Arabian Journal of Geosciences最新文献

{"title":"Hydrodynamic variability and depositional processes of the Mahadek formation, Shillong plateau, NE India: an integrated grain-size and discriminant function approach","authors":"Vijay Kumar Salwanshi,&nbsp;Mrutyunjay Das,&nbsp;M. A. Khonglah","doi":"10.1007/s12517-026-12592-3","DOIUrl":"10.1007/s12517-026-12592-3","url":null,"abstract":"<div><p>The Mahadek Formation represents a Cretaceous sedimentary succession developed within a tectonically active basin in north-eastern India. This study integrates grain-size statistics, discriminant function analysis, and field observations to reconstruct depositional processes and basin evolution. 40 representative samples have been collected from the Mahadek Formation and analysed for grain-size statistics using Folk and Ward (1957) statistical parameters. The mean grain size ranges from 0.23 to 2.47 ɸ, standard deviation from 0.88 to 1.71 ɸ, skewness from − 0.25 to + 0.43, and kurtosis from 0.58 to 2.11. The sediments are dominated by medium- to coarse-grained sandstones with subordinate fine and very coarse fractions, indicating variable hydrodynamic conditions and fluctuating flow competence. The predominance of poorly sorted and fine-skewed samples suggests fluctuating depositional energy and episodic deposition of finer sediment. The discriminant-function results indicate variable environmental signatures, and their integration with sedimentary structures, lithological characteristics and fossil evidence suggests a predominantly fluvial system with intermittent shallow-marine influence. These characteristics indicate a shift from high-energy fluvial environments to transitional and shallow marine conditions. However, the grain-size and discriminant-function data do not independently constrain specific climatic phases, eustatic sea-level fluctuations or tectonic subsidence. The depositional characteristics of the Mahadek Formation are compatible with the broader Late Cretaceous regional and global climatic and eustatic framework, but these factors are considered as regional context rather than directly demonstrated controls. The study highlights the importance of integrating sedimentological data with the climate and eustatic controls to understand the evolution of fluvio-shallow marine depositional systems in the sedimentary basins.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 10","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing urban growth and its impact on groundwater potential zones in Guwahati City, Assam, India: a GIS-based approach","authors":"Jayashri Dutta,&nbsp;Runti Choudhury","doi":"10.1007/s12517-026-12585-2","DOIUrl":"10.1007/s12517-026-12585-2","url":null,"abstract":"<div><p>Remote sensing and GIS significantly enhance the possibilities for groundwater assessment, monitoring, and management. This study aims to utilise these tools to illustrate urban growth over the past three decades in Guwahati City, India and to decipher the Groundwater Potential Zones (GWPZ), thereby facilitating effective groundwater management. Landsat images were used to classify land use and land cover (LULC). The thematic layers were appropriately selected and assigned weights employing multi-criteria decision analysis, Analytical Hierarchy Process (AHP). Urban expansion has occurred extensively over three decades, with 210% growth in built-up area, reducing vegetation (13.24%), water bodies (13.49%), and fallow land (98.11%). The Highest weight was assigned to geology, followed by geomorphology, lithology, drainage density, LULC, slope, lineament density, topsoil sand, and topsoil clay allowed categorisation of GWPZs into very poor, poor, intermediate, high, and very high. The very poor potential covered the highest area (40.91%) in 2011, followed by very high (30.41%), high (18.14%), moderate (10.53%), and poor (0.01%). In 2022 also, the highest area fell within very poor potential zones, accounting for 53.49%, followed by high (23.79%), moderate (16.48%), very high (6.17%), and poor (0.01%). The increase in very poor GWPZ is primarily linked to the urban expansion. Conversely, high to very high GWPZ are located in the eastern, northern, and south–western parts of the city; while very poor prospects are found within the central zone, where impervious coverings hinder groundwater recharge. Groundwater monitoring wells further reveal a declining trend in groundwater levels within the city’s central zone.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 10","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gradient boosting and machine learning approaches for enhanced tight gas reservoir evaluation in the Eastern Sichuan Basin","authors":"Shakir Ullah,&nbsp;Anwar Ullah,&nbsp;Naresh Kumar,&nbsp;Zhang Xing,&nbsp;Aniqa Murtaza,&nbsp;Nawaz Mustafa","doi":"10.1007/s12517-026-12588-z","DOIUrl":"10.1007/s12517-026-12588-z","url":null,"abstract":"<div><p>Tight gas reservoirs in structurally complex areas, such as the Eastern Sichuan Basin, generally exhibit moderate porosity but extremely low permeability (usually <span>(varvec{&lt; 0.1 mD})</span>), together with heterogeneous lithofacies and abundant natural fractures. The mechanical and petrophysical complexity of these formations poses significant challenges for conventional logging techniques, potentially resulting in up to a 30% underestimation of fracture zones. This study addresses these limitations by integrating dipole sonic logging with advanced gradient boosting and machine learning (ML) approaches to enhance the extraction and interpretation of critical subsurface properties, improving tight gas reservoir evaluation. A comprehensive six-model ML framework was developed using data from Well Fulu 1 (depth interval: 2300-2838 meters), incorporating algorithms such as Random Forest, XGBoost, LightGBM, Support Vector Machines, Gradient Boosting, and K-Nearest Neighbors. The dataset covered 19 geophysical and petrophysical parameters, including Young’s Modulus, Poisson’s Ratio, Fracture Index, Gamma Ray, Density, and acoustic travel times. Validation through 10-fold cross-validation with statistical significance testing (p &lt;0.001) and economic impact analysis revealed substantial improvements. We achieved Random Forest 94.3% sensitivity in fracture detection compared to 68% using conventional methods, XGBoost reached an R<span>(^{varvec{2}})</span> of 0.89 for TOC prediction (vs. 0.71), and the processing time was reduced by 92-94%. The economic impact was significant, with $1.906M total value creation per well, including direct cost savings ($666K) and NPV improvements ($1.24M). This ML-enhanced workflow offers a scalable, efficient solution for tight gas reservoir evaluation, establishing a new paradigm for data-driven exploration and development strategies in unconventional hydrocarbon resources.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 10","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TGDI-assisted U-NET framework for tree canopy semantic segmentation","authors":"Shanti Chandra,&nbsp;Sushilkumar Jaiswar,&nbsp;Kumar Anip,&nbsp;Girish Kumar Singh","doi":"10.1007/s12517-026-12590-5","DOIUrl":"10.1007/s12517-026-12590-5","url":null,"abstract":"<div><p>In this paper, a modified version of Tree Grass Differentiation Index (TGDI) is calculated by using image processing algorithms, based on Canny edge detection and brightness. Calculated value of TGDI is utilized for thresholding to generate a binary mask of tree canopies in the image. The U-NET convolutional neural network (CNN) architecture is employed to train the semantic segmentation model for tree canopies using binary mask generated from the TGDI thresholding process. The process was repeated by training a UNET model without TGDI as a feature, and its performance has been compared with the model trained with TGDI as a feature. Furthermore, Residual UNET architecture has been explored to compare the performance of the model trained with and without TGDI as a feature. To perform this study, Boston, USA area is considered and the dataset is taken from the US Tree Map website. The raster has a resolution of 60 cm and has three bands, Red, Green and Blue, with height 12,810 and width 9966. The performance of the models was evaluated based on accuracy, Dice coefficient, and F1 score. U-NET with TGDI as feature achieved the pixel accuracy of 96.7% with Dice coefficient of 0.94, and F1 score of 0.93, while the U-NET model without TGDI achieved pixel accuracy of 91.8% with Dice coefficient of 0.84 and F1 score of 0.87. These results show how the traditional index-based techniques and deep learning models may be effectively combined to accurately identify tree canopies in the satellite images. The outcome of this study suggests that incorporating spectral information, such as TGDI into deep learning models can improve the segmentation performance.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 10","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Greenhouse gas emissions: trends, impacts, and global mitigation approaches","authors":"Sushmita,&nbsp;Amandeep Kaur,&nbsp;Amanpreet Kaur,&nbsp;Pawan Kumar","doi":"10.1007/s12517-026-12578-1","DOIUrl":"10.1007/s12517-026-12578-1","url":null,"abstract":"<div><p>Greenhouse gas (GHG) emissions are a significant driver of global climate change, leading to widespread environmental, social, and economic impacts. GHG emissions occur primarily due to human activities such as burning fossil fuels for energy, industrial processes, deforestation, agriculture, and waste management. This leads to an overall warming of the Earth’s surface and atmosphere, causing climate change. The consequences of GHG emissions and climate change are far-reaching and include intense extreme weather events, disruptions to ecosystems and biodiversity, and impacts on human health, agriculture, and economies. In this review article, we examine the trends and patterns of GHG emissions, assess their environmental and human health impacts, and explore a range of mitigation strategies to reduce emissions and mitigate climate change. Through a comprehensive analysis of existing literature and case studies, we highlight the importance of concerted local, national, and global efforts to address GHG emissions and transition to a low-carbon economy. Overall, this article is a valuable resource for policymakers, researchers, and stakeholders seeking to understand the complexities of GHG emissions and identify pathways toward a sustainable, resilient future.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 9","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change and water security in Afghanistan: a comprehensive review","authors":"Zabihullah Karokhil,&nbsp;Habibullah Salim,&nbsp;Najibullah Totakhil","doi":"10.1007/s12517-026-12593-2","DOIUrl":"10.1007/s12517-026-12593-2","url":null,"abstract":"<div><p>Afghanistan is becoming more water insecure, given that water resources rely on winter precipitation, snowpack, glacier melt, seasonal river flow and shallow groundwater systems. Based on the review of peer-reviewed literature, institutional reports and hydrological datasets, this review presents evidence regarding the effects of climate change on water security in Afghanistan from 2000 to 2026. It examines observed and projected changes in the areas of temperature, precipitation, droughts and floods, glacier and snowpack conditions, river flow, groundwater recharge, and human pressures on water resources. The evidence reviewed indicates that temperatures have been rising in Afghanistan, precipitation has become more variable, there have been more droughts and floods, glaciers in the Hindu Kush region have been retreating, the reliability of snow cover has decreased, groundwater recharge has been decreasing, and there has been an increasing mismatch between water supply and demand. Such changes are significant in Kabul, Helmand, Amu Darya, Hari Rod – Murghab, and Northern river basins. The review further reveals that population growth, expansion of irrigation, over-extraction of groundwater, dam construction and limited institutional capacity exacerbate water stress caused by climate change. The study helps fill important knowledge gaps in the field of climate-resilient pathways by pulling together recent quantitative evidence and proposing a range of climate-resilient pathways that include integrated water resources management, better hydrometeorological monitoring, sustainable groundwater management, more efficient irrigation, water storage development and regional cooperation.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 9","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change and water security in Afghanistan: a comprehensive review","authors":"Zabihullah Karokhil,&nbsp;Habibullah Salim,&nbsp;Najibullah Totakhil","doi":"10.1007/s12517-026-12593-2","DOIUrl":"10.1007/s12517-026-12593-2","url":null,"abstract":"<div><p>Afghanistan is becoming more water insecure, given that water resources rely on winter precipitation, snowpack, glacier melt, seasonal river flow and shallow groundwater systems. Based on the review of peer-reviewed literature, institutional reports and hydrological datasets, this review presents evidence regarding the effects of climate change on water security in Afghanistan from 2000 to 2026. It examines observed and projected changes in the areas of temperature, precipitation, droughts and floods, glacier and snowpack conditions, river flow, groundwater recharge, and human pressures on water resources. The evidence reviewed indicates that temperatures have been rising in Afghanistan, precipitation has become more variable, there have been more droughts and floods, glaciers in the Hindu Kush region have been retreating, the reliability of snow cover has decreased, groundwater recharge has been decreasing, and there has been an increasing mismatch between water supply and demand. Such changes are significant in Kabul, Helmand, Amu Darya, Hari Rod – Murghab, and Northern river basins. The review further reveals that population growth, expansion of irrigation, over-extraction of groundwater, dam construction and limited institutional capacity exacerbate water stress caused by climate change. The study helps fill important knowledge gaps in the field of climate-resilient pathways by pulling together recent quantitative evidence and proposing a range of climate-resilient pathways that include integrated water resources management, better hydrometeorological monitoring, sustainable groundwater management, more efficient irrigation, water storage development and regional cooperation.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 9","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Greenhouse gas emissions: trends, impacts, and global mitigation approaches","authors":"Sushmita,&nbsp;Amandeep Kaur,&nbsp;Amanpreet Kaur,&nbsp;Pawan Kumar","doi":"10.1007/s12517-026-12578-1","DOIUrl":"10.1007/s12517-026-12578-1","url":null,"abstract":"<div><p>Greenhouse gas (GHG) emissions are a significant driver of global climate change, leading to widespread environmental, social, and economic impacts. GHG emissions occur primarily due to human activities such as burning fossil fuels for energy, industrial processes, deforestation, agriculture, and waste management. This leads to an overall warming of the Earth’s surface and atmosphere, causing climate change. The consequences of GHG emissions and climate change are far-reaching and include intense extreme weather events, disruptions to ecosystems and biodiversity, and impacts on human health, agriculture, and economies. In this review article, we examine the trends and patterns of GHG emissions, assess their environmental and human health impacts, and explore a range of mitigation strategies to reduce emissions and mitigate climate change. Through a comprehensive analysis of existing literature and case studies, we highlight the importance of concerted local, national, and global efforts to address GHG emissions and transition to a low-carbon economy. Overall, this article is a valuable resource for policymakers, researchers, and stakeholders seeking to understand the complexities of GHG emissions and identify pathways toward a sustainable, resilient future.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 9","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate-aware natural hazard modelling in data-scarce regions: a structured review of GIS, remote sensing, and artificial intelligence","authors":"Chaimaa EL KIHAL,&nbsp;Rachid BENBRIK,&nbsp;Nassima ELHAYA,&nbsp;Kholoud KAHIME","doi":"10.1007/s12517-026-12586-1","DOIUrl":"10.1007/s12517-026-12586-1","url":null,"abstract":"<div><p>As climate change progresses, natural hazards are becoming more frequent, more intense, and increasingly complex in their spatial behaviour, placing growing pressure on traditional hazard assessment frameworks. This review examines recent developments between 2019 and 2025, with particular attention to the expanding use of Geographic Information Systems (GIS), remote sensing, and artificial intelligence (AI) in modelling climate-related hazards such as floods, droughts, wildfires, and coastal processes. A structured screening of peer-reviewed literature retrieved from Scopus and Web of Science, supplemented by searches in Google Scholar, resulted in the retention of 121 peer-reviewed journal articles for analysis, covering floods, droughts, wildfires, and coastal hazards. The review identifies a pronounced methodological transition from conventional statistical and GIS-overlay approaches toward machine learning, deep learning, and hybrid modelling frameworks better suited to capturing nonlinear and dynamic hazard interactions. While these approaches often enhance predictive performance, several challenges remain, including data scarcity, spatial autocorrelation bias, validation constraints, and limited transferability across diverse climatic contexts. Moreover, although AI-based models frequently report higher predictive accuracy, their interpretability and integration into operational planning and risk management systems remain uneven, particularly in data-constrained environments. Using Morocco as an illustrative case of climatic heterogeneity and uneven monitoring infrastructure, this review underscores the importance of developing climate-aware, transparent, and transferable modelling strategies that integrate remote sensing time series, physical process understanding, and rigorous validation practices. Strengthening these dimensions is essential to ensure that methodological advancements translate into effective risk reduction and adaptive planning.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 9","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148838147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater potential zonation mapping using deterministic, statistical, and machine learning approaches: a comparative study of the Purulia district, west Bengal, India","authors":"Manas Das,&nbsp;Bikash Dutta,&nbsp;Sourajit Mal,&nbsp;Sakir Laskar,&nbsp;Biplab Das,&nbsp;Gaurav Kumar Gautam","doi":"10.1007/s12517-026-12587-0","DOIUrl":"10.1007/s12517-026-12587-0","url":null,"abstract":"<div><p>This study presents the first comprehensive model-comparison framework integrating the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), Logistic Regression (LR), and Random Forest (RF) for GPZ mapping in the Purulia district of West Bengal, India, situated on the eastern fringe of the Chota Nagpur Plateau. Fifteen hydrogeologically significant conditioning factors were prepared from multi-source geospatial data (ALOS PALSAR DEM, Sentinel-2, Geological Survey of India, Central Ground Water Board), and 66 verified groundwater well observations were partitioned into training (60.6%, <i>n</i> = 40) and testing (39.4%, <i>n</i> = 26) subsets. The AHP model employed a pairwise comparison matrix with a Consistency Ratio of 0.08, while FR assigned bivariate weights across all factor sub-classes. The LR model used a Principal Component Analysis-based Mahalanobis distance framework, retaining components explaining ≥ 95% of the variance, and the RF model employed 500 trees with mtry = 4. Model performance was evaluated through Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), Seed Cell Area Index (SCAI), and Pearson correlation analysis. The RF model achieved the highest predictive accuracy (AUC = 0.75), followed by LR (0.75), FR (0.72), and AHP (0.66), confirming that data-driven approaches outperform expert-based MCDA in capturing complex hydrogeological relationships. Variable importance analysis identified Geomorphology (4.21), LULC (2.19), and NDVI (2.04) as the dominant predictors. SCAI represents the ‘Moderate’ GPZ class as the most spatially coherent (SCAIₚₕₚ = 0.61; SCAIₕ_r = 0.51). Spatial outputs revealed that 36–40% of the 6,259 km² district falls within the ‘High’ to ‘Very High’ potential range, concentrated in the northeastern and southeastern blocks, which are underlain by metamorphosed volcanic rocks, sandstone outcrops, and pediment zones. The integrated framework offers a replicable, statistically robust methodology applicable to NAQUIM aquifer management and to implementing the Jal Shakti Abhiyan policy in similar hard-rock, semi-arid terrains worldwide.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"19 9","pages":""},"PeriodicalIF":1.827,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148838148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}