Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2026 02 20 Petrography, geochemistry and dating of the Seydal area granitoid bodies (South-east of Birjand), Southern Khorasan Petrography, geochemistry and dating of the Seydal area granitoid bodies (South-east of Birjand), Southern Khorasan 1 23 100784 10.48308/esrj.2021.100784 FA Iman Araadfar Department of Geology, Faculty of Sciences, University of Birjand, Birjand, Iran Mohammad Hossein Zarrin Koub Department of Geology, Faculty of Sciences, University of Birjand, Birjand, Iran Seyyed Saeid Mohammadi Department of Geology, Faculty of Sciences, University of Birjand, Birjand, Iran Ebrahim Qolami Department of Geology, Faculty of Sciences, University of Birjand, Birjand, Iran Sun-Lin Chung Department of Geosciences, National Taiwan University, Taipei, Taiwan Afsaneh Rashidpoor Department of Geology, Faculty of Sciences, University of Birjand, Birjand, Iran Journal Article 2023 05 14 <strong>Introduction</strong> The Seydal granitoid bodies is located in eastern Iran on the western boundary of the Sistan suture zone. This zone represents remnants of the lithosphere of an oceanic basin that formed through processes of continental collision and ocean closure. Numerous granitoid intrusions with varying ages, ranging from the Cretaceous to the Eocene, have been identified in this zone. The Seydal granitoid, situated near the village of Seydal, approximately 150 km southeast of Birjand, was previously described in studies as a plagiogranite, gneiss, and leucogranite with an Upper Cretaceous age. Based on new evidence suggesting a younger age for this intrusion, this study aims to investigate the petrography, geochemical analysis, and geochronology of the Seydal granitoid. <strong> </strong> <strong>Materials and Methods</strong> The Seydal region lies in the northwestern part of the Sistan suture zone. This zone is characterized as an accretionary complex formed by the subduction of the Sistan oceanic lithosphere. The ophiolitic complex of the area, which consists of peridotite, gabbro, and basalt units, was intruded by magmatic activities during the Late Cretaceous and Early Eocene. The most recent magmatic activity in the region includes alkaline basaltic volcanism, which occurred during the Miocene to Quaternary period. Based on studies, the lithological units in the Seydal region are classified into four main groups: Regional metamorphic rocks of Cretaceous age, including slate, phyllite, schist, and amphibolite, primarily exposed in the southern part of the area. Ophiolitic mélange complex of Cretaceous age, consisting of peridotite, gabbro, and basalt units. Clastic and carbonate sedimentary units of Cretaceous (deep marine) and Eocene (shallow marine) age, including shale, sandstone, and carbonate rocks, which are widespread in the northern region and overlie the ophiolitic complex. Seydal granitoid intrusion, which is approximately 19 km in length, with a NW-SE orientation, and intrudes into the ophiolitic and metamorphic units of Cretaceous age. The Seydal granitoid is further subdivided into three main sections: Granodiorite section, which forms the largest unit of the intrusion and accounts for approximately 99% of its volume. Monzogranite section, occurring as small bodies along the boundary between the granodiorite and ultramafic units. Syenogranite section, appearing as a small intrusion in the northern part of the area near the boundary with ultramafic and granodiorite units. This study began with a review of previous research and the analysis of satellite imagery, including ASTER, Sentinel-2, and Landsat-8. During fieldwork, 230 rock samples were collected from the area, and 123 samples were selected for the preparation of thin sections for petrographic analysis in the laboratory. Based on petrographic observations, 10 fresh samples (free of alteration and weathering) were selected for geochemical analysis. These samples were crushed and powdered before being sent to SGS Canada for chemical analysis using ICP (for major elements) and ICP-MS (for trace elements). The data were analyzed using GCDkit v5 software, and the regional geological map was prepared using ArcGIS v10.5. To determine the precise age of the granodiorite unit, a sample was sent to the Institute of Geology and Geophysics in Beijing for zircon separation. Zircons were separated using heavy liquid and magnetic methods and then transferred to the National Taiwan University. A total of 57 large, euhedral zircon grains were embedded in epoxy and polished to a thickness of 20 µm. Cathodoluminescence (CL) images were obtained to identify zones suitable for laser ablation. Uranium-lead (U-Pb) zircon dating was performed using the LA-ICP-MS technique with an Agilent 7500 LA instrument. The results were analyzed to construct concordia diagrams for geochronological interpretation. <strong> </strong> <strong>Results and Discussion</strong> In the petrographic study of the granitoid bodies in the Seydal region, the rocks are classified into granodiorite and granite (monzogranite and syenogranite) based on modal analysis. The predominant texture in these rocks is granular, with additional textures such as myrmekitic, graphic, and perthitic also observed. These textures may indicate simultaneous growth from a melt, interaction between solid and melt phases, or immiscibility between two solids. <strong>Types of Granitoid Bodies and Petrographic Characteristics</strong> <strong> Granodiorite</strong> Granodiorite represents the largest granitoid unit in the region and is characterized by its granular texture, medium grain size, and leucocratic nature. Poikilitic and myrmekitic textures are also observed in this unit. The primary mineralogical composition includes quartz, plagioclase, and potassium feldspar: <strong>Plagioclase:</strong> Occurs as euhedral to subhedral crystals with albite twinning, predominantly of oligoclase and andesine types, with partial sericitization in some cases. <strong>Potassium Feldspar:</strong> Includes orthoclase and microcline, displaying Carlsbad twinning, and is slightly sericitized. <strong>Quartz:</strong> Appears as anhedral to subhedral crystals with sizes ranging from 1 to 3 mm. <strong>Ferromagnesian Minerals:</strong> Comprise about 10% of the rock volume and include green biotite and hornblende. Accessory minerals include sphene, zircon, and apatite. Sphene is observed as euhedral brown crystals, while zircon and apatite are seen as inclusions in other minerals. Secondary minerals include chlorite, epidote, and clay minerals. <strong> Monzogranite</strong> The predominant texture of monzogranite is granular, with additional textures such as granophyric and myrmekitic appearing in some samples. The primary mineral composition includes: <strong>Plagioclase:</strong> Comprises 30-35% of the rock by volume. <strong>Potassium Feldspar:</strong> Predominantly orthoclase, occasionally microcline, with 30-35% volumetric abundance, typically anhedral. <strong>Quartz:</strong> Accounts for 20-25% of the rock by volume. <strong>Ferromagnesian Minerals:</strong> Includes biotite (1-3%) and muscovite (~8%). In some samples, potassium feldspars have been altered to clay minerals, plagioclase to sericite, and ferromagnesian minerals to chlorite and epidote. Accessory minerals include apatite and zircon. <strong> Syenogranite</strong> Syenogranite exhibits a predominantly granular and coarse-grained texture, with granophyric and graphic textures also present. The main mineralogical composition includes: Potassium Feldspar: Comprising 50-60% of the rock, primarily orthoclase with occasional microcline. Quartz: Anhedral crystals with undulatory extinction, making up 20-30% of the rock. Plagioclase: Represents 10-15% of the rock, predominantly oligoclase. Ferromagnesian minerals include muscovite (3-8%) and biotite (<1%). Garnet crystals, a distinguishing feature of S-type granitoids, are also observed in this unit. <strong>Mafic Enclaves in the Granitoid Bodies</strong> <strong> Mafic Microgranular Enclaves</strong> These enclaves are the most abundant type within the granodioritic bodies and mainly consist of plagioclase, hornblende, and quartz. Their sizes range from a few centimeters to 1 meter and are generally finer-grained compared to the host granitoid. Their mineralogical composition is similar to that of the granodiorite, but due to lower degrees of fractionation, they contain higher amounts of mafic minerals and less quartz. These enclaves likely originated from early-crystallized portions of the granitoid magma, which were subsequently transported to higher levels during the magma intrusion process. <strong> Mafic Xenoliths</strong> Mafic xenoliths represent fragments detached from gabbroic bodies during the ascent of the granitoid magma. These xenoliths range in size from 2 to 10 cm and exhibit distinct boundaries with the host rock. Their main constituents are pyroxene and plagioclase, which have undergone extensive alteration and saussuritization, resulting in a fine-grained texture. <strong>Geochemical Characteristics of the Granitoid Bodies</strong> Geochemical analyses using ICP and ICP-MS methods reveal that the granitoid samples from the Seydal region fall within the compositional range of granodiorite, monzogranite, and syenogranite. Granodiorites belong to the calc-alkaline series, while monzogranites and syenogranites are part of the potassium-rich calc-alkaline series. <strong>Major and Trace Element Composition</strong> The granodiorites exhibit metaluminous characteristics, whereas monzogranites and syenogranites are peraluminous. Analysis of rare earth elements (REE) using chondrite-normalized and mantle-normalized spider diagrams indicates enrichment in light REEs (LREEs) and relatively lower enrichment in heavy REEs (HREEs) for granodiorites. Positive anomalies in Rb, Th, and Ce, coupled with negative anomalies in Nb, Ti, and Ba, suggest processes of crystal fractionation and partial melting in subduction-related tectonic settings. <strong>Geochemical Diagrams Analysis</strong> Granodiorites of the region display characteristics consistent with arc-related granitoids associated with subduction zones. Negative Nb anomalies are attributed to crystal fractionation of amphibole, titanite, and rutile. Conversely, monzogranites and syenogranites show negative anomalies for Ba, Sr, and Ti and positive anomalies for Rb, Th, and La, confirming their crustal origin. <strong>Magmatic Origin and Tectonic Setting</strong> Geochemical data suggest that granodiorites were derived from partial melting of amphibolitic rocks, whereas monzogranites and syenogranites resulted from partial melting of pelitic sediments. This difference in magmatic origin explains the variations in their geochemical compositions. Granodiorites exhibit characteristics of I-type granitoids, while monzogranites and syenogranites align with the S-type granitoid classification. <strong>Geochronology</strong> To determine the crystallization age of the granodiorite body, a sample was subjected to zircon U–Pb dating. Zircons were separated using heavy liquids, and cathodoluminescence (CL) imaging revealed magmatic zoning within the zircon grains. The dating results indicate a crystallization age of 54.3 ± 0.7 Ma (early Eocene), which is younger than the previously assumed Cretaceous age. A U/Th ratio of less than 1 in the zircons confirms their magmatic origin. <strong> </strong> <strong>Conclusion</strong> The Seydal granitoid bodies, comprising granodiorite, monzogranite, and syenogranite, exhibit geochemical and tectonic characteristics associated with post-collisional active continental margins. These bodies formed after the closure of an oceanic seaway and the emplacement of oceanic lithosphere onto the continental margin, through processes of partial melting and crustal assimilation. Granodiorites are derived from mafic sources (metabasalt), while monzogranites and syenogranites originated from pelitic sediments. Geochemical, petrographic, and geochronological evidence collectively highlights the Seydal granitoid body as an example of a post-collisional granitoid system, providing significant insights into the tectonomagmatic evolution of the region. <strong>Introduction</strong> The Seydal granitoid bodies is located in eastern Iran on the western boundary of the Sistan suture zone. This zone represents remnants of the lithosphere of an oceanic basin that formed through processes of continental collision and ocean closure. Numerous granitoid intrusions with varying ages, ranging from the Cretaceous to the Eocene, have been identified in this zone. The Seydal granitoid, situated near the village of Seydal, approximately 150 km southeast of Birjand, was previously described in studies as a plagiogranite, gneiss, and leucogranite with an Upper Cretaceous age. Based on new evidence suggesting a younger age for this intrusion, this study aims to investigate the petrography, geochemical analysis, and geochronology of the Seydal granitoid. <strong> </strong> <strong>Materials and Methods</strong> The Seydal region lies in the northwestern part of the Sistan suture zone. This zone is characterized as an accretionary complex formed by the subduction of the Sistan oceanic lithosphere. The ophiolitic complex of the area, which consists of peridotite, gabbro, and basalt units, was intruded by magmatic activities during the Late Cretaceous and Early Eocene. The most recent magmatic activity in the region includes alkaline basaltic volcanism, which occurred during the Miocene to Quaternary period. Based on studies, the lithological units in the Seydal region are classified into four main groups: Regional metamorphic rocks of Cretaceous age, including slate, phyllite, schist, and amphibolite, primarily exposed in the southern part of the area. Ophiolitic mélange complex of Cretaceous age, consisting of peridotite, gabbro, and basalt units. Clastic and carbonate sedimentary units of Cretaceous (deep marine) and Eocene (shallow marine) age, including shale, sandstone, and carbonate rocks, which are widespread in the northern region and overlie the ophiolitic complex. Seydal granitoid intrusion, which is approximately 19 km in length, with a NW-SE orientation, and intrudes into the ophiolitic and metamorphic units of Cretaceous age. The Seydal granitoid is further subdivided into three main sections: Granodiorite section, which forms the largest unit of the intrusion and accounts for approximately 99% of its volume. Monzogranite section, occurring as small bodies along the boundary between the granodiorite and ultramafic units. Syenogranite section, appearing as a small intrusion in the northern part of the area near the boundary with ultramafic and granodiorite units. This study began with a review of previous research and the analysis of satellite imagery, including ASTER, Sentinel-2, and Landsat-8. During fieldwork, 230 rock samples were collected from the area, and 123 samples were selected for the preparation of thin sections for petrographic analysis in the laboratory. Based on petrographic observations, 10 fresh samples (free of alteration and weathering) were selected for geochemical analysis. These samples were crushed and powdered before being sent to SGS Canada for chemical analysis using ICP (for major elements) and ICP-MS (for trace elements). The data were analyzed using GCDkit v5 software, and the regional geological map was prepared using ArcGIS v10.5. To determine the precise age of the granodiorite unit, a sample was sent to the Institute of Geology and Geophysics in Beijing for zircon separation. Zircons were separated using heavy liquid and magnetic methods and then transferred to the National Taiwan University. A total of 57 large, euhedral zircon grains were embedded in epoxy and polished to a thickness of 20 µm. Cathodoluminescence (CL) images were obtained to identify zones suitable for laser ablation. Uranium-lead (U-Pb) zircon dating was performed using the LA-ICP-MS technique with an Agilent 7500 LA instrument. The results were analyzed to construct concordia diagrams for geochronological interpretation. <strong> </strong> <strong>Results and Discussion</strong> In the petrographic study of the granitoid bodies in the Seydal region, the rocks are classified into granodiorite and granite (monzogranite and syenogranite) based on modal analysis. The predominant texture in these rocks is granular, with additional textures such as myrmekitic, graphic, and perthitic also observed. These textures may indicate simultaneous growth from a melt, interaction between solid and melt phases, or immiscibility between two solids. <strong>Types of Granitoid Bodies and Petrographic Characteristics</strong> <strong> Granodiorite</strong> Granodiorite represents the largest granitoid unit in the region and is characterized by its granular texture, medium grain size, and leucocratic nature. Poikilitic and myrmekitic textures are also observed in this unit. The primary mineralogical composition includes quartz, plagioclase, and potassium feldspar: <strong>Plagioclase:</strong> Occurs as euhedral to subhedral crystals with albite twinning, predominantly of oligoclase and andesine types, with partial sericitization in some cases. <strong>Potassium Feldspar:</strong> Includes orthoclase and microcline, displaying Carlsbad twinning, and is slightly sericitized. <strong>Quartz:</strong> Appears as anhedral to subhedral crystals with sizes ranging from 1 to 3 mm. <strong>Ferromagnesian Minerals:</strong> Comprise about 10% of the rock volume and include green biotite and hornblende. Accessory minerals include sphene, zircon, and apatite. Sphene is observed as euhedral brown crystals, while zircon and apatite are seen as inclusions in other minerals. Secondary minerals include chlorite, epidote, and clay minerals. <strong> Monzogranite</strong> The predominant texture of monzogranite is granular, with additional textures such as granophyric and myrmekitic appearing in some samples. The primary mineral composition includes: <strong>Plagioclase:</strong> Comprises 30-35% of the rock by volume. <strong>Potassium Feldspar:</strong> Predominantly orthoclase, occasionally microcline, with 30-35% volumetric abundance, typically anhedral. <strong>Quartz:</strong> Accounts for 20-25% of the rock by volume. <strong>Ferromagnesian Minerals:</strong> Includes biotite (1-3%) and muscovite (~8%). In some samples, potassium feldspars have been altered to clay minerals, plagioclase to sericite, and ferromagnesian minerals to chlorite and epidote. Accessory minerals include apatite and zircon. <strong> Syenogranite</strong> Syenogranite exhibits a predominantly granular and coarse-grained texture, with granophyric and graphic textures also present. The main mineralogical composition includes: Potassium Feldspar: Comprising 50-60% of the rock, primarily orthoclase with occasional microcline. Quartz: Anhedral crystals with undulatory extinction, making up 20-30% of the rock. Plagioclase: Represents 10-15% of the rock, predominantly oligoclase. Ferromagnesian minerals include muscovite (3-8%) and biotite (<1%). Garnet crystals, a distinguishing feature of S-type granitoids, are also observed in this unit. <strong>Mafic Enclaves in the Granitoid Bodies</strong> <strong> Mafic Microgranular Enclaves</strong> These enclaves are the most abundant type within the granodioritic bodies and mainly consist of plagioclase, hornblende, and quartz. Their sizes range from a few centimeters to 1 meter and are generally finer-grained compared to the host granitoid. Their mineralogical composition is similar to that of the granodiorite, but due to lower degrees of fractionation, they contain higher amounts of mafic minerals and less quartz. These enclaves likely originated from early-crystallized portions of the granitoid magma, which were subsequently transported to higher levels during the magma intrusion process. <strong> Mafic Xenoliths</strong> Mafic xenoliths represent fragments detached from gabbroic bodies during the ascent of the granitoid magma. These xenoliths range in size from 2 to 10 cm and exhibit distinct boundaries with the host rock. Their main constituents are pyroxene and plagioclase, which have undergone extensive alteration and saussuritization, resulting in a fine-grained texture. <strong>Geochemical Characteristics of the Granitoid Bodies</strong> Geochemical analyses using ICP and ICP-MS methods reveal that the granitoid samples from the Seydal region fall within the compositional range of granodiorite, monzogranite, and syenogranite. Granodiorites belong to the calc-alkaline series, while monzogranites and syenogranites are part of the potassium-rich calc-alkaline series. <strong>Major and Trace Element Composition</strong> The granodiorites exhibit metaluminous characteristics, whereas monzogranites and syenogranites are peraluminous. Analysis of rare earth elements (REE) using chondrite-normalized and mantle-normalized spider diagrams indicates enrichment in light REEs (LREEs) and relatively lower enrichment in heavy REEs (HREEs) for granodiorites. Positive anomalies in Rb, Th, and Ce, coupled with negative anomalies in Nb, Ti, and Ba, suggest processes of crystal fractionation and partial melting in subduction-related tectonic settings. <strong>Geochemical Diagrams Analysis</strong> Granodiorites of the region display characteristics consistent with arc-related granitoids associated with subduction zones. Negative Nb anomalies are attributed to crystal fractionation of amphibole, titanite, and rutile. Conversely, monzogranites and syenogranites show negative anomalies for Ba, Sr, and Ti and positive anomalies for Rb, Th, and La, confirming their crustal origin. <strong>Magmatic Origin and Tectonic Setting</strong> Geochemical data suggest that granodiorites were derived from partial melting of amphibolitic rocks, whereas monzogranites and syenogranites resulted from partial melting of pelitic sediments. This difference in magmatic origin explains the variations in their geochemical compositions. Granodiorites exhibit characteristics of I-type granitoids, while monzogranites and syenogranites align with the S-type granitoid classification. <strong>Geochronology</strong> To determine the crystallization age of the granodiorite body, a sample was subjected to zircon U–Pb dating. Zircons were separated using heavy liquids, and cathodoluminescence (CL) imaging revealed magmatic zoning within the zircon grains. The dating results indicate a crystallization age of 54.3 ± 0.7 Ma (early Eocene), which is younger than the previously assumed Cretaceous age. A U/Th ratio of less than 1 in the zircons confirms their magmatic origin. <strong> </strong> <strong>Conclusion</strong> The Seydal granitoid bodies, comprising granodiorite, monzogranite, and syenogranite, exhibit geochemical and tectonic characteristics associated with post-collisional active continental margins. These bodies formed after the closure of an oceanic seaway and the emplacement of oceanic lithosphere onto the continental margin, through processes of partial melting and crustal assimilation. Granodiorites are derived from mafic sources (metabasalt), while monzogranites and syenogranites originated from pelitic sediments. Geochemical, petrographic, and geochronological evidence collectively highlights the Seydal granitoid body as an example of a post-collisional granitoid system, providing significant insights into the tectonomagmatic evolution of the region. https://esrj.sbu.ac.ir/article_100784_3121d14bf9162733f1413a469138b40e.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2026 02 20 Geology, Mineralography and geochemistry in 16 B Fe mineralization Bafq (Yazd) Geology, Mineralography and geochemistry in 16 B Fe mineralization Bafq (Yazd) 24 49 100929 10.48308/esrj.2021.100929 FA Pouria Salami Economic geology at the Research Institute for Earth Sciences (Geological Survey of Iran), Tehran, Iran Afshin Akbarpour Economic geology at the Research Institute for Earth Sciences (Geological Survey of Iran), Tehran, Iran Mohammad Lotfi Economic geology at the Research Institute for Earth Sciences (Geological Survey of Iran), Tehran, Iran Arash Gourabjiri Economic geology at Mianeh Azad University, Mianeh, Iran Journal Article 2023 05 17 <strong>Introduction</strong> The XVI-B anomaly iron ore deposit is situated within the Central Iranian structural zone, specifically in the Bafq region. This region is notable for its lack of a direct association between specific tectonic periods and iron ore deposits. The Bafq area contains 39 iron ore deposits with an estimated total reserve of 2 billion tons, making it one of the most significant iron ore extraction regions in Iran. <strong> </strong> <strong>Materials and Methods</strong> Prominent iron ore deposits in the area include Sechahoon (117 Mt), Chadormalu (400 Mt), and Choghart (216 Mt). The Bafq anomaly iron ore deposit is located within the Central Iranian structural zone. This study involved the collection of drilling core samples for ICP-MS analysis (conducted at Karaj Laboratory), thin-polish and thin-section preparations (54 samples), and XRF analysis (5 samples, also conducted at Karaj Laboratory). According to the structural-sedimentary unit classification of Iran, the study area lies within the central Iranian zone (Nabawi, 1976). This zone contains some of the oldest metamorphic rocks in Iran, dating back to the Precambrian (Aghanbati, 2004). The Bafq mineral district, a subset of the central-eastern Iranian microplate, has experienced tectonic evolution influenced by the Katanga orogeny and related movements over the past 600 million years (Taghavi, 2015). The region's Neoproterozoic–Early Cambrian mass magnetite deposits are predominantly found in volcanic rocks associated with mantle diapirism along caldera margins. Notable deposits include Chaghez, Chadormalu, Choghart, and Sechahoon (Torab et al, 2007). The dispersed mineralization of iron and rare earth elements in the Bafq mining area is directly linked to intracontinental rifting. Volcanism, magmatism, and regional tectonics, influenced by rift dynamics, played a significant role in the mineralization of igneous rocks (Samani, 1993). The XVI-B deposit is part of the Bafq mineral district, located within the central-eastern Iranian microplate. Stratigraphically, the area is divided into western, central, and eastern sections (Ramezani and Tucker, 2003). The oldest rocks (Neoproterozoic–Early Cambrian) are situated in the eastern part, while the youngest rocks (Eocene) are found in the west. Major faults, including the Nybaz-Chatak and Poshte-Badam faults, delineate these sections. <strong>Results and Discussion</strong> The region contains diverse igneous and metamorphic rocks. The igneous rocks include gabbro, diorite, syenite, quartz monzonite, granite, and highly altered basic rocks (metabasites). The metamorphic rocks are dominated by marble and skarn formations. The leucogranite, characterized by idiomorphic and graphic textures, contains plagioclase and sodic feldspar crystals alongside quartz, alkali feldspar, biotite, and secondary minerals such as sericite, clay minerals, epidote, and carbonates. Tectonic activity has resulted in cataclastic textures in some samples. Mineralization in the area is primarily associated with syenite, gabbro, and skarn rocks. The metallic minerals include magnetite, hematite, pyrite, and chalcopyrite, while non-metallic minerals such as quartz, actinolite, calcite, and epidote are also present. <strong>Mineralization and Geochemical Characteristics</strong> Iron mineralization predominantly occurs as magnetite, which is observed in massive, void-filling, and disseminated forms. Near the surface, magnetite undergoes oxidation, resulting in its transformation into hematite, goethite, and other iron oxides. Associated metallic minerals include pyrite and chalcopyrite, often found with quartz, actinolite, calcite, and epidote in host rocks, intrusive syenites, gabbros, and skarns.The total iron oxide content in the collected samples ranges from 25% to 75%, while silica content varies between 5% and 45%. Titanium concentrations are relatively low, between 0.1% and 0.5%, and exhibit a negative correlation with iron content. Potassium oxide levels range from 0.1% to 1.8%, and phosphorus content varies between 0.02% and 0.35%, indicating an absence of phosphate mineralization. Magnesium oxide levels range from 1% to 12%, largely attributable to the presence of ferromagnesian minerals such as amphibole and dolomite. Negative correlations between magnesium oxide and iron suggest minimal substitution of magnesium for iron in the mineral lattice. Aluminum and calcium oxides range from 2% to 12% and 2% to 26%, respectively. The ore samples contain cobalt (3–75 ppm), nickel (1–17 ppm), chromium (10–96 ppm), and vanadium (40–120 ppm). The behavior of rare earth elements (REEs) indicates hydrothermal alteration, with total REE content varying between 13.1 and 375.1 ppm. Enrichment in light REEs (LREEs) and depletion in heavy REEs (HREEs), alongside a positive Eu anomaly, are consistent with skarn-type deposits (Bea et al, 1996). This pattern suggests that REEs may substitute for elements in garnet, zircon, and magnetite structures. <strong> </strong> <strong>Conclusion</strong> The geological evidence indicates that the oldest rocks in the area are Precambrian metamorphic units, including gneiss, mica-schist, amphibolite, and migmatite, which form the bedrock of the mineralization anomaly. The deposit is covered by Tertiary and Quaternary sediments of the Bafq Basin. The alkaline diorite-syenite intrusive units play a significant role in hosting mineralization.The mineralization comprises a variety of igneous and metamorphic rocks, including gabbro, syenite, quartz monzonite, granite, marble, and skarn. Magnetite is the dominant iron oxide ore and is accompanied by pyrite and chalcopyrite. Oxidation near the surface leads to the formation of secondary iron oxides like hematite and goethite. The geochemical data suggest that the deposit is of hydrothermal origin, with characteristics aligning it with skarn-type deposits. Correlations among trace elements such as Co/Ni, Cr/Ni, and Cr/V, along with ratios like Al/Co and Sn/Ga, further support this classification. The REE patterns, including a positive Eu anomaly and LREE enrichment, are consistent with skarn-type mineralization and provide insights into the role of hydrothermal fluids in the formation process. This study highlights the skarn origin of the XVI-B anomaly iron ore deposit, emphasizing the significance of intrusive magmatic activity and hydrothermal processes in its formation. These findings contribute valuable insights for the geological modeling and economic evaluation of the deposit. <strong>Introduction</strong> The XVI-B anomaly iron ore deposit is situated within the Central Iranian structural zone, specifically in the Bafq region. This region is notable for its lack of a direct association between specific tectonic periods and iron ore deposits. The Bafq area contains 39 iron ore deposits with an estimated total reserve of 2 billion tons, making it one of the most significant iron ore extraction regions in Iran. <strong> </strong> <strong>Materials and Methods</strong> Prominent iron ore deposits in the area include Sechahoon (117 Mt), Chadormalu (400 Mt), and Choghart (216 Mt). The Bafq anomaly iron ore deposit is located within the Central Iranian structural zone. This study involved the collection of drilling core samples for ICP-MS analysis (conducted at Karaj Laboratory), thin-polish and thin-section preparations (54 samples), and XRF analysis (5 samples, also conducted at Karaj Laboratory). According to the structural-sedimentary unit classification of Iran, the study area lies within the central Iranian zone (Nabawi, 1976). This zone contains some of the oldest metamorphic rocks in Iran, dating back to the Precambrian (Aghanbati, 2004). The Bafq mineral district, a subset of the central-eastern Iranian microplate, has experienced tectonic evolution influenced by the Katanga orogeny and related movements over the past 600 million years (Taghavi, 2015). The region's Neoproterozoic–Early Cambrian mass magnetite deposits are predominantly found in volcanic rocks associated with mantle diapirism along caldera margins. Notable deposits include Chaghez, Chadormalu, Choghart, and Sechahoon (Torab et al, 2007). The dispersed mineralization of iron and rare earth elements in the Bafq mining area is directly linked to intracontinental rifting. Volcanism, magmatism, and regional tectonics, influenced by rift dynamics, played a significant role in the mineralization of igneous rocks (Samani, 1993). The XVI-B deposit is part of the Bafq mineral district, located within the central-eastern Iranian microplate. Stratigraphically, the area is divided into western, central, and eastern sections (Ramezani and Tucker, 2003). The oldest rocks (Neoproterozoic–Early Cambrian) are situated in the eastern part, while the youngest rocks (Eocene) are found in the west. Major faults, including the Nybaz-Chatak and Poshte-Badam faults, delineate these sections. <strong>Results and Discussion</strong> The region contains diverse igneous and metamorphic rocks. The igneous rocks include gabbro, diorite, syenite, quartz monzonite, granite, and highly altered basic rocks (metabasites). The metamorphic rocks are dominated by marble and skarn formations. The leucogranite, characterized by idiomorphic and graphic textures, contains plagioclase and sodic feldspar crystals alongside quartz, alkali feldspar, biotite, and secondary minerals such as sericite, clay minerals, epidote, and carbonates. Tectonic activity has resulted in cataclastic textures in some samples. Mineralization in the area is primarily associated with syenite, gabbro, and skarn rocks. The metallic minerals include magnetite, hematite, pyrite, and chalcopyrite, while non-metallic minerals such as quartz, actinolite, calcite, and epidote are also present. <strong>Mineralization and Geochemical Characteristics</strong> Iron mineralization predominantly occurs as magnetite, which is observed in massive, void-filling, and disseminated forms. Near the surface, magnetite undergoes oxidation, resulting in its transformation into hematite, goethite, and other iron oxides. Associated metallic minerals include pyrite and chalcopyrite, often found with quartz, actinolite, calcite, and epidote in host rocks, intrusive syenites, gabbros, and skarns.The total iron oxide content in the collected samples ranges from 25% to 75%, while silica content varies between 5% and 45%. Titanium concentrations are relatively low, between 0.1% and 0.5%, and exhibit a negative correlation with iron content. Potassium oxide levels range from 0.1% to 1.8%, and phosphorus content varies between 0.02% and 0.35%, indicating an absence of phosphate mineralization. Magnesium oxide levels range from 1% to 12%, largely attributable to the presence of ferromagnesian minerals such as amphibole and dolomite. Negative correlations between magnesium oxide and iron suggest minimal substitution of magnesium for iron in the mineral lattice. Aluminum and calcium oxides range from 2% to 12% and 2% to 26%, respectively. The ore samples contain cobalt (3–75 ppm), nickel (1–17 ppm), chromium (10–96 ppm), and vanadium (40–120 ppm). The behavior of rare earth elements (REEs) indicates hydrothermal alteration, with total REE content varying between 13.1 and 375.1 ppm. Enrichment in light REEs (LREEs) and depletion in heavy REEs (HREEs), alongside a positive Eu anomaly, are consistent with skarn-type deposits (Bea et al, 1996). This pattern suggests that REEs may substitute for elements in garnet, zircon, and magnetite structures. <strong> </strong> <strong>Conclusion</strong> The geological evidence indicates that the oldest rocks in the area are Precambrian metamorphic units, including gneiss, mica-schist, amphibolite, and migmatite, which form the bedrock of the mineralization anomaly. The deposit is covered by Tertiary and Quaternary sediments of the Bafq Basin. The alkaline diorite-syenite intrusive units play a significant role in hosting mineralization.The mineralization comprises a variety of igneous and metamorphic rocks, including gabbro, syenite, quartz monzonite, granite, marble, and skarn. Magnetite is the dominant iron oxide ore and is accompanied by pyrite and chalcopyrite. Oxidation near the surface leads to the formation of secondary iron oxides like hematite and goethite. The geochemical data suggest that the deposit is of hydrothermal origin, with characteristics aligning it with skarn-type deposits. Correlations among trace elements such as Co/Ni, Cr/Ni, and Cr/V, along with ratios like Al/Co and Sn/Ga, further support this classification. The REE patterns, including a positive Eu anomaly and LREE enrichment, are consistent with skarn-type mineralization and provide insights into the role of hydrothermal fluids in the formation process. This study highlights the skarn origin of the XVI-B anomaly iron ore deposit, emphasizing the significance of intrusive magmatic activity and hydrothermal processes in its formation. These findings contribute valuable insights for the geological modeling and economic evaluation of the deposit. https://esrj.sbu.ac.ir/article_100929_e19801f9caf39bff8afc5e4548f2942d.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2026 02 20 Estimation of solar ultraviolet (UV-B) radiation using Aura satellite Ozone monitoring instrument (OMI) in Iran Estimation of solar ultraviolet (UV-B) radiation using Aura satellite Ozone monitoring instrument (OMI) in Iran 50 67 105351 10.48308/esrj.2025.105351 FA Koohzad Raispour UDepartment of Geography, Faculty of Humanities, University of Zanjan, Zanjan, Iran Journal Article 2023 07 19 <strong>Introduction</strong> Part of the sun’s rays is made up of ultraviolet rays, which have short wavelengths and a lot of energy. Ultraviolet light in three ranges of long wavelength ultraviolet (UV-A) with a wavelength range of 0.390 - 0.315 μm, medium wavelength ultraviolet (UV-B) with a long range the wavelength is divided by 0.155 μm - 0.280 μm and short-wavelength ultraviolet (UV-C) with a wavelength less than 0.280 μm. UV-B light is the most harmful radiation on the skin and causes various side effects including sunburn, skin allergies and skin cancer. This radiation affects the DNA strand by altering the genetic material and increases the potential for intracellular carcinogenesis. Ultraviolet (UVI) index is a small (numerical) value that indicates the intensity of ultraviolet (UV) rays in the desired location and area. This index is a parameter for raising public awareness about the effects of UV radiation on health and how much skin protection is needed for different amounts. Based on the index provided by the World Health Organization, the concentration level or UV index is shown on a scale of 2 to +1. The higher the value of this index, the more destructive power it has on the skin and eyes. <strong> </strong> <strong>Materials and Methods</strong> In this study, the Level 3 product (OMUVBd-L3) of the solar ultraviolet (UV-B) index of the OMI sensor with a spatial resolution of 0.25 × 0.25 degree for the time series 2005 to 2020 was used. The required data was downloaded from the website http://aura.gsfc.nasa.gov in a daily time step and after the necessary processing; it was converted into monthly and seasonal values. The data used were converted into network data and information tables by applying the necessary algorithms, and the necessary outputs were extracted as a raster based on the geographical border of Iran. Finally, in order to better understand the temporal-spatial behavior of the UV index reaching the surface in Iran, the results were presented in the form of maps, graphs and graphs and the temporal-spatial estimation of solar UV radiation in Iran. <strong> </strong> <strong>Results and Discussion</strong> Spatially, there is a significant difference in the distribution of UV-B input radiation in Iran. According to the global index of solar ultraviolet radiation, more than 90% of Iran's area is exposed to high to very high radiation risk. The highest average of UV-B index is related to the summer season (11.29) and the lowest average is related to the winter season (3.53). In terms of spatial distribution, there are significant differences between the seasons. The spatial distribution of the monthly UV index provides more information about the details of changes in solar UV radiation reaching the earth's surface throughout the year; So that it is possible to determine the minimum and maximum, months as well as the months with balanced UV radiation conditions. A comparison of the amount of UV-B radiation in different months clearly shows January as the least dangerous month and June as the most dangerous month of the year. Since the value of the solar UV index is a function of the total amount of incoming solar radiation. Therefore, factors such as the angle of radiation, the duration of radiation and the amount of UV control the UV-B solar ultraviolet index.   <strong>Conclusion</strong> The results of the analysis and comparison of seasonal and monthly maps of solar UV index in Iran, indicate that in all months and seasons of the year from north to south, the intensity of solar UV radiation increases. In the northern part of Iran, due to higher latitude and less solar radiation reaching the earth's surface, the UV-B index is lower than other parts of Iran. On the other hand, in the more southern offerings, because both the angle of radiation is vertical and the sky is clearer, it provides the conditions for receiving the maximum amount of solar energy and consequently UV-B solar ultraviolet radiation. The prevalence of such conditions is established in all months and seasons of the year, so that the radiant regions (ultraviolet UV-B) are fully compliant with the mentioned conditions. Therefore, it can be said that in the warm period of the year (spring and summer), the highlands as well as the lower offerings have more solar UV radiation and therefore the risk of eye and skin vulnerability increases. Therefore, it is recommended to take protective measures against UV radiation if it is necessary to be present in areas exposed to direct sunlight. <strong>Introduction</strong> Part of the sun’s rays is made up of ultraviolet rays, which have short wavelengths and a lot of energy. Ultraviolet light in three ranges of long wavelength ultraviolet (UV-A) with a wavelength range of 0.390 - 0.315 μm, medium wavelength ultraviolet (UV-B) with a long range the wavelength is divided by 0.155 μm - 0.280 μm and short-wavelength ultraviolet (UV-C) with a wavelength less than 0.280 μm. UV-B light is the most harmful radiation on the skin and causes various side effects including sunburn, skin allergies and skin cancer. This radiation affects the DNA strand by altering the genetic material and increases the potential for intracellular carcinogenesis. Ultraviolet (UVI) index is a small (numerical) value that indicates the intensity of ultraviolet (UV) rays in the desired location and area. This index is a parameter for raising public awareness about the effects of UV radiation on health and how much skin protection is needed for different amounts. Based on the index provided by the World Health Organization, the concentration level or UV index is shown on a scale of 2 to +1. The higher the value of this index, the more destructive power it has on the skin and eyes. <strong> </strong> <strong>Materials and Methods</strong> In this study, the Level 3 product (OMUVBd-L3) of the solar ultraviolet (UV-B) index of the OMI sensor with a spatial resolution of 0.25 × 0.25 degree for the time series 2005 to 2020 was used. The required data was downloaded from the website http://aura.gsfc.nasa.gov in a daily time step and after the necessary processing; it was converted into monthly and seasonal values. The data used were converted into network data and information tables by applying the necessary algorithms, and the necessary outputs were extracted as a raster based on the geographical border of Iran. Finally, in order to better understand the temporal-spatial behavior of the UV index reaching the surface in Iran, the results were presented in the form of maps, graphs and graphs and the temporal-spatial estimation of solar UV radiation in Iran. <strong> </strong> <strong>Results and Discussion</strong> Spatially, there is a significant difference in the distribution of UV-B input radiation in Iran. According to the global index of solar ultraviolet radiation, more than 90% of Iran's area is exposed to high to very high radiation risk. The highest average of UV-B index is related to the summer season (11.29) and the lowest average is related to the winter season (3.53). In terms of spatial distribution, there are significant differences between the seasons. The spatial distribution of the monthly UV index provides more information about the details of changes in solar UV radiation reaching the earth's surface throughout the year; So that it is possible to determine the minimum and maximum, months as well as the months with balanced UV radiation conditions. A comparison of the amount of UV-B radiation in different months clearly shows January as the least dangerous month and June as the most dangerous month of the year. Since the value of the solar UV index is a function of the total amount of incoming solar radiation. Therefore, factors such as the angle of radiation, the duration of radiation and the amount of UV control the UV-B solar ultraviolet index.   <strong>Conclusion</strong> The results of the analysis and comparison of seasonal and monthly maps of solar UV index in Iran, indicate that in all months and seasons of the year from north to south, the intensity of solar UV radiation increases. In the northern part of Iran, due to higher latitude and less solar radiation reaching the earth's surface, the UV-B index is lower than other parts of Iran. On the other hand, in the more southern offerings, because both the angle of radiation is vertical and the sky is clearer, it provides the conditions for receiving the maximum amount of solar energy and consequently UV-B solar ultraviolet radiation. The prevalence of such conditions is established in all months and seasons of the year, so that the radiant regions (ultraviolet UV-B) are fully compliant with the mentioned conditions. Therefore, it can be said that in the warm period of the year (spring and summer), the highlands as well as the lower offerings have more solar UV radiation and therefore the risk of eye and skin vulnerability increases. Therefore, it is recommended to take protective measures against UV radiation if it is necessary to be present in areas exposed to direct sunlight. https://esrj.sbu.ac.ir/article_105351_875efc068b1a0b7a740d140f0d67bdbc.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2026 02 20 Occurrence and genesis of zeolites located in Hir in Ardabil province based on the stable isotopes findings Occurrence and genesis of zeolites located in Hir in Ardabil province based on the stable isotopes findings 68 81 104876 10.48308/esrj.2024.104876 FA Ali Lotfi Bakhsh Department of geology, faculty of sciences, University of Mohaghegh Ardabali, Ardabil, Iran 0000-0003-2780-5303 Journal Article 2024 04 27 <strong>Introduction</strong> Zeolites, which form the largest group of silicate minerals, belong to the tectosilicate family, in which the [SiO4]^4- and [AlO4]^5- tetrahedrals are connected to each other in the form of a three-dimensional network so that an empty space is created between them. Channels are created in the structure of zeolites by placing the empty spaces one after the other. Zeolites have unique physical and chemical properties that have led to their widespread use in various fields. Most zeolites are secondary minerals formed in a water-rich environment in the temperature range of 40 to 250 °C. Zeolites can form during the reaction of aqueous fluids with rocks in various geological environments. They are formed during diagenetic processes in sedimentary rocks (including volcanic deposits) that can be grouped into several geological environments or hydrological systems, such as open hydrological systems, closed hydrological systems, soil and surface sediments, and deep marine sediments. Zeolites in volcanic lava cavities are formed during burial metamorphism of lava masses, hydrothermal alteration of continental basalts, or diagenesis in areas with high heat flow caused by active geothermal systems. The purpose of this research is to determine the type of zeolite minerals formed in the volcanic host rock located in the magmatic belt of Alborz-Azerbaijan in the northwest of Iran and their formation based on the data obtained from stable isotopes of oxygen and deuterium. <strong> </strong> <strong>Materials and Methods</strong> The studied area is located 25 km southeast of Ardabil and east of Hir, which according to the map of the main tectonic subdivisions of Iran is located in the Tertiary-Quaternary volcanic zone on the western Alborz-Azerbaijan. In this research, X-ray fluorescence analysis (XRF) has been used to determine the type of host rocks. Petrographic studies were done using microscopic thin sections by polarizing microscope. X-ray diffraction (XRD) and electron microprobe (EMP) analysis have also been used to study the mineralogy of the samples and their chemical composition. The analysis of stable isotopes of oxygen and hydrogen has been performed to determine the isotopic composition of zeolite minerals by mass spectrometer (MS) method.   <strong>Results and Discussion</strong> Lithology in the studied area do not have high diversity and young volcanic rocks cover almost the entire area. The analysis of samples taken from the host rock showed that zeolites were formed in the Eocene andesitic volcanic unit with porphyry to megaporphyry texture. Plagioclases make up the most important phenocrysts of the host rock. Based on the results of XRD analysis and the study of thin sections, the zeolites of Hir region are composed of stilbite, barrerite, stellerite, chabazite, scolecite and mesolite minerals. The zeolite crystals are milky to pale yellow in color and often with radial, sugar cube, fascicled, intergrown blades and needles in the form of veins, filling the pore space and druses are scattered inside the cavities and fractures of the volcanic host rock. Zeolite mineralization in thin sections occurs as massive, open- space filling and inclusion. The massive types of stilbite often have a mosaic and intergrown texture, and its space filling types often have a radial and fan-like texture. Needle crystals of mesolite are formed in limited form as inclusions inside some stilbite crystals. The results of the microprobe analysis on the Ca+Mg–Na–K+Ba+Sr diagram showed that the studied zeolites including stilbite, scolecite and chabazite have a calcic nature, and stilbite and chabazite contain some amounts of sodium in addition to calcium as the main component. Also, the results of the analysis of oxygen and hydrogen isotopes of three selected zeolites showed that their isotopic values are close to each other and they are located in the δ¹⁸O-δD diagram in the field of meteoric hydrothermal waters and close to the kaolinite line. Being close the kaolinite line indicates their formation under surface conditions. Stilbite, scolecite and chabazite are formed at temperatures below 100 degrees Celsius in the order of stilbite → scolecite → chabazite. <strong> </strong> <strong>Conclusion</strong> The presence of evidences such as the formation of large and euhedral zeolite crystals, their limitation to the cracks and fractures of young volcanic rocks, and the absence of metamorphic zeolite facies minerals (such as perhenite) show the hydrothermal origin of zeolites in the Hir area. According to the appearance of zeolite minerals in the Hir area, the presence of suitable host rock and the results obtained from stable isotope studies, it is thought that the meteoric fluids penetrated into the volcanic units and were gradually heated. Then, by decomposition the glass matrix of the rocks and minerals prone to alteration such as plagioclase, they have provided the necessary materials (CaO, Al₂O₃, SiO₄, Na₂O) for the formation of zeolites. Zeolite minerals have formed by circulation inside the cavities, fractures and open spaces in the volcanic units in areas near the surface that have lower temperature. <strong> </strong> <strong>Introduction</strong> Zeolites, which form the largest group of silicate minerals, belong to the tectosilicate family, in which the [SiO4]^4- and [AlO4]^5- tetrahedrals are connected to each other in the form of a three-dimensional network so that an empty space is created between them. Channels are created in the structure of zeolites by placing the empty spaces one after the other. Zeolites have unique physical and chemical properties that have led to their widespread use in various fields. Most zeolites are secondary minerals formed in a water-rich environment in the temperature range of 40 to 250 °C. Zeolites can form during the reaction of aqueous fluids with rocks in various geological environments. They are formed during diagenetic processes in sedimentary rocks (including volcanic deposits) that can be grouped into several geological environments or hydrological systems, such as open hydrological systems, closed hydrological systems, soil and surface sediments, and deep marine sediments. Zeolites in volcanic lava cavities are formed during burial metamorphism of lava masses, hydrothermal alteration of continental basalts, or diagenesis in areas with high heat flow caused by active geothermal systems. The purpose of this research is to determine the type of zeolite minerals formed in the volcanic host rock located in the magmatic belt of Alborz-Azerbaijan in the northwest of Iran and their formation based on the data obtained from stable isotopes of oxygen and deuterium. <strong> </strong> <strong>Materials and Methods</strong> The studied area is located 25 km southeast of Ardabil and east of Hir, which according to the map of the main tectonic subdivisions of Iran is located in the Tertiary-Quaternary volcanic zone on the western Alborz-Azerbaijan. In this research, X-ray fluorescence analysis (XRF) has been used to determine the type of host rocks. Petrographic studies were done using microscopic thin sections by polarizing microscope. X-ray diffraction (XRD) and electron microprobe (EMP) analysis have also been used to study the mineralogy of the samples and their chemical composition. The analysis of stable isotopes of oxygen and hydrogen has been performed to determine the isotopic composition of zeolite minerals by mass spectrometer (MS) method.   <strong>Results and Discussion</strong> Lithology in the studied area do not have high diversity and young volcanic rocks cover almost the entire area. The analysis of samples taken from the host rock showed that zeolites were formed in the Eocene andesitic volcanic unit with porphyry to megaporphyry texture. Plagioclases make up the most important phenocrysts of the host rock. Based on the results of XRD analysis and the study of thin sections, the zeolites of Hir region are composed of stilbite, barrerite, stellerite, chabazite, scolecite and mesolite minerals. The zeolite crystals are milky to pale yellow in color and often with radial, sugar cube, fascicled, intergrown blades and needles in the form of veins, filling the pore space and druses are scattered inside the cavities and fractures of the volcanic host rock. Zeolite mineralization in thin sections occurs as massive, open- space filling and inclusion. The massive types of stilbite often have a mosaic and intergrown texture, and its space filling types often have a radial and fan-like texture. Needle crystals of mesolite are formed in limited form as inclusions inside some stilbite crystals. The results of the microprobe analysis on the Ca+Mg–Na–K+Ba+Sr diagram showed that the studied zeolites including stilbite, scolecite and chabazite have a calcic nature, and stilbite and chabazite contain some amounts of sodium in addition to calcium as the main component. Also, the results of the analysis of oxygen and hydrogen isotopes of three selected zeolites showed that their isotopic values are close to each other and they are located in the δ¹⁸O-δD diagram in the field of meteoric hydrothermal waters and close to the kaolinite line. Being close the kaolinite line indicates their formation under surface conditions. Stilbite, scolecite and chabazite are formed at temperatures below 100 degrees Celsius in the order of stilbite → scolecite → chabazite. <strong> </strong> <strong>Conclusion</strong> The presence of evidences such as the formation of large and euhedral zeolite crystals, their limitation to the cracks and fractures of young volcanic rocks, and the absence of metamorphic zeolite facies minerals (such as perhenite) show the hydrothermal origin of zeolites in the Hir area. According to the appearance of zeolite minerals in the Hir area, the presence of suitable host rock and the results obtained from stable isotope studies, it is thought that the meteoric fluids penetrated into the volcanic units and were gradually heated. Then, by decomposition the glass matrix of the rocks and minerals prone to alteration such as plagioclase, they have provided the necessary materials (CaO, Al₂O₃, SiO₄, Na₂O) for the formation of zeolites. Zeolite minerals have formed by circulation inside the cavities, fractures and open spaces in the volcanic units in areas near the surface that have lower temperature. <strong> </strong> https://esrj.sbu.ac.ir/article_104876_e9027e1baba162aedb17590f6149d926.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2025 02 19 Estimation of cross-sectional kriging for modeling and evaluation of ore reserve (Case study: Emarat Pb-Zn deposit) Estimation of cross-sectional kriging for modeling and evaluation of ore reserve (Case study: Emarat Pb-Zn deposit) 82 103 104878 10.48308/esrj.2024.236155.1229 FA Reza Ahmadi Department of Mining Engineering, College of Earth Sciences Engineering, Arak University of Technology, Arak, Iran Mohammad Saleh Ahmadi Department of Mining Engineering, College of Earth Sciences Engineering, Arak University of Technology, Arak, Iran Journal Article 2024 07 02 <strong>Introduction</strong> Modeling the spatial distribution of grade and reserve estimation are the most important issues and the main goal of exploration operation. This kind of modeling depending on the amount, type and method of carried out exploration works and available exploration information, is performed using various methods. In the following situations, underground exploratory works such as exploration tunnels are carried out: 1- vertical or steep slope mineral deposits, 2- mountainous geomorphology with high altitudes and rough topography, 3- difficult or impossible situation of drilling, and 4- very high thickness or volume of overburden on the mineral deposit. In fact, in such conditions, for the most of mineral deposits there is continuity in the vertical direction whereas the changes are mostly along the horizontal surfaces. In the present research, block modeling and geostatistical reserve estimation of the Emarat Pb-Zn deposit located in the Markazi province have been carried out using log-kriging method with a different approach and a delicate technique. To achieve the goal, firstly, processing, modeling and geostatistical estimation of total Pb-Zn assay data obtained from exploratory tunnels drilled at the various elevation levels were performed as two-dimensional, separately. Then, using the gained results, three-dimensional estimation process for whole of the deposit was done. Such an approach for geostatistical estimation of a deposit grade and reserve discovered by underground exploratory works, has not been reported in any research before. <strong> </strong> <strong>Materials and Methods</strong> Study deposit and available exploration data The Emarat Pb-Zn deposit is located about 45 km southwest of Arak city, between longitudes 49°30' to 49°45' East and latitudes 33°45' to 34°0' North, in an area with the altitude of 2,180 m above sea level. The topography of the Emarat region is very uneven. Uniform stratigraphy, severe folding, absence of igneous rocks and strati-banding are the geological characteristics of the region, with the strike of folding conforming to the trend of the Zagros folding. The Emarat Pb-Zn deposit is located on the Sanandaj-Sirjan tectonic zone and the Malayer-Isfahan lead and zinc metallogenic belt. The geological units of the region are generally carbonate, shale, and marl sedimentary units from the Jurassic to Cretaceous periods, with folding and faulting, which are composed of steep slopes. In general, the Emarat Pb-Zn deposit is a synclinorium trending northwest-southeast, with a length of 1.5 km and a width ranging from 250 to 850 m. In the Emarat deposit, lead and zinc minerals are located within a calcareous siliceous layer interface of a dark gray massive limestone band in the footwall and a Cretaceous shale layer in the hanging-wall. This formation belongs to the lower or middle Cretaceous. In the Emarat Pb-Zn deposit, many activities containing drilling of horizontal exploratory- exploitational tunnels and crosscuts with a total length of 11,000 m in six elevation levels with a height difference of about 10 m have been carried out, whereas 1,238 Pb-Zn assay data of samples taken from the tunnels are available.   <strong>Cross-sectional kriging method</strong> From the dimensional viewpoint, in the case of two-dimensional acquisitioned data, such as assay data of surface samples, processing operation and geostatistical estimation with Kriging method is done as two-dimensional. There are also cases in which, although the data acquisition is three-dimensional, it is better to perform the geostatistical estimation first on two-dimensional surfaces, then the estimation results obtained from different levels are combined with each other and the final three-dimensional model is produced. This type of kriging estimation is called cross-sectional kriging, which can be done along a series of horizontal or vertical sections. Of course, its use in horizontal sections is more logical and desirable. Since in such cases there is practically no sample and consequently no data in the gap between two consecutive adjacent surfaces, and all processed and estimated data is related to samples taken from two-dimensional surfaces, therefore, the distance and spatial relationship of the data at each level is higher. So that the continuity of the deposit is better revealed at each level. <strong>Log-Kriging method</strong> If the statistical distribution of the used data is not normal, linear kriging estimation methods cannot be employed, because in this case, there will be a correlation between variance and mean and pseudo-anisotropy appears in the strike variograms. In such conditions, it is better to normalize the data with a suitable transformation method such as logarithmic so that linear methods can be used for estimation. Next, the estimation operation is applied on the logarithm of data with the ordinary (or simple) kriging method, and then the estimated values are converted to real values with an inverse transformation. This non-linear kriging method is called log-kriging. <strong> </strong> <strong>Results and Discussion</strong> Based on the statistical processing of total Pb-Zn assay data for the elevation levels of 2032, 2024, 1998, 1988, 1978 and 1964-1968 m separately, data distribution was known lognormal, normalized with two and three parameters logarithmic transformation. Also, plotting standard deviation against the total Pb-Zn assay data of the various elevation levels revealed dependence of the variance with mean. To analyze the spatial structure of the region, horizontal strike variograms with azimuths ranging from 25 to 135 degrees were drawn for each elevation level separately, as well as two better horizontal variograms in perpendicular directions were selected. Due to the lognormal distribution of the assay data, to avoid the appearance of pseudo-anisotropy, the variography was performed for the transformed total lead and zinc assay data. All theoretical variogram models fitted over empirical variograms are spherical type, and strike variograms in various directions have the same sill but different ranges. Therefore, the studied area has geometric anisotropy. Using variography of the different elevation levels and determination of ellipse search radii, grade of each elevation level was estimated with geostatistical cross-sectional log-kriging method for 10 by 10 m blocks as well as isograde map from estimation process was drawn. According to the isograde maps at most elevation levels of the Emarat Pb-Zn deposit, the mineral deposit grade in the eastern half is higher than the western part. Afterward, the estimation process was validated through cross-validation approach with the well-known jackknife method and kriging estimation error maps of each elevation level. In such validation method based on the variogram model, each time one of the input data (known) is estimated through the Kriging method using neighboring samples around that sample, while the estimated values are compared with the actual ones. In other words, any known data is estimated by assuming that its value is unknown. In this regard, the determination coefficient of regression between actual and estimated grade values in all cases except elevation level of 2024 is more than 0.5, indicating good correlation between the data. Thus, the estimation has a good validity. To create block model of the deposit, a prototype grid model with size of 1110×530×265 m, cell size of 10×10×10 m and estimated assay data of different elevation levels through the advanced inverse distance weighted (AIDW) algorithm, was used. The size of blocks was set to 10*10*10 m based on the size and extension area of mineral deposit in each elevation level, as well as distance of the elevation levels. In AIDW algorithm, it is possible to weight the distance with different powers in various directions. In this case, weights of two and one were assigned to the data in the horizontal (exploration tunnels at each elevation level) and vertical directions (distance between different elevation levels) respectively, due to greater variety in the horizontal direction. At the end, different categories of proven, probable and prospected reserves were determined for seven cut-off grades of 4, 4.5, 5, 5.5, 6, 6.5 and 7 percent. According to the tonnage-grade diagram of the total deposit for different categories of proven, probable and prospected reserves, the proven reserve and related average grade diagrams reveal little changes for various cut off grades locating a short distance from each other. The low changes and almost horizontal state of both two graphs are due to the high total grade of lead and zinc in the proven reserve category, discovered using more exploration activities with less estimation error. <strong> </strong> <strong>Conclusion</strong> In this research, a new approach was used for geostatistical estimation of grade and ore reserve, in the Emarat Pb-Zn deposit. To achieve the goal, based on data mining of different elevation levels of the deposit, first a 2D geostatistical estimation was carried out using the cross-sectional log-kriging method for each elevation level, separately. Afterward, 3D block model of the deposit was created. In fact, in this study, the exploration data available on horizontal surfaces at the different elevation levels of the Emarat Pb-Zn deposit were generalized to 3D space through variography and 2D geostatistical estimation, by creating block model. The results of the research show that in some cases according to the conditions of the studied deposit and type of carried out exploration activities, modeling and estimation of ore reserve is possible more simply with optimal accuracy through the relatively new and accurate geostatistical methods. This scenario was performed by the approach of dimension reduction of estimation space and then converting the estimation space from two to three dimensions. The results of this research will be useful for all earth sciences users, including geologists, mining exploration and exploitation engineers. <strong> </strong> <strong>Introduction</strong> Modeling the spatial distribution of grade and reserve estimation are the most important issues and the main goal of exploration operation. This kind of modeling depending on the amount, type and method of carried out exploration works and available exploration information, is performed using various methods. In the following situations, underground exploratory works such as exploration tunnels are carried out: 1- vertical or steep slope mineral deposits, 2- mountainous geomorphology with high altitudes and rough topography, 3- difficult or impossible situation of drilling, and 4- very high thickness or volume of overburden on the mineral deposit. In fact, in such conditions, for the most of mineral deposits there is continuity in the vertical direction whereas the changes are mostly along the horizontal surfaces. In the present research, block modeling and geostatistical reserve estimation of the Emarat Pb-Zn deposit located in the Markazi province have been carried out using log-kriging method with a different approach and a delicate technique. To achieve the goal, firstly, processing, modeling and geostatistical estimation of total Pb-Zn assay data obtained from exploratory tunnels drilled at the various elevation levels were performed as two-dimensional, separately. Then, using the gained results, three-dimensional estimation process for whole of the deposit was done. Such an approach for geostatistical estimation of a deposit grade and reserve discovered by underground exploratory works, has not been reported in any research before. <strong> </strong> <strong>Materials and Methods</strong> Study deposit and available exploration data The Emarat Pb-Zn deposit is located about 45 km southwest of Arak city, between longitudes 49°30' to 49°45' East and latitudes 33°45' to 34°0' North, in an area with the altitude of 2,180 m above sea level. The topography of the Emarat region is very uneven. Uniform stratigraphy, severe folding, absence of igneous rocks and strati-banding are the geological characteristics of the region, with the strike of folding conforming to the trend of the Zagros folding. The Emarat Pb-Zn deposit is located on the Sanandaj-Sirjan tectonic zone and the Malayer-Isfahan lead and zinc metallogenic belt. The geological units of the region are generally carbonate, shale, and marl sedimentary units from the Jurassic to Cretaceous periods, with folding and faulting, which are composed of steep slopes. In general, the Emarat Pb-Zn deposit is a synclinorium trending northwest-southeast, with a length of 1.5 km and a width ranging from 250 to 850 m. In the Emarat deposit, lead and zinc minerals are located within a calcareous siliceous layer interface of a dark gray massive limestone band in the footwall and a Cretaceous shale layer in the hanging-wall. This formation belongs to the lower or middle Cretaceous. In the Emarat Pb-Zn deposit, many activities containing drilling of horizontal exploratory- exploitational tunnels and crosscuts with a total length of 11,000 m in six elevation levels with a height difference of about 10 m have been carried out, whereas 1,238 Pb-Zn assay data of samples taken from the tunnels are available.   <strong>Cross-sectional kriging method</strong> From the dimensional viewpoint, in the case of two-dimensional acquisitioned data, such as assay data of surface samples, processing operation and geostatistical estimation with Kriging method is done as two-dimensional. There are also cases in which, although the data acquisition is three-dimensional, it is better to perform the geostatistical estimation first on two-dimensional surfaces, then the estimation results obtained from different levels are combined with each other and the final three-dimensional model is produced. This type of kriging estimation is called cross-sectional kriging, which can be done along a series of horizontal or vertical sections. Of course, its use in horizontal sections is more logical and desirable. Since in such cases there is practically no sample and consequently no data in the gap between two consecutive adjacent surfaces, and all processed and estimated data is related to samples taken from two-dimensional surfaces, therefore, the distance and spatial relationship of the data at each level is higher. So that the continuity of the deposit is better revealed at each level. <strong>Log-Kriging method</strong> If the statistical distribution of the used data is not normal, linear kriging estimation methods cannot be employed, because in this case, there will be a correlation between variance and mean and pseudo-anisotropy appears in the strike variograms. In such conditions, it is better to normalize the data with a suitable transformation method such as logarithmic so that linear methods can be used for estimation. Next, the estimation operation is applied on the logarithm of data with the ordinary (or simple) kriging method, and then the estimated values are converted to real values with an inverse transformation. This non-linear kriging method is called log-kriging. <strong> </strong> <strong>Results and Discussion</strong> Based on the statistical processing of total Pb-Zn assay data for the elevation levels of 2032, 2024, 1998, 1988, 1978 and 1964-1968 m separately, data distribution was known lognormal, normalized with two and three parameters logarithmic transformation. Also, plotting standard deviation against the total Pb-Zn assay data of the various elevation levels revealed dependence of the variance with mean. To analyze the spatial structure of the region, horizontal strike variograms with azimuths ranging from 25 to 135 degrees were drawn for each elevation level separately, as well as two better horizontal variograms in perpendicular directions were selected. Due to the lognormal distribution of the assay data, to avoid the appearance of pseudo-anisotropy, the variography was performed for the transformed total lead and zinc assay data. All theoretical variogram models fitted over empirical variograms are spherical type, and strike variograms in various directions have the same sill but different ranges. Therefore, the studied area has geometric anisotropy. Using variography of the different elevation levels and determination of ellipse search radii, grade of each elevation level was estimated with geostatistical cross-sectional log-kriging method for 10 by 10 m blocks as well as isograde map from estimation process was drawn. According to the isograde maps at most elevation levels of the Emarat Pb-Zn deposit, the mineral deposit grade in the eastern half is higher than the western part. Afterward, the estimation process was validated through cross-validation approach with the well-known jackknife method and kriging estimation error maps of each elevation level. In such validation method based on the variogram model, each time one of the input data (known) is estimated through the Kriging method using neighboring samples around that sample, while the estimated values are compared with the actual ones. In other words, any known data is estimated by assuming that its value is unknown. In this regard, the determination coefficient of regression between actual and estimated grade values in all cases except elevation level of 2024 is more than 0.5, indicating good correlation between the data. Thus, the estimation has a good validity. To create block model of the deposit, a prototype grid model with size of 1110×530×265 m, cell size of 10×10×10 m and estimated assay data of different elevation levels through the advanced inverse distance weighted (AIDW) algorithm, was used. The size of blocks was set to 10*10*10 m based on the size and extension area of mineral deposit in each elevation level, as well as distance of the elevation levels. In AIDW algorithm, it is possible to weight the distance with different powers in various directions. In this case, weights of two and one were assigned to the data in the horizontal (exploration tunnels at each elevation level) and vertical directions (distance between different elevation levels) respectively, due to greater variety in the horizontal direction. At the end, different categories of proven, probable and prospected reserves were determined for seven cut-off grades of 4, 4.5, 5, 5.5, 6, 6.5 and 7 percent. According to the tonnage-grade diagram of the total deposit for different categories of proven, probable and prospected reserves, the proven reserve and related average grade diagrams reveal little changes for various cut off grades locating a short distance from each other. The low changes and almost horizontal state of both two graphs are due to the high total grade of lead and zinc in the proven reserve category, discovered using more exploration activities with less estimation error. <strong> </strong> <strong>Conclusion</strong> In this research, a new approach was used for geostatistical estimation of grade and ore reserve, in the Emarat Pb-Zn deposit. To achieve the goal, based on data mining of different elevation levels of the deposit, first a 2D geostatistical estimation was carried out using the cross-sectional log-kriging method for each elevation level, separately. Afterward, 3D block model of the deposit was created. In fact, in this study, the exploration data available on horizontal surfaces at the different elevation levels of the Emarat Pb-Zn deposit were generalized to 3D space through variography and 2D geostatistical estimation, by creating block model. The results of the research show that in some cases according to the conditions of the studied deposit and type of carried out exploration activities, modeling and estimation of ore reserve is possible more simply with optimal accuracy through the relatively new and accurate geostatistical methods. This scenario was performed by the approach of dimension reduction of estimation space and then converting the estimation space from two to three dimensions. The results of this research will be useful for all earth sciences users, including geologists, mining exploration and exploitation engineers. <strong> </strong> https://esrj.sbu.ac.ir/article_104878_dbd277d8b5c217252db3c743398e2b4b.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2025 02 19 Combination of Landsat-8 and Sentinel-2 images in order to detect alterations of porphyry deposits (Masjed Daghi), northwest Iran Combination of Landsat-8 and Sentinel-2 images in order to detect alterations of porphyry deposits (Masjed Daghi), northwest Iran 104 127 104879 10.48308/esrj.2024.234408.1211 FA Maryam Khani Alamuti Department of Mineral Exploration, Faculty of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran 0009-0009-1923-7861 Susan Ebrahimi Department of Mineral Exploration, Faculty of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran 0000-0002-1323-2391 Behnaz Bigdeli Department of Geotechnical and Transport Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran 0000-0001-8082-4373 Journal Article 2024 04 27 <strong>Introduction</strong> Remote sensing is one of the widely used methods in geology and exploration of mineral deposits and plays an important role I identifying changes. Different methods of remote sensing have made it possible to investigate and study a wide range with accuracy, speed and less cost. Since the change related to porphyry mineralization have a suitable expansion; therefore, this type of deposits can be suitable index in the methods and discoveries of porphyry deposits. Masjed Daghi copper porphyry area is located 35 km east of Julfa in the East Azerbaijan province. The formation of epithermal gold veins on the porphyry deposit and the spatial and temporal relationship of two deposits have been investigated. The simultaneous use of Sentinel-2 sensor and Landsat-8 satellite and supervised classification methods based on machine learning was done for the first time in this research on Masjed Daghi porphyry deposit in northwest Iran. The aim of this study is to identify the types of alteration associated with porphyry deposits using different techniques of processing images from Sentinel-2 and Landsat-8 satellites; which can be a suitable exploration guide for porphyry copper deposits in Iran. In this study, band combination, band ratio (BR) and least squares regression (LS-Fit) methods have been used to determine the location of alteration zones. Also, supervised classification methods based on machine learning such as: maximum similarity (ML), neural network methods (ANN) and support vector machines (SVM) and the combination of these three classifications using the maximum voting (MV) method have been used for the accuracy and precision of using these images, and the positive impact and performance of these classifications in separating lithologies in geological maps has been investigated and confirmed (Farhadi et al., 2024). Also, these methods have been investigated in the chemical distribution of elements in the Iran Kouh lead and zinc deposit, and the results of this study showed that these methods were promising for predicting the elemental distribution of minerals (Farhadi et al. 2022). In this study, the results obtained were verified and confirmed using field evidence and geological studies. Simultaneous use of the Sentinel-2 sensor and the Landsat-8 satellite and classification methods supervised machine learning-based classification was performed for the first time in this research on a porphyry deposit in northwestern Iran. Also, using the output of the band ratio and band combination methods and the least squares regression as initial inputs as training and test data for use in machine learning methods and combining three classifications with the maximum voting method in the Landsat-8 satellite and the Sentinel-2 sensor was used for the first time, which has yielded good results. <strong>Materials and Methods</strong> Detailed studies have been conducted on the alterations of the Masjed Daghi region, and based on them, six alterations have been recognized in the region. These alterations include potassic, phyllic, intermediate argillic, advanced argillic, silicic, and propylitic. Silicic, advanced argillic, intermediate argillic, and propylitic alterations are associated with epithermal gold mineralization and extend from the inside of the vein outwards, respectively. Alterations associated with porphyry copper mineralization include potassic, phyllic, intermediate argillic, and propylitic. Potassic alterations: This alteration is visible with a small extension (2000 m²) around and on the adjacent of the Arpachay River. Geological studies in the area show that potassic alteration is affected by phyllic and argillic alterations and causes overlap between these alterations. The mineralogy of this alteration includes potassium feldspar, biotite, and magnetite with some sericite, chlorite, and clay minerals. Phyllic alterations: Tis alteration covers a large part of the area and covers the potassic alteration in the form and haloes around and on the adjacent of the Arpachay River. Mineralogical studies show that silicate minerals such as plagioclase, potassium feldspar, and ferromagnesian minerals (hornblende and biotite) in the parent rock have been altered and replaced by sericite and quartz as the main minerals and chlorite as the secondary mineral along the sulfide minerals. This alteration is widely overlapped by argillic alteration. Also, stony silica veins with minerals show a relatively good spread at the regional level and in the phyllic zone. Advanced argillic:  This alteration is limited and formed in the vicinity of gold-bearing silica veins. This alteration had a great impact on the host rock (trachyandesite) and has transformed the plagioclase in the host rock into the clay minerals and destroyed the original texture of the rock. The minerals constituting this alteration include quartz, kaolinite, hypogene alunite, barite, pyrite and tourmaline. Moderate argillic alteration: It is spread with a relatively high spread in the middle part of the mineralization area and in many cases overlaps with phyllic alteration. The mineralogical composition of this alteration includes kaolinite, illite, quartz, and carbonate. Propylitic alteration: This alteration is the outermost alteration zone observed on the eastern margin of the region with a relatively limited extension. The characteristic minerals of this alteration are epidote, chlorite, and calcite, where hornblende and pyroxene have been transformed into chlorite and calcite, and plagioclase has been replaced by calcite, epidote, chlorite and also clay minerals. Siliceous alteration: This type of alteration is found around mineralized veins, which has provided a suitable environment and conditions for gold mineralization. The high silica values in the region indicate that the hydrothermal solutions are saturated with silica. The siliceous alteration zone is one of the most alteration important alterations in the region, which appears in the form of veins and veinlets, these types of alterations are the main hosts of the gold mineralization. Mineralization: he Masjeddaghi mineralization system consist of two types of porphyry copper mineralization and epithermal gold. The most important mineralization in the Masjeddaghi porphyry deposit include rutile, molybdenite, magnetite, pyrite, chalcopyrite, bornite, sphalerite, chalcocite, and covellite. Epithermal gold mineralization include pyrite, chalcopyrite, bornite, galena, sphalerite, and gold associated with quartz, barite and anhydrite.                <strong> </strong> <strong>Results and Discussion</strong> The oldest rock unit of the region includes flysch sediment of Eocene age associated limestone, shale and conglomerate. The lithological volcanic composition are Eocene trachyandesite associated Oligocene monzodiorite. These host rock suffering potassic, phyllic, argillic, propylitic and silicic alteration. Mineralization system consist of two types of porphyry and epithermal systems. The most important minerals are molybdenite, magnetite, pyrite, bornite, chalcopyrite, and sphalerite in the porphyry system and pyrite, chalcopyrite, sphalerite, gold associated quartz, barite, and anhydrite in the epithermal system. Landsat-8 satellite images of the three supervised classifications of ANN, ML and SVM have the accuracy of 77.71%, 70.48% and 79.23% respectively. In the Sentinel-2 sensor, the tree supervised classifications of ANN, Ml and SVM in Masjed Daghi region have the accuracy of 78.69%, 59.16% and 7.75%, respectively. The obtained results show the superiority of Sentinel-2 sensor over Landsat-8 in highlighting the variations in the study area of Masjed Daghi. Also, by comparing to kappa coefficients obtained from Landsat-8 and Sentinel-2, it emphasizes the superiority of Sentinel-2. Among the classifications applied on the images, SVM classification is more accurate in both satellites and sensors; this point indicates the better performance of Support Vector Machine (SVM) algorithm. But ML classification in Landsat-8 has a better performance that sentinel-2, which the kappa coefficient results will also confirm this issue. The output results of overall accuracy (OA) for the maximum voting (MV) method compared to support vector machine algorithm method have increased by about 3.75% in the Landsat-8 satellite. Maximum voting with 82.38% overall accuracy and 0.6868 kappa coefficient for Landsat-8 satellite indicate; the combining the output data of the  classification with the maximum voting method improve the identification of changes. Also, for the Sentinel-2 coefficient f 0.7070 has increased by about 4.5% compared to the support vector machine method. <strong> </strong> <strong>Conclusion</strong> Landsat-8 and Sentinel-2 data and its compliance with the geological range of Masjed Daghi region show that the Sentinel-2 sensor has better and higher accuracy than the Landsat-8 satellite. 2. The classification of the support vector in the Landsat-8 satellite and Sentinel-2 sensor in the study area of Masjed Daghi has a higher accuracy and kappa coefficient, and the Sentinel-2 sensor has a higher accuracy and precision then the Landsat-8 satellite. Using the classification (Neural network, maximum similarity and support vector machine) using the Sentinel-2 sensor and the Landsat-8 satellite shows; the support machine classification in the Landsat-8 satellite and the Sentinel-2sensor in the Masjeddaghi study area has higher accuracy and kappa coefficient, and the Sentinel-2 sensor has higher accuracy and precision than the Landsat-8 satellite. Also these three classification compared to the band ration methods, the last squares regression and the band combination have higher accuracy for highlighting the changes in the study area. In the accuracy section, all three classification are compared with each other in numerical formed finally their combination. 3. Sentinel-2 and Landsat-8 data show the overall accuracy output for the maximum voting method compared to the support vector machine algorithm method in satellite Landsat-8 has increased by about 75.3%, which shows that combining the output data of the classifications using the maximum voting approach has improved the identification of changes. 4. Sentinel-2 detector has a very high accuracy in the presented classifications and in the maximum ratio voting method due to its better spectral and spatial power to the Landsat-8 satellite. <strong>Introduction</strong> Remote sensing is one of the widely used methods in geology and exploration of mineral deposits and plays an important role I identifying changes. Different methods of remote sensing have made it possible to investigate and study a wide range with accuracy, speed and less cost. Since the change related to porphyry mineralization have a suitable expansion; therefore, this type of deposits can be suitable index in the methods and discoveries of porphyry deposits. Masjed Daghi copper porphyry area is located 35 km east of Julfa in the East Azerbaijan province. The formation of epithermal gold veins on the porphyry deposit and the spatial and temporal relationship of two deposits have been investigated. The simultaneous use of Sentinel-2 sensor and Landsat-8 satellite and supervised classification methods based on machine learning was done for the first time in this research on Masjed Daghi porphyry deposit in northwest Iran. The aim of this study is to identify the types of alteration associated with porphyry deposits using different techniques of processing images from Sentinel-2 and Landsat-8 satellites; which can be a suitable exploration guide for porphyry copper deposits in Iran. In this study, band combination, band ratio (BR) and least squares regression (LS-Fit) methods have been used to determine the location of alteration zones. Also, supervised classification methods based on machine learning such as: maximum similarity (ML), neural network methods (ANN) and support vector machines (SVM) and the combination of these three classifications using the maximum voting (MV) method have been used for the accuracy and precision of using these images, and the positive impact and performance of these classifications in separating lithologies in geological maps has been investigated and confirmed (Farhadi et al., 2024). Also, these methods have been investigated in the chemical distribution of elements in the Iran Kouh lead and zinc deposit, and the results of this study showed that these methods were promising for predicting the elemental distribution of minerals (Farhadi et al. 2022). In this study, the results obtained were verified and confirmed using field evidence and geological studies. Simultaneous use of the Sentinel-2 sensor and the Landsat-8 satellite and classification methods supervised machine learning-based classification was performed for the first time in this research on a porphyry deposit in northwestern Iran. Also, using the output of the band ratio and band combination methods and the least squares regression as initial inputs as training and test data for use in machine learning methods and combining three classifications with the maximum voting method in the Landsat-8 satellite and the Sentinel-2 sensor was used for the first time, which has yielded good results. <strong>Materials and Methods</strong> Detailed studies have been conducted on the alterations of the Masjed Daghi region, and based on them, six alterations have been recognized in the region. These alterations include potassic, phyllic, intermediate argillic, advanced argillic, silicic, and propylitic. Silicic, advanced argillic, intermediate argillic, and propylitic alterations are associated with epithermal gold mineralization and extend from the inside of the vein outwards, respectively. Alterations associated with porphyry copper mineralization include potassic, phyllic, intermediate argillic, and propylitic. Potassic alterations: This alteration is visible with a small extension (2000 m²) around and on the adjacent of the Arpachay River. Geological studies in the area show that potassic alteration is affected by phyllic and argillic alterations and causes overlap between these alterations. The mineralogy of this alteration includes potassium feldspar, biotite, and magnetite with some sericite, chlorite, and clay minerals. Phyllic alterations: Tis alteration covers a large part of the area and covers the potassic alteration in the form and haloes around and on the adjacent of the Arpachay River. Mineralogical studies show that silicate minerals such as plagioclase, potassium feldspar, and ferromagnesian minerals (hornblende and biotite) in the parent rock have been altered and replaced by sericite and quartz as the main minerals and chlorite as the secondary mineral along the sulfide minerals. This alteration is widely overlapped by argillic alteration. Also, stony silica veins with minerals show a relatively good spread at the regional level and in the phyllic zone. Advanced argillic:  This alteration is limited and formed in the vicinity of gold-bearing silica veins. This alteration had a great impact on the host rock (trachyandesite) and has transformed the plagioclase in the host rock into the clay minerals and destroyed the original texture of the rock. The minerals constituting this alteration include quartz, kaolinite, hypogene alunite, barite, pyrite and tourmaline. Moderate argillic alteration: It is spread with a relatively high spread in the middle part of the mineralization area and in many cases overlaps with phyllic alteration. The mineralogical composition of this alteration includes kaolinite, illite, quartz, and carbonate. Propylitic alteration: This alteration is the outermost alteration zone observed on the eastern margin of the region with a relatively limited extension. The characteristic minerals of this alteration are epidote, chlorite, and calcite, where hornblende and pyroxene have been transformed into chlorite and calcite, and plagioclase has been replaced by calcite, epidote, chlorite and also clay minerals. Siliceous alteration: This type of alteration is found around mineralized veins, which has provided a suitable environment and conditions for gold mineralization. The high silica values in the region indicate that the hydrothermal solutions are saturated with silica. The siliceous alteration zone is one of the most alteration important alterations in the region, which appears in the form of veins and veinlets, these types of alterations are the main hosts of the gold mineralization. Mineralization: he Masjeddaghi mineralization system consist of two types of porphyry copper mineralization and epithermal gold. The most important mineralization in the Masjeddaghi porphyry deposit include rutile, molybdenite, magnetite, pyrite, chalcopyrite, bornite, sphalerite, chalcocite, and covellite. Epithermal gold mineralization include pyrite, chalcopyrite, bornite, galena, sphalerite, and gold associated with quartz, barite and anhydrite.                <strong> </strong> <strong>Results and Discussion</strong> The oldest rock unit of the region includes flysch sediment of Eocene age associated limestone, shale and conglomerate. The lithological volcanic composition are Eocene trachyandesite associated Oligocene monzodiorite. These host rock suffering potassic, phyllic, argillic, propylitic and silicic alteration. Mineralization system consist of two types of porphyry and epithermal systems. The most important minerals are molybdenite, magnetite, pyrite, bornite, chalcopyrite, and sphalerite in the porphyry system and pyrite, chalcopyrite, sphalerite, gold associated quartz, barite, and anhydrite in the epithermal system. Landsat-8 satellite images of the three supervised classifications of ANN, ML and SVM have the accuracy of 77.71%, 70.48% and 79.23% respectively. In the Sentinel-2 sensor, the tree supervised classifications of ANN, Ml and SVM in Masjed Daghi region have the accuracy of 78.69%, 59.16% and 7.75%, respectively. The obtained results show the superiority of Sentinel-2 sensor over Landsat-8 in highlighting the variations in the study area of Masjed Daghi. Also, by comparing to kappa coefficients obtained from Landsat-8 and Sentinel-2, it emphasizes the superiority of Sentinel-2. Among the classifications applied on the images, SVM classification is more accurate in both satellites and sensors; this point indicates the better performance of Support Vector Machine (SVM) algorithm. But ML classification in Landsat-8 has a better performance that sentinel-2, which the kappa coefficient results will also confirm this issue. The output results of overall accuracy (OA) for the maximum voting (MV) method compared to support vector machine algorithm method have increased by about 3.75% in the Landsat-8 satellite. Maximum voting with 82.38% overall accuracy and 0.6868 kappa coefficient for Landsat-8 satellite indicate; the combining the output data of the  classification with the maximum voting method improve the identification of changes. Also, for the Sentinel-2 coefficient f 0.7070 has increased by about 4.5% compared to the support vector machine method. <strong> </strong> <strong>Conclusion</strong> Landsat-8 and Sentinel-2 data and its compliance with the geological range of Masjed Daghi region show that the Sentinel-2 sensor has better and higher accuracy than the Landsat-8 satellite. 2. The classification of the support vector in the Landsat-8 satellite and Sentinel-2 sensor in the study area of Masjed Daghi has a higher accuracy and kappa coefficient, and the Sentinel-2 sensor has a higher accuracy and precision then the Landsat-8 satellite. Using the classification (Neural network, maximum similarity and support vector machine) using the Sentinel-2 sensor and the Landsat-8 satellite shows; the support machine classification in the Landsat-8 satellite and the Sentinel-2sensor in the Masjeddaghi study area has higher accuracy and kappa coefficient, and the Sentinel-2 sensor has higher accuracy and precision than the Landsat-8 satellite. Also these three classification compared to the band ration methods, the last squares regression and the band combination have higher accuracy for highlighting the changes in the study area. In the accuracy section, all three classification are compared with each other in numerical formed finally their combination. 3. Sentinel-2 and Landsat-8 data show the overall accuracy output for the maximum voting method compared to the support vector machine algorithm method in satellite Landsat-8 has increased by about 75.3%, which shows that combining the output data of the classifications using the maximum voting approach has improved the identification of changes. 4. Sentinel-2 detector has a very high accuracy in the presented classifications and in the maximum ratio voting method due to its better spectral and spatial power to the Landsat-8 satellite. https://esrj.sbu.ac.ir/article_104879_dc606ae470ec8399ccf538a7adc8827a.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2025 02 19 Tracking moisture sources and analysis of instability indicators leading to heavy rains in Northwest Iran Tracking moisture sources and analysis of instability indicators leading to heavy rains in Northwest Iran 128 151 105224 10.48308/esrj.2025.235940.1225 FA Ali Shahi Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran 0000-0001-9607-4132 Bromand Salahi Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran 0000-0003-4826-6185 Journal Article 2024 06 05 <strong>Introduction</strong> The study of climate hazards such as heavy precipitation is very important due to its direct impact on flooding. Due to the climate change that the world has experienced, climate hazards have increased. What is certain is that humans cannot prevent the occurrence of climate hazards, but by being aware of these events in advance, under the influence of climate forecasts, they can reduce the destructive consequences of these hazards. Also, considering the very prominent role of humans in increasing the most important climate forcing, namely greenhouse gases, especially carbon dioxide, by managing fossil fuels and increasing new energy power plants, which are known as clean energies, climate changes that cause extreme events can be reduced. Another issue is the management of heavy precipitation to control large amounts of water for use in agriculture, which seems to be able to benefit from this weather event by taking measures. Another important point regarding heavy precipitation in the northwest region of Iran is to pay attention to the construction of residential areas in places far from rivers, which are vulnerable to flooding caused by heavy precipitation. The most important cause of extreme events such as heavy precipitation is currently climate change. The main factor causing climate change and desertification is greenhouse gases. The most important type of greenhouse gas is carbon dioxide. The main reason for the increase in this gas, which has a long life and is very poorly degradable, is humans. In other words, the main cause of the increase and intensification of extreme events is human misbehavior in dealing with nature. The northwest region of Iran is prone to heavy precipitation due to its mountainous topography and location on the main path of Mediterranean cyclones. This research was conducted with the aim of identifying the moisture sources of heavy precipitation in northwest Iran and also analyzing the instability indicators related to it. <strong> </strong> <strong>Materials and Methods</strong> The study area in this study is northwest Iran, including West Azarbaijan, East Azarbaijan, Ardabil, North Kurdistan, and West Zanjan provinces. In this study, daily and hourly (3-hour) precipitation data and hourly (3-hour) wind data (speed and direction) were obtained from the Iranian Meteorological Organization (www.irimo.ir) for 23 synoptic stations located in northwest Iran during the period 1990-2019. The upper atmosphere data of the Tabriz station (the only upper atmosphere station in northwest Iran) were obtained from the University of Wyoming website (http://weather.uwyo.edu/upperair/sounding.html). The upper atmosphere data of this study were obtained from the NCEP/NCAR database (www.cdc.noaa.gov). Trial and error estimates showed that if the percentile is higher than 99 and the area covered by heavy precipitation is more than 30%, synoptic conditions will provide a good justification for heavy precipitation. In this paper, days when at least 7 stations in the study area simultaneously had at least 20 mm of precipitation were selected. In this study, using TTI, CAPE, KI, LI, SI and SWEAT indices, the state of atmospheric instability in northwest Iran was evaluated at a representative station in the region (Tabriz) on days of heavy precipitation (43 days). Based on factor analysis in the SPSS software environment, the main factors were identified from among the 6 indicators, then using cluster analysis, the main clusters were extracted and the Skew-T diagram of the representative days of each cluster was drawn and interpreted in the RAOB software environment. To select representative stations for the northwest region of Iran, 15% (3 synoptic stations) of the stations in the study area were selected based on altitude (meters), climate (number of heavy precipitation and average heavy precipitation during the study period), and large distance from each other (based on kilometers and geographical location). Using cluster analysis in the SPSS software environment, clusters were extracted based on the effective variables (relative humidity, wind vector, precipitable water) of the mid-level atmosphere in the northwest region of Iran. Then, the representative of each cluster was determined and for each representative day of heavy precipitation event (4 days out of 43 heavy precipitation events), in each of the 3 representative stations of the study area (3 stations out of 23 synoptic stations), the path and source of moisture of heavy precipitation were traced using the backward method (72 hours before the days of heavy precipitation in northwest Iran) and using global data analyzed at the  National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) with a time step of 6 hours with a spatial resolution of 2.5 × 2.5 longitude and latitude for the levels of 850, 700 and 550 hectopascals, with the HYSPLIT web model. The wind gust diagram was drawn and interpreted using the WRPLOT software for the representative days at the representative stations of northwest Iran. The combined wind and precipitation diagram was drawn and interpreted hourly in the Excel software environment for the representative days at the representative stations of the study area. <strong> </strong> <strong>Results and Discussion</strong> According to the criteria for heavy precipitation in this study, 43 extreme precipitation events were identified in the observation period (1990-2019). Using hierarchical cluster analysis using the Ward method with Euclidean distance, 2 main clusters were extracted from the 43 extreme precipitation events. The first cluster shows heavy precipitation events with dynamic ascent in the study area, and the second cluster includes heavy precipitation events with convective ascent in the research area. Of the two clusters, the first cluster has a higher frequency and indicates the dominance of heavy precipitation with dynamic origin over heavy precipitation with thermodynamic nature in the study area during the period under study. By drawing the Skew-T diagram in the RAOB software environment for representative days of each cluster, the instability conditions on representative days indicated the intensification and stability of atmospheric instability at levels above 850 hectopascals for the representative dynamic cluster. In the representative day's Skew-T diagram, the thermodynamic instability cluster was observed up to a maximum level of 850 hectopascals. Calculations showed that, considering the instability indices and the Skew-T thermodynamic diagram, the role of the convection factor in heavy precipitation  in northwest Iran was low and the dynamic factor was the main reason for heavy precipitation. The results of the study based on the windrose diagram indicate that the prevailing winds causing heavy precipitation  events blew from the southwest and had an average speed of 3.5 m/s. The output of the HYSPLIT diagram also confirms the southwest direction of the study area for the moisture input path of extreme precipitation. Also, the results of the combined hourly wind speed and precipitation diagram showed that the maximum wind speed and maximum precipitation on heavy precipitation  days were at 12:00 GMT, equivalent to 15:30 local time, which indicates the strengthening of the effective dynamic system in the region at this hour. In other words, the cyclone located at this hour, with the convergence created, has brought maximum humidity to the region and, with its sharp ascent, has provided the cause of heavy precipitation  in northwest Iran. Based on the calculations, the average atmospheric variability of precipitable water, relative humidity, and wind speed in extreme precipitation events in northwest Iran has been 16 kg/m2, 68 percent, and 20 m/s, respectively. <strong> </strong> <strong>Conclusion</strong> Based on the research conducted in the northwest region of Iran, in the period 1990-2019 on heavy precipitation, the results showed that, considering the instability indices and the Skew-T thermodynamic diagram, the role of the convection factor in heavy precipitation was very low and the dynamic factor was the main reason for heavy precipitation. The results of the study based on the HYSPLIT model showed that the main path of moisture entry into the study area is the southwest and the main source of moisture supply for heavy precipitation is the Red Sea. The results of the study based on the windrose diagram indicate that the prevailing winds in heavy precipitation events blew from the southwest and their speed was 3.5 m/s on average. The combined hourly wind speed and precipitation diagram showed that the maximum wind speed on heavy precipitation days was at 12:00 GMT, equivalent to 15:30 local time, which indicates the strengthening of the effective dynamic system in the study area at this hour. Humans cannot eliminate weather hazards. Weather hazards are part of nature, and humans can only reduce the frequency and severity of these events. In the northwest of Iran, the best solution to deal with the risks caused by heavy precipitation  is to identify the causes of this event, such as the moisture sources that provide heavy precipitation , and to evaluate instability indicators that indicate the conditions for the formation of heavy precipitation. The next step is to inform the residents of the region, such as farmers, travelers, and others, about the occurrence of this event and warn them of the possibility of flooding. Insuring crops and residential houses, constructing residential houses in susceptible areas on high foundations with a height of 3 or 4 meters, increasing vegetation cover and planting seedlings with the aim of increasing soil permeability, dredging rivers to prevent water levels from rising due to sediment deposition, taking protective measures on river banks with the aim of reducing soil erosion in coastal areas, using mobile concrete dams during precipitation  in agricultural and residential areas with the aim of preventing possible flood damage during heavy precipitation , and avoiding unnecessary transportation due to reduced visibility, slipperiness, and flooding of urban and roadways are considered major solutions to reduce losses caused by heavy precipitation. The results of this study are in good agreement with the results of other researchers in terms of the dominance of dynamic instability in heavy rainfall, the occurrence of heavy rainfall in the spring due to convective causes, the occurrence of extreme rainfall due to the supply of moisture to the Red Sea by the Mediterranean cyclone, and the confirmation of the strengthening of cyclones causing heavy rainfall at 12:00 GMT. <strong>Introduction</strong> The study of climate hazards such as heavy precipitation is very important due to its direct impact on flooding. Due to the climate change that the world has experienced, climate hazards have increased. What is certain is that humans cannot prevent the occurrence of climate hazards, but by being aware of these events in advance, under the influence of climate forecasts, they can reduce the destructive consequences of these hazards. Also, considering the very prominent role of humans in increasing the most important climate forcing, namely greenhouse gases, especially carbon dioxide, by managing fossil fuels and increasing new energy power plants, which are known as clean energies, climate changes that cause extreme events can be reduced. Another issue is the management of heavy precipitation to control large amounts of water for use in agriculture, which seems to be able to benefit from this weather event by taking measures. Another important point regarding heavy precipitation in the northwest region of Iran is to pay attention to the construction of residential areas in places far from rivers, which are vulnerable to flooding caused by heavy precipitation. The most important cause of extreme events such as heavy precipitation is currently climate change. The main factor causing climate change and desertification is greenhouse gases. The most important type of greenhouse gas is carbon dioxide. The main reason for the increase in this gas, which has a long life and is very poorly degradable, is humans. In other words, the main cause of the increase and intensification of extreme events is human misbehavior in dealing with nature. The northwest region of Iran is prone to heavy precipitation due to its mountainous topography and location on the main path of Mediterranean cyclones. This research was conducted with the aim of identifying the moisture sources of heavy precipitation in northwest Iran and also analyzing the instability indicators related to it. <strong> </strong> <strong>Materials and Methods</strong> The study area in this study is northwest Iran, including West Azarbaijan, East Azarbaijan, Ardabil, North Kurdistan, and West Zanjan provinces. In this study, daily and hourly (3-hour) precipitation data and hourly (3-hour) wind data (speed and direction) were obtained from the Iranian Meteorological Organization (www.irimo.ir) for 23 synoptic stations located in northwest Iran during the period 1990-2019. The upper atmosphere data of the Tabriz station (the only upper atmosphere station in northwest Iran) were obtained from the University of Wyoming website (http://weather.uwyo.edu/upperair/sounding.html). The upper atmosphere data of this study were obtained from the NCEP/NCAR database (www.cdc.noaa.gov). Trial and error estimates showed that if the percentile is higher than 99 and the area covered by heavy precipitation is more than 30%, synoptic conditions will provide a good justification for heavy precipitation. In this paper, days when at least 7 stations in the study area simultaneously had at least 20 mm of precipitation were selected. In this study, using TTI, CAPE, KI, LI, SI and SWEAT indices, the state of atmospheric instability in northwest Iran was evaluated at a representative station in the region (Tabriz) on days of heavy precipitation (43 days). Based on factor analysis in the SPSS software environment, the main factors were identified from among the 6 indicators, then using cluster analysis, the main clusters were extracted and the Skew-T diagram of the representative days of each cluster was drawn and interpreted in the RAOB software environment. To select representative stations for the northwest region of Iran, 15% (3 synoptic stations) of the stations in the study area were selected based on altitude (meters), climate (number of heavy precipitation and average heavy precipitation during the study period), and large distance from each other (based on kilometers and geographical location). Using cluster analysis in the SPSS software environment, clusters were extracted based on the effective variables (relative humidity, wind vector, precipitable water) of the mid-level atmosphere in the northwest region of Iran. Then, the representative of each cluster was determined and for each representative day of heavy precipitation event (4 days out of 43 heavy precipitation events), in each of the 3 representative stations of the study area (3 stations out of 23 synoptic stations), the path and source of moisture of heavy precipitation were traced using the backward method (72 hours before the days of heavy precipitation in northwest Iran) and using global data analyzed at the  National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) with a time step of 6 hours with a spatial resolution of 2.5 × 2.5 longitude and latitude for the levels of 850, 700 and 550 hectopascals, with the HYSPLIT web model. The wind gust diagram was drawn and interpreted using the WRPLOT software for the representative days at the representative stations of northwest Iran. The combined wind and precipitation diagram was drawn and interpreted hourly in the Excel software environment for the representative days at the representative stations of the study area. <strong> </strong> <strong>Results and Discussion</strong> According to the criteria for heavy precipitation in this study, 43 extreme precipitation events were identified in the observation period (1990-2019). Using hierarchical cluster analysis using the Ward method with Euclidean distance, 2 main clusters were extracted from the 43 extreme precipitation events. The first cluster shows heavy precipitation events with dynamic ascent in the study area, and the second cluster includes heavy precipitation events with convective ascent in the research area. Of the two clusters, the first cluster has a higher frequency and indicates the dominance of heavy precipitation with dynamic origin over heavy precipitation with thermodynamic nature in the study area during the period under study. By drawing the Skew-T diagram in the RAOB software environment for representative days of each cluster, the instability conditions on representative days indicated the intensification and stability of atmospheric instability at levels above 850 hectopascals for the representative dynamic cluster. In the representative day's Skew-T diagram, the thermodynamic instability cluster was observed up to a maximum level of 850 hectopascals. Calculations showed that, considering the instability indices and the Skew-T thermodynamic diagram, the role of the convection factor in heavy precipitation  in northwest Iran was low and the dynamic factor was the main reason for heavy precipitation. The results of the study based on the windrose diagram indicate that the prevailing winds causing heavy precipitation  events blew from the southwest and had an average speed of 3.5 m/s. The output of the HYSPLIT diagram also confirms the southwest direction of the study area for the moisture input path of extreme precipitation. Also, the results of the combined hourly wind speed and precipitation diagram showed that the maximum wind speed and maximum precipitation on heavy precipitation  days were at 12:00 GMT, equivalent to 15:30 local time, which indicates the strengthening of the effective dynamic system in the region at this hour. In other words, the cyclone located at this hour, with the convergence created, has brought maximum humidity to the region and, with its sharp ascent, has provided the cause of heavy precipitation  in northwest Iran. Based on the calculations, the average atmospheric variability of precipitable water, relative humidity, and wind speed in extreme precipitation events in northwest Iran has been 16 kg/m2, 68 percent, and 20 m/s, respectively. <strong> </strong> <strong>Conclusion</strong> Based on the research conducted in the northwest region of Iran, in the period 1990-2019 on heavy precipitation, the results showed that, considering the instability indices and the Skew-T thermodynamic diagram, the role of the convection factor in heavy precipitation was very low and the dynamic factor was the main reason for heavy precipitation. The results of the study based on the HYSPLIT model showed that the main path of moisture entry into the study area is the southwest and the main source of moisture supply for heavy precipitation is the Red Sea. The results of the study based on the windrose diagram indicate that the prevailing winds in heavy precipitation events blew from the southwest and their speed was 3.5 m/s on average. The combined hourly wind speed and precipitation diagram showed that the maximum wind speed on heavy precipitation days was at 12:00 GMT, equivalent to 15:30 local time, which indicates the strengthening of the effective dynamic system in the study area at this hour. Humans cannot eliminate weather hazards. Weather hazards are part of nature, and humans can only reduce the frequency and severity of these events. In the northwest of Iran, the best solution to deal with the risks caused by heavy precipitation  is to identify the causes of this event, such as the moisture sources that provide heavy precipitation , and to evaluate instability indicators that indicate the conditions for the formation of heavy precipitation. The next step is to inform the residents of the region, such as farmers, travelers, and others, about the occurrence of this event and warn them of the possibility of flooding. Insuring crops and residential houses, constructing residential houses in susceptible areas on high foundations with a height of 3 or 4 meters, increasing vegetation cover and planting seedlings with the aim of increasing soil permeability, dredging rivers to prevent water levels from rising due to sediment deposition, taking protective measures on river banks with the aim of reducing soil erosion in coastal areas, using mobile concrete dams during precipitation  in agricultural and residential areas with the aim of preventing possible flood damage during heavy precipitation , and avoiding unnecessary transportation due to reduced visibility, slipperiness, and flooding of urban and roadways are considered major solutions to reduce losses caused by heavy precipitation. The results of this study are in good agreement with the results of other researchers in terms of the dominance of dynamic instability in heavy rainfall, the occurrence of heavy rainfall in the spring due to convective causes, the occurrence of extreme rainfall due to the supply of moisture to the Red Sea by the Mediterranean cyclone, and the confirmation of the strengthening of cyclones causing heavy rainfall at 12:00 GMT. https://esrj.sbu.ac.ir/article_105224_66c3fc5cce73d075260679880ee17680.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2026 02 20 Landslide hazard zoning using random forest and support vector machine models (Case study: Talar basin) Landslide hazard zoning using random forest and support vector machine models (Case study: Talar basin) 152 168 105349 10.48308/esrj.2025.236839.1234 FA Negar Babarbi Department of Geography, Faculty of Humanities and Social Sciences, University of Mazandaran, Babolsar, Iran Ghasem Lorestani Department of Geography, Faculty of Humanities and Social Sciences, University of Mazandaran, Babolsar, Iran 0000-0003-0222-7906 Reza Esmaili Department of Geography, Faculty of Humanities and Social Sciences, University of Mazandaran, Babolsar, Iran Journal Article 2024 09 08 <strong>Introduction</strong> Landslide is a geomorphological phenomenon with high potential for human and financial losses that occurs due to the sudden and rapid movement of soil, rock, and other materials on low to medium slopes down the slope. This phenomenon is considered one of the most destructive natural disasters in steep areas. Given the importance and sensitivity of the subject, this article is dedicated to landslide risk zoning in the Talar Basin. Studies conducted in countries with similar climatic conditions, global experiences, and perspectives on landslide risk zoning can best help in planning for management and damage reduction. Nearly 70 percent of Iran's land area is above 1000 meters in altitude (Zomordian, 2004) and 34 percent has a slope of more than 10 percent (Jedari Eyvazi, 1995). This indicates that Iran is a mountainous country and prone to slope movements. Active neotectonics, together with young Tertiary geological formations and climatic conditions, provide the basis for facilitating slope movements, especially landslides. Meanwhile, the Talar watershed is also prone to landslides due to its foothill and mountainous conditions in the central and southern parts of the basin. The high altitudes of the central Alborz and deep valleys are the main features of the ruggedness of this region. The vegetation of the region includes Hyrcanian forests, mountain pastures, and agricultural lands. The climate of the Talar basin is temperate and humid. Annual precipitation in this region varies on average between 200 - 1000 mm, and most precipitation occurs in the autumn and winter seasons. Due to its abundant rainfall and specific topography, this region is prone to floods and landslides. The Talar watershed has significant geological diversity. Limestone and schist are among the dominant rocks in this region. Due to the presence of active faults and complex geological structures, the potential of the region for landslides is high. Land use changes, deforestation, water pollution, and Mass movements are among the main problems of this region. Conservation and management measures are necessary to reduce the negative effects of these problems. Given the diversity and complexity of the natural, climatic, and human factors of this region, accurate knowledge of environmental characteristics can help in better planning and management of natural and environmental resources. The aim of the present study is to use past landslide data and investigate the factors affecting the occurrence of this geomorphological phenomenon, to prepare a landslide zoning map using random forest and support vector methods, and to further evaluate the accuracy and efficiency of the aforementioned models in predicting and identifying landslide-prone areas using the ROC curve. Unfortunately, due to the high sensitivity of the formations of the northern Alborz range and the tendency of non-native immigrants to settle and spend their leisure time in the mountainous and summer slopes overlooking the plain in the Talar basin, conditions have been created for drastic changes in land use, which in the event of a landslide will cause high and irreparable losses in terms of life and property. Therefore, investigating and identifying landslide hazard zones can be effective in raising awareness and controlling landforms in the face of slope processes in the Talar basin. On the other hand, the aforementioned models have been used separately or in combination with other models in landslide hazard zoning, but a comparison of the efficiency of the two aforementioned models to identify the best model in an area with semi-humid environmental conditions in the north of the country has not been conducted. <strong> </strong> <strong>Materials and Methods</strong> The Talar watershed is one of the ten largest basins in the central part of Mazandaran Province, with an area of ​​3227.4 square kilometers, located in Mazandaran Province and south of the city of Qaemshahr.  The minimum and maximum elevation of this basin is between -26 and 4002 meters above sea level. The average elevation of the basin is 767 meters. The ruggedness units of the basin can be divided into three classes: plain (up to 200 meters), hills (200-500 meters), and mountains (more than 500). Since various factors are effective in landslide occurrence, in this study, natural factors and variables such as topographic factors including altitude, slope, slope direction, topographic moisture index, profile and plan curvature, along with distance from rivers, distance from faults, lithology, precipitation, normalized difference vegetation index (NDVI) and human factors such as distance from road and land use have been used. To produce the altitude, slope, slope direction, topographic moisture index, profile curvature, plan curvature and distance from rivers layers, the United States Geological Survey (USGS) digital elevation model with 30-meter pixel size was used. To produce the distance from faults and lithology layers, the 1/100,000 geological map of the Geological Survey of Iran was used. To produce the precipitation contour layer, data from rain gauge stations in the Talar basin were used. To prepare the vegetation layer (NDVI), the vegetation index was used, and to prepare the land use layer, the Landsat OLI satellite images were used with the supervised maximum likelihood classification method in Envi5.6 software. Using the Global Positioning System (GPS), the distribution layer of landslide occurrence points was prepared. To do this, first, by field surveying, the location of landslides that were physically accessible was recorded by GPS. Of course, due to the topographic conditions of the study area and due to their impassability and inaccessibility, Google Earth software was used to identify some landslide points, and finally, 61 landslide points were collected and recorded in the study area. 70% of the points (43 points) were randomly selected as training points and 30% of the points (18 points) were randomly selected for model validation. Training data was used for modeling and validation data was used for accuracy. Two methods, random forest and support vector machine, were used for landslide zoning and modeling. <strong> </strong> <strong>Results and Discussion</strong> In the study of natural and human variables, it was determined that 56% of landslides occurred at altitudes between 436 and 1126 meters. More than 78% of landslides are observed on slopes above 25 degrees. 49% of the landslides occurred in the east, northeast and 38% in the south and southwest directions. 75% of landslides occurred on shale, sandstone, conglomerate, and marl rocks in TRJs and Mmsl formations. Slopes with low to medium curvature with a frequency of 50% show the highest overlap with landslides. In terms of soil topographic moisture index, 80% of landslides are in values ​​above 1.6 to 8 indices. About 64% of landslides are located within 0 to 1.3 km of rivers, and 51% of landslides in the study area occur within 0 to 600 m of roads. About 78% of landslides are located within 2.5 km of faults. Approximately 50 and 45% of recorded landslides occur in pasture and forest areas in the study basin, respectively. Due to the high rainfall in the entire basin, landslide hotspots are visible, but in terms of landslide frequency, 30% of landslides occur in areas with rainfall greater than 581 mm. According to the zoning results and the information received, in the RF model, the highest risk class belongs to very low risk with a frequency of 54%, and the high risk class in this model is 18.4%. However, in the SVM model, the very low class shows the highest spread with a frequency of 40.6%, and the very high risk class accounts for 11% of the entire basin. 70% of the 61 landslide occurrence points were randomly selected for model training and 30% for model validation to evaluate the accuracy of the model with data that were not used in the training process. The evaluation results in the study area showed that the area under the curve in the SVM and RF models is 12.87 and 27.85, respectively. According to the classification provided for the area under the curve (excellent 0.9-1, very good 0.8-0.9, good 0.7-0.8, moderate 0.6-0.7, and poor 0.5-0.6), both models have very good accuracy, but in comparison, it can be said that the landslide zoning obtained from the support vector machine model in the study area has a higher level of accuracy. It seems that the slight difference in the validity of the models under study is related to the difference in the number and weighting of the criteria, as well as the difference in the climatic conditions of the study basins. <strong>Conclusion</strong> The studied area with 61 landslide zones is a very sensitive area to landslides, which has increased significantly due to increasing human activities. To evaluate landslide-prone areas, 12 factors affecting landslide occurrence (elevation, slope, slope direction, precipitation, distance from the river, distance from the fault, distance from the road, vegetation index, soil topographic moisture, curvature index, lithology and land use) were used. The overlap of landslide points and layers of effective factors showed that the lower parts of the basin due to the gentle slope and low altitude, the type of constituent rocks, are less sensitive to landslide occurrence, and in contrast, the middle and upstream parts of the Talar basin due to foothill and mountainous conditions, with high altitude and slope and sensitive formations, indicate much more favorable conditions for landslide occurrence. The results of the random forest model indicate very high and high risk areas as 18.4 and 14.9 percent, respectively. While the results of the support vector machine model indicate Listed hazard classes areas as 11 and 16.7 percent of the area, respectively. In the Talar basin, the risk classes mainly coincide in the southern and central highlands with steep slopes and on the edges of rivers and roads. Comparison of the aforementioned models showed that both models have high efficiency in landslide occurrence zoning, but the results of ROC curve evaluation showed that the area under the curve obtained for the support vector machine and random forest models are 87.1 and 85.3, respectively. According to the classification provided for the area under the curve, the support vector machine model has a higher accuracy in landslide susceptibility zoning in the study area. According to the results obtained from the two models, on average more than 40 percent of the basin is at medium to very high risk of landslide occurrence, and the need for planning for basin management and paying more attention to this phenomenon seems essential. <strong>Introduction</strong> Landslide is a geomorphological phenomenon with high potential for human and financial losses that occurs due to the sudden and rapid movement of soil, rock, and other materials on low to medium slopes down the slope. This phenomenon is considered one of the most destructive natural disasters in steep areas. Given the importance and sensitivity of the subject, this article is dedicated to landslide risk zoning in the Talar Basin. Studies conducted in countries with similar climatic conditions, global experiences, and perspectives on landslide risk zoning can best help in planning for management and damage reduction. Nearly 70 percent of Iran's land area is above 1000 meters in altitude (Zomordian, 2004) and 34 percent has a slope of more than 10 percent (Jedari Eyvazi, 1995). This indicates that Iran is a mountainous country and prone to slope movements. Active neotectonics, together with young Tertiary geological formations and climatic conditions, provide the basis for facilitating slope movements, especially landslides. Meanwhile, the Talar watershed is also prone to landslides due to its foothill and mountainous conditions in the central and southern parts of the basin. The high altitudes of the central Alborz and deep valleys are the main features of the ruggedness of this region. The vegetation of the region includes Hyrcanian forests, mountain pastures, and agricultural lands. The climate of the Talar basin is temperate and humid. Annual precipitation in this region varies on average between 200 - 1000 mm, and most precipitation occurs in the autumn and winter seasons. Due to its abundant rainfall and specific topography, this region is prone to floods and landslides. The Talar watershed has significant geological diversity. Limestone and schist are among the dominant rocks in this region. Due to the presence of active faults and complex geological structures, the potential of the region for landslides is high. Land use changes, deforestation, water pollution, and Mass movements are among the main problems of this region. Conservation and management measures are necessary to reduce the negative effects of these problems. Given the diversity and complexity of the natural, climatic, and human factors of this region, accurate knowledge of environmental characteristics can help in better planning and management of natural and environmental resources. The aim of the present study is to use past landslide data and investigate the factors affecting the occurrence of this geomorphological phenomenon, to prepare a landslide zoning map using random forest and support vector methods, and to further evaluate the accuracy and efficiency of the aforementioned models in predicting and identifying landslide-prone areas using the ROC curve. Unfortunately, due to the high sensitivity of the formations of the northern Alborz range and the tendency of non-native immigrants to settle and spend their leisure time in the mountainous and summer slopes overlooking the plain in the Talar basin, conditions have been created for drastic changes in land use, which in the event of a landslide will cause high and irreparable losses in terms of life and property. Therefore, investigating and identifying landslide hazard zones can be effective in raising awareness and controlling landforms in the face of slope processes in the Talar basin. On the other hand, the aforementioned models have been used separately or in combination with other models in landslide hazard zoning, but a comparison of the efficiency of the two aforementioned models to identify the best model in an area with semi-humid environmental conditions in the north of the country has not been conducted. <strong> </strong> <strong>Materials and Methods</strong> The Talar watershed is one of the ten largest basins in the central part of Mazandaran Province, with an area of ​​3227.4 square kilometers, located in Mazandaran Province and south of the city of Qaemshahr.  The minimum and maximum elevation of this basin is between -26 and 4002 meters above sea level. The average elevation of the basin is 767 meters. The ruggedness units of the basin can be divided into three classes: plain (up to 200 meters), hills (200-500 meters), and mountains (more than 500). Since various factors are effective in landslide occurrence, in this study, natural factors and variables such as topographic factors including altitude, slope, slope direction, topographic moisture index, profile and plan curvature, along with distance from rivers, distance from faults, lithology, precipitation, normalized difference vegetation index (NDVI) and human factors such as distance from road and land use have been used. To produce the altitude, slope, slope direction, topographic moisture index, profile curvature, plan curvature and distance from rivers layers, the United States Geological Survey (USGS) digital elevation model with 30-meter pixel size was used. To produce the distance from faults and lithology layers, the 1/100,000 geological map of the Geological Survey of Iran was used. To produce the precipitation contour layer, data from rain gauge stations in the Talar basin were used. To prepare the vegetation layer (NDVI), the vegetation index was used, and to prepare the land use layer, the Landsat OLI satellite images were used with the supervised maximum likelihood classification method in Envi5.6 software. Using the Global Positioning System (GPS), the distribution layer of landslide occurrence points was prepared. To do this, first, by field surveying, the location of landslides that were physically accessible was recorded by GPS. Of course, due to the topographic conditions of the study area and due to their impassability and inaccessibility, Google Earth software was used to identify some landslide points, and finally, 61 landslide points were collected and recorded in the study area. 70% of the points (43 points) were randomly selected as training points and 30% of the points (18 points) were randomly selected for model validation. Training data was used for modeling and validation data was used for accuracy. Two methods, random forest and support vector machine, were used for landslide zoning and modeling. <strong> </strong> <strong>Results and Discussion</strong> In the study of natural and human variables, it was determined that 56% of landslides occurred at altitudes between 436 and 1126 meters. More than 78% of landslides are observed on slopes above 25 degrees. 49% of the landslides occurred in the east, northeast and 38% in the south and southwest directions. 75% of landslides occurred on shale, sandstone, conglomerate, and marl rocks in TRJs and Mmsl formations. Slopes with low to medium curvature with a frequency of 50% show the highest overlap with landslides. In terms of soil topographic moisture index, 80% of landslides are in values ​​above 1.6 to 8 indices. About 64% of landslides are located within 0 to 1.3 km of rivers, and 51% of landslides in the study area occur within 0 to 600 m of roads. About 78% of landslides are located within 2.5 km of faults. Approximately 50 and 45% of recorded landslides occur in pasture and forest areas in the study basin, respectively. Due to the high rainfall in the entire basin, landslide hotspots are visible, but in terms of landslide frequency, 30% of landslides occur in areas with rainfall greater than 581 mm. According to the zoning results and the information received, in the RF model, the highest risk class belongs to very low risk with a frequency of 54%, and the high risk class in this model is 18.4%. However, in the SVM model, the very low class shows the highest spread with a frequency of 40.6%, and the very high risk class accounts for 11% of the entire basin. 70% of the 61 landslide occurrence points were randomly selected for model training and 30% for model validation to evaluate the accuracy of the model with data that were not used in the training process. The evaluation results in the study area showed that the area under the curve in the SVM and RF models is 12.87 and 27.85, respectively. According to the classification provided for the area under the curve (excellent 0.9-1, very good 0.8-0.9, good 0.7-0.8, moderate 0.6-0.7, and poor 0.5-0.6), both models have very good accuracy, but in comparison, it can be said that the landslide zoning obtained from the support vector machine model in the study area has a higher level of accuracy. It seems that the slight difference in the validity of the models under study is related to the difference in the number and weighting of the criteria, as well as the difference in the climatic conditions of the study basins. <strong>Conclusion</strong> The studied area with 61 landslide zones is a very sensitive area to landslides, which has increased significantly due to increasing human activities. To evaluate landslide-prone areas, 12 factors affecting landslide occurrence (elevation, slope, slope direction, precipitation, distance from the river, distance from the fault, distance from the road, vegetation index, soil topographic moisture, curvature index, lithology and land use) were used. The overlap of landslide points and layers of effective factors showed that the lower parts of the basin due to the gentle slope and low altitude, the type of constituent rocks, are less sensitive to landslide occurrence, and in contrast, the middle and upstream parts of the Talar basin due to foothill and mountainous conditions, with high altitude and slope and sensitive formations, indicate much more favorable conditions for landslide occurrence. The results of the random forest model indicate very high and high risk areas as 18.4 and 14.9 percent, respectively. While the results of the support vector machine model indicate Listed hazard classes areas as 11 and 16.7 percent of the area, respectively. In the Talar basin, the risk classes mainly coincide in the southern and central highlands with steep slopes and on the edges of rivers and roads. Comparison of the aforementioned models showed that both models have high efficiency in landslide occurrence zoning, but the results of ROC curve evaluation showed that the area under the curve obtained for the support vector machine and random forest models are 87.1 and 85.3, respectively. According to the classification provided for the area under the curve, the support vector machine model has a higher accuracy in landslide susceptibility zoning in the study area. According to the results obtained from the two models, on average more than 40 percent of the basin is at medium to very high risk of landslide occurrence, and the need for planning for basin management and paying more attention to this phenomenon seems essential. https://esrj.sbu.ac.ir/article_105349_93d3ec57424e77d53d157a25cac11d8b.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2025 02 19 Geochemical explorations and introduction of stratabound copper in Yeylagh Samanloo area, west of Sabalan, NW Iran Geochemical explorations and introduction of stratabound copper in Yeylagh Samanloo area, west of Sabalan, NW Iran 169 188 105350 10.48308/esrj.2025.105350 FA Hadi Mohammadian Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran 0009-0000-6882-1110 Vartan Simmonds Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran Kamal Siahcheshm Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran 0000-0001-5728-1374 Journal Article 2023 05 26 <strong>Introduction</strong> The Yeilaq Samanloo area is located 19 km southwest of Meshginshahr and 22 km west of Sablan in the West Alborz-Azarbaijan structural zone. Cenozoic igneous-pyroclastic rocks cover more than 95% of the area. The Eocene units are mainly composed of volcanic rocks, including andesite, trachy-andesite to trachy-basalt, tuff and shale layers. A granitoid intrusive body (granodiorite, monzonite, quartz monzonite) with Upper Oligocene age intruded the Eocene volcanic rocks and produced chlorite and epidote alteration in them, especially in the contact zone. A number of silica veins containing pyrite and chalcopyrite cross-cut both the granodiorite body and Eocene volcanic rocks, which host gold and copper mineralization. The youngest unit includes Sablan lavas of trachy-andesite, basaltic andesite and andesite with Quaternary age, which have flowed unconformably on the Eocene volcanic rocks. <strong> </strong> <strong>Materials and Methods</strong> In this research, 65 samples were taken from stream sediments for geochemical studies. In order to check the anomalies revealed from stream sediment studies, 30 rock samples were taken for lithogeochemical studies and 10 petrological samples from the igneous rocks and were analyzed by XRF and ICP-MS (petrological samples), ICP-OES (geochemical samples of stream sediments) and Fire Assay (for gold) at  Zarazma lab. <strong> </strong> <strong>Results and Discussion</strong> Based on the petrological diagrams, the volcanic rocks of the region mainly have andesitic to andesi-basaltic composition, high potassium calc-alkaline and shoshonitic nature, and meta-aluminous to per-aluminous affinity. The tectonic setting of these rocks is an active continental margin, and their trace and RE elements pattern is similar to the subduction-related rocks. Remote sensing and field studies show that the distribution of various alteration zones is not extensive. The chlorite-epidote (propylitic) alteration zone is the most widespread zone, mainly observed in the northeast and southeast of the area. Argillic and sericitic alterations are present in the central and southwestern parts, and the distribution of alunite-pyrophyllite alteration is scattered and very limited. Stream-sediment geochemical studies and lithogeochemical investigations upstream the observed anomalies led to the introduction of several Cu-Ag and precious and base metal mineralization areas for the first time in this region. Coincidence of geochemical anomalies with alteration zones shows that Cu anomalies are mostly associated with argillic and sericitic. The association of Au with argillic and sericitic alteration zones in the south of the area is noteworthy. But Ag mineralization is associated only with propylitic and to some extent, argillic alteration. Microscopic studies of rock samples showed that the Cu-Ag mineralization in the Samanloo area is stratabound, being associated with andesitic units of the Upper Eocene and includes pyrite, chalcopyrite, bornite, malachite, azurite, chalcocite, native copper and to a lesser extent, covellite, which occur as disseminations, open space fillings and replacements, especially within the mega-porphyritic andesite unit. In the rock samples of this area, the highest anomalies of elements are: Cu (67800 ppm), Ag (18 ppm) and Au (1088 ppb). Based on the obtained statistical correlations, the anomalous elements were divided into three groups: (1) Cu-Ag, (2) As-Sb-S-Au and (3) Pb-Zn-Fe, which are attributed to three genetic-lithologic groups. The first group is related to the granitoid body, especially the halo around it. The second group is directly related to pyroclastic units and Eocene lavas, especially mega-porphyritic andesites and chlorite-argillic alteration zone within it. The third group can be attributed to the silicic veins/veinlets of the Neogene tectono-magmatic activities and the infiltration of hydrothermal fluids into fractures; Au displays more considerable anomaly among the elements of this group. <strong> </strong> <strong>Conclusion</strong> Based on the characteristics of mineralization, including host rocks, stratabound nature, mineralogy, metal content and alteration, it can be concluded that the mineralization at Yeilagh Samanloo area is of Manto-type copper deposits. According to the structure, texture and mineralogy of the mega-porphyritic andesite unit, two phases can be considered for the hypogene mineralization at the Samanloo area: primary diagenetic stage and burial stage. Early diagenetic processes led to the formation of pyrite within the porphyritic andesite unit, which is the host of mineralization, and as a result, reducing conditions have appeared in this unit. In the next stage, under the influence of the burial process, oxidant saline fluids have migrated and washed Cu from the underlying Cu-rich volcanic units (trachy-andesite, tuff with shale layers) and deposited it in the reducing mega-porphyritic andesites. <strong>Introduction</strong> The Yeilaq Samanloo area is located 19 km southwest of Meshginshahr and 22 km west of Sablan in the West Alborz-Azarbaijan structural zone. Cenozoic igneous-pyroclastic rocks cover more than 95% of the area. The Eocene units are mainly composed of volcanic rocks, including andesite, trachy-andesite to trachy-basalt, tuff and shale layers. A granitoid intrusive body (granodiorite, monzonite, quartz monzonite) with Upper Oligocene age intruded the Eocene volcanic rocks and produced chlorite and epidote alteration in them, especially in the contact zone. A number of silica veins containing pyrite and chalcopyrite cross-cut both the granodiorite body and Eocene volcanic rocks, which host gold and copper mineralization. The youngest unit includes Sablan lavas of trachy-andesite, basaltic andesite and andesite with Quaternary age, which have flowed unconformably on the Eocene volcanic rocks. <strong> </strong> <strong>Materials and Methods</strong> In this research, 65 samples were taken from stream sediments for geochemical studies. In order to check the anomalies revealed from stream sediment studies, 30 rock samples were taken for lithogeochemical studies and 10 petrological samples from the igneous rocks and were analyzed by XRF and ICP-MS (petrological samples), ICP-OES (geochemical samples of stream sediments) and Fire Assay (for gold) at  Zarazma lab. <strong> </strong> <strong>Results and Discussion</strong> Based on the petrological diagrams, the volcanic rocks of the region mainly have andesitic to andesi-basaltic composition, high potassium calc-alkaline and shoshonitic nature, and meta-aluminous to per-aluminous affinity. The tectonic setting of these rocks is an active continental margin, and their trace and RE elements pattern is similar to the subduction-related rocks. Remote sensing and field studies show that the distribution of various alteration zones is not extensive. The chlorite-epidote (propylitic) alteration zone is the most widespread zone, mainly observed in the northeast and southeast of the area. Argillic and sericitic alterations are present in the central and southwestern parts, and the distribution of alunite-pyrophyllite alteration is scattered and very limited. Stream-sediment geochemical studies and lithogeochemical investigations upstream the observed anomalies led to the introduction of several Cu-Ag and precious and base metal mineralization areas for the first time in this region. Coincidence of geochemical anomalies with alteration zones shows that Cu anomalies are mostly associated with argillic and sericitic. The association of Au with argillic and sericitic alteration zones in the south of the area is noteworthy. But Ag mineralization is associated only with propylitic and to some extent, argillic alteration. Microscopic studies of rock samples showed that the Cu-Ag mineralization in the Samanloo area is stratabound, being associated with andesitic units of the Upper Eocene and includes pyrite, chalcopyrite, bornite, malachite, azurite, chalcocite, native copper and to a lesser extent, covellite, which occur as disseminations, open space fillings and replacements, especially within the mega-porphyritic andesite unit. In the rock samples of this area, the highest anomalies of elements are: Cu (67800 ppm), Ag (18 ppm) and Au (1088 ppb). Based on the obtained statistical correlations, the anomalous elements were divided into three groups: (1) Cu-Ag, (2) As-Sb-S-Au and (3) Pb-Zn-Fe, which are attributed to three genetic-lithologic groups. The first group is related to the granitoid body, especially the halo around it. The second group is directly related to pyroclastic units and Eocene lavas, especially mega-porphyritic andesites and chlorite-argillic alteration zone within it. The third group can be attributed to the silicic veins/veinlets of the Neogene tectono-magmatic activities and the infiltration of hydrothermal fluids into fractures; Au displays more considerable anomaly among the elements of this group. <strong> </strong> <strong>Conclusion</strong> Based on the characteristics of mineralization, including host rocks, stratabound nature, mineralogy, metal content and alteration, it can be concluded that the mineralization at Yeilagh Samanloo area is of Manto-type copper deposits. According to the structure, texture and mineralogy of the mega-porphyritic andesite unit, two phases can be considered for the hypogene mineralization at the Samanloo area: primary diagenetic stage and burial stage. Early diagenetic processes led to the formation of pyrite within the porphyritic andesite unit, which is the host of mineralization, and as a result, reducing conditions have appeared in this unit. In the next stage, under the influence of the burial process, oxidant saline fluids have migrated and washed Cu from the underlying Cu-rich volcanic units (trachy-andesite, tuff with shale layers) and deposited it in the reducing mega-porphyritic andesites. https://esrj.sbu.ac.ir/article_105350_2fc8b744533b6f33ec20f19a80a5d2d2.pdf

Shahid Beheshti University Researches in Earth Sciences 2008-8299 16 1 2025 02 19 Political geopolitical analysis of the linkage between climate change, migration, and social insecurity in Khuzestan province Political geopolitical analysis of the linkage between climate change, migration, and social insecurity in Khuzestan province 189 203 105348 10.48308/esrj.2025.237098.1237 FA Mojtaba Naderi Department of Geography, Garmsar Branch, Islamic Azad University, Garmsar, Iran 0009-0003-9022-4867 Davoud HasanAbadi Department of Geography, Garmsar Branch, Islamic Azad University, Garmsar, Iran 0009-0003-9022-4867 Azam Yousefi 2Department of Geography, Science and Research Branch, Islamic Azad University, Tehran, Iran Majid Vali Shariat Panahi 3Department of Geography, Yadegar-e-Imam Branch, Islamic Azad University, Shahr-e-Rey, Iran Journal Article 2024 10 01 <strong>Introduction</strong> The issue of security is one of the important and fundamental concerns of governments in the national space. Without security, no progress or development can be achieved in a country. Among the regions of the country, the southwest, particularly Khuzestan province, is significantly affected by security-related issues in various forms. This topic has impacted the spatial development of this part of the country both as a primary and secondary factor. This paper examines the perspectives on security and insecurity as secondary factors in the southwest region, particularly focusing on Khuzestan, and aims to answer the question: What are the most important security consequences of climate migrants on Khuzestan province? Climate change has emerged as a key topic in social, economic, and environmental research. According to international reports, the effects of climate change are such that millions of people worldwide are driven to migrate. Climate migrants or environmental refugees are individuals who are forced to leave their homes due to natural disasters or significant changes in their environment. Today, Khuzestan province is recognized as one of the most vulnerable areas in Iran, increasingly facing environmental crises due to escalating climate changes. This province is particularly challenged by issues such as drought, rising dust storms, and diminishing water resources. As a result, migration has become a serious challenge in this region. Thus, this research is designed to investigate the various dimensions of climate migrations and their impacts on economic and security issues in Khuzestan province. <strong> </strong> <strong>Materials and Methods</strong> This research was conducted in two phases. In the first phase, theoretical and conceptual data were collected through a review of existing literature and archival and library resources. This phase aimed to identify various aspects of the impacts of climate migrations and to gather primary data. In the second phase, an electronic questionnaire was used to collect field data, which included questions focused on the factors influencing migration and its impacts on local security and economy. The collected responses were analyzed using SPSS software, and to assess the reliability of the questionnaire, a Cronbach's alpha test was conducted, yielding a score of 0.750, indicating the reliability of the questionnaire. Additionally, the Friedman ranking test was used to analyze the data and extract results.   <strong>Results and Discussion</strong> The research findings related to the consequences of the dust crisis are categorized into five dimensions: social-cultural, economic, political-governance, environmental, and defense-security. To identify the most significant security consequences of each of these dimensions, the Friedman ranking test was utilized. The consequences of climate migrations in Khuzestan can be divided into several general categories: Social-Cultural Consequences: Increased Poverty: One of the most important issues arising from forced migration is the rise in poverty in communities that face a loss of workforce and business decline. Increase in Unemployment: The exodus of capable population results in labor shortages for employers, leading to unemployment in rural and disadvantaged areas .Increased Social Discontent: The gap between the government and the public, as well as heightened social protests, are significant consequences of the government's failure to respond to migrants and local residents. Economic Consequences :Decreased Agricultural Productivity: Agriculture, heavily dependent on water resources and vulnerable environmental conditions, significantly suffers from population displacement, leading to reduced productivity .Increased Cost of Living: Inability to meet basic life needs puts tremendous pressure on people's livelihoods .Disruption of Economic Activities: Forced migrations can lead to a decrease in investment levels in evacuated areas. Political-Governance Consequences: Increased Gap Between Government and Citizens: Neglecting the social and economic needs of migrants can lead to broader dissatisfaction within society .Evolving Ethnic Strains and Social Unrest: Population dislocation may exacerbate ethnic and cultural tensions, impacting peaceful coexistence. Geographical Consequences: Increase in Urban Marginality: Migration toward cities can lead to the emergence of slums and instability in urban areas, thereby creating further management challenges .Disruption of Ecological Balance: The outflow of labor from rural areas to cities can negatively affect the ecological balance of populations. Security-Defense Consequences: Increased Security Costs: Governments may feel compelled to raise security costs due to social unrest or issues arising from forced migrations, which puts pressure on public resources.Threats from Dissident Groups: The unique geographical and border conditions of Khuzestan make it susceptible to security threats, which could be aggravated by demographic changes. <strong> </strong> <strong>Conclusion</strong> Based on the findings of this research, it can be concluded that the impacts of climate migrations in Khuzestan not only result in economic problems but also affect social, political, geographical, and security dimensions. Therefore, policymakers must seriously address the management of conditions arising from climate change to prevent the consequences and impacts of climate migrations and to enhance community resilience these crises. <strong> </strong> <strong>Introduction</strong> The issue of security is one of the important and fundamental concerns of governments in the national space. Without security, no progress or development can be achieved in a country. Among the regions of the country, the southwest, particularly Khuzestan province, is significantly affected by security-related issues in various forms. This topic has impacted the spatial development of this part of the country both as a primary and secondary factor. This paper examines the perspectives on security and insecurity as secondary factors in the southwest region, particularly focusing on Khuzestan, and aims to answer the question: What are the most important security consequences of climate migrants on Khuzestan province? Climate change has emerged as a key topic in social, economic, and environmental research. According to international reports, the effects of climate change are such that millions of people worldwide are driven to migrate. Climate migrants or environmental refugees are individuals who are forced to leave their homes due to natural disasters or significant changes in their environment. Today, Khuzestan province is recognized as one of the most vulnerable areas in Iran, increasingly facing environmental crises due to escalating climate changes. This province is particularly challenged by issues such as drought, rising dust storms, and diminishing water resources. As a result, migration has become a serious challenge in this region. Thus, this research is designed to investigate the various dimensions of climate migrations and their impacts on economic and security issues in Khuzestan province. <strong> </strong> <strong>Materials and Methods</strong> This research was conducted in two phases. In the first phase, theoretical and conceptual data were collected through a review of existing literature and archival and library resources. This phase aimed to identify various aspects of the impacts of climate migrations and to gather primary data. In the second phase, an electronic questionnaire was used to collect field data, which included questions focused on the factors influencing migration and its impacts on local security and economy. The collected responses were analyzed using SPSS software, and to assess the reliability of the questionnaire, a Cronbach's alpha test was conducted, yielding a score of 0.750, indicating the reliability of the questionnaire. Additionally, the Friedman ranking test was used to analyze the data and extract results.   <strong>Results and Discussion</strong> The research findings related to the consequences of the dust crisis are categorized into five dimensions: social-cultural, economic, political-governance, environmental, and defense-security. To identify the most significant security consequences of each of these dimensions, the Friedman ranking test was utilized. The consequences of climate migrations in Khuzestan can be divided into several general categories: Social-Cultural Consequences: Increased Poverty: One of the most important issues arising from forced migration is the rise in poverty in communities that face a loss of workforce and business decline. Increase in Unemployment: The exodus of capable population results in labor shortages for employers, leading to unemployment in rural and disadvantaged areas .Increased Social Discontent: The gap between the government and the public, as well as heightened social protests, are significant consequences of the government's failure to respond to migrants and local residents. Economic Consequences :Decreased Agricultural Productivity: Agriculture, heavily dependent on water resources and vulnerable environmental conditions, significantly suffers from population displacement, leading to reduced productivity .Increased Cost of Living: Inability to meet basic life needs puts tremendous pressure on people's livelihoods .Disruption of Economic Activities: Forced migrations can lead to a decrease in investment levels in evacuated areas. Political-Governance Consequences: Increased Gap Between Government and Citizens: Neglecting the social and economic needs of migrants can lead to broader dissatisfaction within society .Evolving Ethnic Strains and Social Unrest: Population dislocation may exacerbate ethnic and cultural tensions, impacting peaceful coexistence. Geographical Consequences: Increase in Urban Marginality: Migration toward cities can lead to the emergence of slums and instability in urban areas, thereby creating further management challenges .Disruption of Ecological Balance: The outflow of labor from rural areas to cities can negatively affect the ecological balance of populations. Security-Defense Consequences: Increased Security Costs: Governments may feel compelled to raise security costs due to social unrest or issues arising from forced migrations, which puts pressure on public resources.Threats from Dissident Groups: The unique geographical and border conditions of Khuzestan make it susceptible to security threats, which could be aggravated by demographic changes. <strong> </strong> <strong>Conclusion</strong> Based on the findings of this research, it can be concluded that the impacts of climate migrations in Khuzestan not only result in economic problems but also affect social, political, geographical, and security dimensions. Therefore, policymakers must seriously address the management of conditions arising from climate change to prevent the consequences and impacts of climate migrations and to enhance community resilience these crises. <strong> </strong> https://esrj.sbu.ac.ir/article_105348_dc82948142f72cff0cf506896184f459.pdf