brown, 1976). Paleotemperature determinations based on isotopic studies are only reliable if calcite (or aragonite) precipitates in isotopic equilibrium with the dripping water. This can be assessed by determining whether δ18O values are constant throughout a growth layer. If the values are different for the same layer, it indicates that the sediment has been affected by evaporation, not just slow CO2 degassing, and this changes the simple temperature-dependent fractionation relationship (Bradley, 2015). The acceptable limit for speleothems deposited in isotopic equilibrium is 0.5‰ for δ18O variations and a maximum of 0.7 for the linear correlation coefficient between δ18O and δ13C along a layer (Lauritzen, 1995; Linge et al, 2001). The results of the isotopic analysis of the stalagmites indicate that they formed under equilibrium conditions and during a slow CO2 degassing process. The key advantage of speleothems in the field of paleoclimate studies is the possibility of accurately dating them to half a million years using U–Th-based methods (Cheng et al, 2013). The age of the stalagmites has been estimated to be 30 ± 1 and 25 ± 1 thousand years using the values and isotopic ratios of U and Th in two stalagmites. The ages of the samples were calculated using Isoplot/Ex (version 3.0) (Ludwig, 2003), a plotting and regression program designed for radioisotope data. Conclusion Given that stalagmites formed under isotopic equilibrium conditions, their oxygen isotope data can be used to determine the cave temperature at two time intervals obtained from the U–Th results. For this purpose, the Sharp equation (2007) was used, which is based on the oxygen isotope fractionation between speleothem (δ18Oc) and dripwater (δ18Ow) based on the ambient temperature (T, °C): T (°C) =15.75 – 4.3(δ18Ocalcite(PDB) – δ18Owater(SMOW)) + 0.14(δ18Ocalcite(PDB) – δ18Owater(SMOW))2 Since this equation contains two unknowns (T and δ18Owater) and only one measured value (δ18Ocalcite), isotopic data from the water droplets at the time of stalagmite formation are needed to obtain the temperature. The δD value of the fluid inclusions was used to calculate its δ18O value using the equation δD = 7.68 Í δ18O + 6.26 (Affolter et al, 2025). Since the δ18O content of the fluid inclusion may have undergone isotopic exchange with the surrounding calcite, the δD values obtained from the fluids inclusions in the two stalagmites were −54.21 and −57.17, respectively. Thus, the δ18O values of the fluid of the two stalagmites were determined to be −7.87 and −8.26, respectively. By inserting the values into the Sharpe's equation, the cave temperature during the formation time of the two stalagmites was obtained as 10.9 and 12.1, respectively. Currently, the average annual air temperature in the Darbandikhan region has been recorded as 22.41°C over the past two decades between 2000 and 2020 (Kalloshy and Sharbazhery, 2023). The global average temperature during this period was 0.72°C (NOAA, 2024). It seems that despite limited data, the calculated annual mean temperatures between 25 and 30 thousand years ago for the study area are in acceptable with the global mean temperature of about -8°C (Petit et al, 1999) in these two time periods.

https://esrj.sbu.ac.ir/article_105844_59a80c7da44ff8b453810b257039fef3.pdf