Geoscience frontiers最新文献

{"title":"Palynofloral and geochemical evidence for Permian-Triassic transition from Talcher Coalfield, Son-Mahanadi Basin, India: Insights into age, palaeovegetation, palaeoclimate and palaeowildfire","authors":"Srikanta Murthy ,&nbsp;Deveshwar P. Mishra ,&nbsp;Dieter Uhl ,&nbsp;Anju Saxena ,&nbsp;Vikram P. Singh ,&nbsp;Runcie P. Mathews ,&nbsp;Anurag Kumar ,&nbsp;Bindhyachal Pandey","doi":"10.1016/j.gsf.2025.102086","DOIUrl":"10.1016/j.gsf.2025.102086","url":null,"abstract":"<div><div>The Permian-Triassic (P/T) transition is marked by the most severe mass-extinction event of the Phanerozoic. Although much is known about this event in the marine realm, there are many open questions regarding what happened during this period to many continental biota. In the case of plants, a drastic mass-extinction event has even been negated by some authors. To add about the knowledge on continental biota in India during this crucial time period, the present study analysed the palynology, palynofacies, organic geochemistry (biomarkers), stable isotopes, and charcoal within the subsurface Gondwana deposits of the Kamthi Formation (late Permian-early Triassic) from core TTB-7 from the Tribida block, located in the Talcher Coalfield of the Mahanadi Basin, India.</div><div>The primary objectives are to validate the age of the strata, ascertain the palaeodepositional setting of the palaeomire, and propose palaeobotanical evidence regarding the occurrence of wildfires within this stratigraphic succession and changes in floral content across the P/T transition. The palynological study proposes two palynoassemblage zones, <em>Densipollenites magnicorpus</em> and <em>Klausipollenites schaubergeri,</em> suggesting a latest Permian (Lopingian) and early Triassic (Induan?) age for the studied succession, respectively. The age is also inferred based on correlation with coeval assemblages from India and other Gondwana continents. The palynoassemblages reveal the dominance of Glossopteridales and Coniferales along with Filicales, Lycopsidales, Equisetales, Cordaitales and Peltaspermales. The relatively higher values of the carbon preference index and terrigenous/aquatic ratio also suggest higher plant input. However, a bimodal <em>n</em>-alkane distribution pattern suggests the contribution of terrigenous and microbial sources. Although the occurrences of long-chain alkanes indicate input of higher plants, the low <em>P</em>_wax values (&lt;0.26) suggest relatively less contribution. The <em>P</em>_aqvalues (≅1) and amorphous organic matter (av. 33.24%) suggest a significant macrophyte input in the studied samples, pointing to the occurrence of moderate aquatic conditions in the basin.</div><div>Furthermore, the distribution of hopanoids and the content of degraded organic matter (av. 29.96%) reflect the bacterial degradation of organic matter. Also, the <em>δ</em>¹³C values of the studied section varied from −31.2‰ to −21.8‰. A large carbon isotopic offset of 9.4‰ across the P/T transition, Pr/Ph ratio (0.3–1.3) and shift in the distribution pattern of palynofacies components is indicating a significant change in climatic conditions. Moreover, the presence of macroscopic charcoal fragments of gymnospermous affinity with pre-charring colonization by fungi provides evidence for wildfire occurring during the Lopingian (Late Permian) in this basin.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 4","pages":"Article 102086"},"PeriodicalIF":8.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orbital to millennial scale dust activity and humidity interaction in Central Asia during the last glacial period","authors":"Haoru Wei ,&nbsp;Yougui Song ,&nbsp;Shugang Kang ,&nbsp;Mingyu Zhang ,&nbsp;Mengping Xie ,&nbsp;Yanping Wang ,&nbsp;Li Han ,&nbsp;Shukhrat Shukurov ,&nbsp;Nosir Shukurov ,&nbsp;Fakhriddin Fayziev","doi":"10.1016/j.gsf.2025.102099","DOIUrl":"10.1016/j.gsf.2025.102099","url":null,"abstract":"<div><div>The factors controlling dust activity and humidity in Central Asia and their relationships remain controversial, partly due to a lack of high-resolution geological records for the mid-to-late last glaciation. In this study, we established an optically stimulated luminescence chronology for the QSHA profile in the Yili Basin, a region influenced by westerlies. Grain size and trace element data were used as paleoclimatic indicators. We investigated the relationships among Central Asian dust activity, humidity, and westerlies strength on orbital to millennial scale from 37.4 ka to 11.6 ka. Our study reveals that, on orbital timescales, humidity is positively correlated with westerlies strength which controlled by precession. Dust activity is controlled by Siberian High which was regulated by Northern Hemisphere high-latitude temperature. Their responses to low-latitude and high-latitude forcing mechanisms respectively and present an opposite relationship. On millennial timescales, humidity and westerlies strength are positively correlated. During Marine Isotope Stage (MIS) 2, humidity and dust activity show synchronous fluctuations, while during MIS 3, they exhibit an inverse relationship. Westerlies strength regulated humidity, which subsequently controlled glacial activity in the Tianshan Mountains, influencing dust activity in Central Asia. Additionally, the QSHA profile recorded seven Dansgaard-Oeschger (D-O) events on millennial timescales, indicating a potential link between Central Asian dust activity and high-latitude temperature variations in the Northern Hemisphere. Our findings provide new insights into dust and humidity interaction during the last glaciation periods in Central Asia and contribute to understanding global dust and hydrological cycles.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 4","pages":"Article 102099"},"PeriodicalIF":8.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting groundwater fluoride levels for drinking suitability using machine learning approaches with traditional and fuzzy logic models-based health risk assessment","authors":"D. Karunanidhi ,&nbsp;M.Rhishi Hari Raj ,&nbsp;V.N. Prapanchan ,&nbsp;T. Subramani","doi":"10.1016/j.gsf.2025.102087","DOIUrl":"10.1016/j.gsf.2025.102087","url":null,"abstract":"<div><div>The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin, South India. Fluoride levels in the study area vary between 0.1 and 3.10 mg/L, with 32 samples exceeding the World Health Organization (WHO) standard of 1.5 mg/L. Hydrogeochemical analyses (Durov and Gibbs) clearly show that the overall water chemistry is primarily influenced by simple dissolution, mixing, and rock-water interactions, indicating that geogenic sources are the predominant contributors to fluoride in the study area. Around 446.5 km² is considered at risk. In predictive analysis, five Machine Learning (ML) models were used, with the AdaBoost model performing better than the other models, achieving 96% accuracy and 4% error rate. The Traditional Health Risk Assessment (THRA) results indicate that 65% of samples pose highly susceptible for dental fluorosis, while 12% of samples pose highly susceptible for skeletal fluorosis in young age groups. The Fuzzy Inference System (FIS) model effectively manages ambiguity and linguistic factors, which are crucial when addressing health risks linked to groundwater fluoride contamination. In this model, input variables include fluoride concentration, individual age, and ingestion rate, while output variables consist of dental caries risk, dental fluorosis, and skeletal fluorosis. The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis, along with very young and young age groups. This study is an essential resource for local authorities, healthcare officials, and communities, aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment, aligning with Sustainable Development Goals (SDGs) 3 and 6, thereby contributing to a cleaner and healthier society.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 4","pages":"Article 102087"},"PeriodicalIF":8.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of overburden layer thickness based on spatial heterogeneity analysis and machine learning models in hillslope regions","authors":"Zhilu Chang ,&nbsp;Shui-Hua Jiang ,&nbsp;Faming Huang ,&nbsp;Lei Shi ,&nbsp;Jinsong Huang ,&nbsp;Jianhong Wan ,&nbsp;Filippo Catani","doi":"10.1016/j.gsf.2025.102109","DOIUrl":"10.1016/j.gsf.2025.102109","url":null,"abstract":"<div><div>The spatial distribution of overburden layer thickness (OLT) is crucial for landslide susceptibility prediction and slope stability analysis. Due to OLT spatial heterogeneity in hillslope regions, combined with the difficulty and time consumption of OLT sample collection, accurately predicting OLT distribution remains a challenging. To address this, a novel framework has been developed. First, OLT samples are collected through field surveys, remote sensing, and geological drilling. Next, the heterogeneity of OLT’s spatial distribution is analyzed using the probability distribution of OLT samples and their horizontal and vertical distributions. The OLT samples are categorized and the small sample categories are expanded using the synthetic minority over-sampling technique (SMOTE). The slope position is selected as a key conditioning factor. Subsequently, 16 conditioning factors are applied to construct OLT prediction model using the random forest regression algorithm. Weights are assigned to each OLT sample category to balance the uneven distribution of sample sizes. Finally, the Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Lin’s concordance correlation coefficient (Lin’s CCC) are employed to validate the OLT prediction results. The Huangtan town serves as the case study. Results show: (1) heterogeneity analysis, SMOTE-based OLT sample expansion strategy and slope position selection can significantly mitigate the effect of spatial heterogeneity on OLT prediction. (2) The Pearson correlation coefficient, RMSE, MAE and Lin’s CCC values are 0.84, 1.173, 1.378 and 0.804, respectively, indicating excellent prediction performance. This research provides an effective solution for predicting OLT distribution in hillslope regions.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 5","pages":"Article 102109"},"PeriodicalIF":8.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The net warming effect of clouds on global surface temperature may be weakening or even disappearing","authors":"Chuanye Shi ,&nbsp;Tianxing Wang ,&nbsp;Gaofeng Wang ,&nbsp;Husi Letu","doi":"10.1016/j.gsf.2025.102107","DOIUrl":"10.1016/j.gsf.2025.102107","url":null,"abstract":"<div><div>Climate change is significantly influenced by both clouds and Earth’s surface temperature (EST). While numerous studies have investigated clouds and EST separately, the extent of clouds’ impact on EST remains unclear. Based on the inspiration and limitation of cloud radiative effect (CRE), this study provides a pioneering attempt to propose a novel indicator, cloud radiative effect on surface temperature (CREST), aiming to quantify how clouds affect EST globally while also analyzing the physical mechanism. Using reanalysis and remotely sensed data, a phased machine learning scheme in combination of surface energy balance theory is proposed to estimate EST under all-sky and hypothetical clear-sky conditions in stages, thereby estimating the newly defined CREST by subtracting the hypothetical clear-sky EST from the all-sky EST. The inter-annual experiments reveal the significant spatial heterogeneity in CREST across land, ocean, and ice/snow regions. As a global offset of the heterogeneity, clouds exhibit a net warming effect on global surface temperature on an annual scale (e.g., 0.26 K in 1981), despite their ability to block sunlight. However, the net warming effect has gradually weakened to nearly zero over the past four decades (e.g., only 0.06 K in 2021), and it’s even possible to transform into a cooling effect, which might be good news for mitigating the global warming.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 5","pages":"Article 102107"},"PeriodicalIF":8.5,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the groundwater recharge processes in intensively irrigated regions: An approach combining isotope hydrology and machine learning","authors":"Md. Arzoo Ansari ,&nbsp;Jacob Noble ,&nbsp;U.Saravana Kumar ,&nbsp;Archana Deodhar ,&nbsp;Naima Akhtar ,&nbsp;Priyanka Singh ,&nbsp;Rishi Raj","doi":"10.1016/j.gsf.2025.102105","DOIUrl":"10.1016/j.gsf.2025.102105","url":null,"abstract":"<div><div>Agriculture is a major contributor to the global economy, accounting for approximately 70% of the freshwater use, which cause significant stress on aquifers in intensively irrigated regions. This stress often leads to the decline in both the quantity and quality of groundwater resources. This study is focused on an intensively irrigated region of Northern India to investigate the sources and mechanism of groundwater recharge using a novel integrated approach combining isotope hydrology, Artificial Neural Network (ANN), and hydrogeochemical models. The study identifies several key sources of groundwater recharge, including natural precipitation, river infiltration, Irrigation Return Flow (IRF), and recharge from canals. Some groundwater samples exhibit mixing from various sources. Groundwater recharge from IRF is found to be isotopically enriched due to evaporation and characterized by high Cl⁻. Stable isotope modeling of evaporative enrichment in irrigated water helped to differentiate the IRF during various cultivation periods (<em>Kharif</em> and <em>Rabi</em>) and deduce the climatic conditions prevailed during the time of recharge. The model quantified that 29% of the irrigated water is lost due to evaporation during the <em>Kharif</em> period and 20% during the <em>Rabi</em> period, reflecting the seasonal variations in IRF contribution to the groundwater. The ANN model, trained with isotope hydrogeochemical data, effectively captures the complex interrelationships between various recharge sources, providing a robust framework for understanding the groundwater dynamics in the study area. A conceptual model was developed to visualize the spatial and temporal distribution of recharge sources, highlighting how seasonal irrigation practices influence the groundwater. The integration of isotope hydrology with ANN methodologies proved to be effective in elucidating the multiple sources and processes of groundwater recharge, offering insights into the sustainability of aquifer systems in intensively irrigated regions. These findings are critical for developing data-driven groundwater management strategies that can adapt to future challenges, including climate change, shifting land use patterns, and evolving agricultural demands. The results have significant implications for policymakers and water resource managers seeking to ensure sustainable groundwater use in water-scarce regions.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 5","pages":"Article 102105"},"PeriodicalIF":8.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics and distribution of late Carboniferous to early Permian wildfires and their controlling factors","authors":"Yanan Li ,&nbsp;Jingqi Xue ,&nbsp;Shuai Wang ,&nbsp;Zhaorui Ye ,&nbsp;Jiao Fang ,&nbsp;Xiongxiong Li","doi":"10.1016/j.gsf.2025.102100","DOIUrl":"10.1016/j.gsf.2025.102100","url":null,"abstract":"<div><div>The late Carboniferous to early Permian period is renowned for extensive coal formation and frequent paleowildfires. Nonetheless, the nature and distribution of these wildfires varied significantly over time. In an effort to elucidate the patterns of paleowildfires during the late Paleozoic Ice Age and to probe into the controlling mechanisms of paleowildfires under icehouse conditions, a comprehensive analysis was performed on coal samples from the Taiyuan and Shanxi formations within the Dacheng coalfield of Hebei Province, North China. The dataset was augmented with global inertinite data from the late Carboniferous to early Permian periods and was compared to paleowildfire patterns from the Pliocene to Holocene epochs. The results show that paleowildfires in the Dacheng coalfield of North China transitioned from moderate-scale, low-intensity surface fires to large-scale, relatively high-intensity ground fires. Globally, the distribution of paleowildfires shifted from Euramerica to Gondwana, Cathaysia, and Angara from 300 Ma to 290 Ma, accompanied by a corresponding increase in inertinite content. This spatial and temporal variation in wildfire activity appears to have been strongly influenced by paleoclimate and atmospheric conditions. At 300 Ma, cooler and wetter paleoclimate, coupled with relatively low atmospheric <em>p</em>O₂ levels, likely contributed to a reduced incidence of paleowildfires. In contrast, at 290 Ma, warmer paleoclimate, higher atmospheric <em>p</em>O₂ levels, and the flourishing mires in Gondwana, Cathaysia, and Angara were conducive to more intense paleowildfires. This pattern is further supported by the comparison to more recent icehouse periods. Similar to the late Carboniferous–early Permian period, wildfire activity increased from the Pliocene to the Holocene, highlighting the critical role of climatic conditions in driving wildfire proliferation under icehouse conditions. However, the Pleistocene to Holocene wildfires were less intense than those of the late Carboniferous–early Permian, suggesting that atmospheric oxygen concentrations played a key role in modulating the evolution of the fire systems over geological timescales. These findings underscore the complex interplay between climate, atmospheric composition, and vegetation in shaping wildfire dynamics across Earth’s history.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 5","pages":"Article 102100"},"PeriodicalIF":8.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A snapshot of subduction initiation within a back-arc basin: Insights from Shiquanhe ophiolite, western Tibet","authors":"Wei-Liang Liu ,&nbsp;Hui Liang ,&nbsp;Harald Furnes ,&nbsp;Xu Zhang ,&nbsp;Qing-Gao Zeng ,&nbsp;Yao-Liang Ma ,&nbsp;Chi Yan ,&nbsp;Ru-Xin Ding ,&nbsp;Yun Zhong ,&nbsp;Run-Xi Gu","doi":"10.1016/j.gsf.2025.102088","DOIUrl":"10.1016/j.gsf.2025.102088","url":null,"abstract":"<div><div>Back-arc basins are key sites for oceanic lithosphere formation and consumption at convergent plate boundaries, and their formation and subduction processes can be highly variable. The tectonic setting and evolution of the Meso-Tethys Shiquanhe-Jiali ophiolite sub-belt (SJO sub-belt) within Bangong-Nujiang Suture Zone (BNSZ), central Tibet, are disputed for the complex rock composition and ages. In this paper, we present geochronology, geochemistry and field observations on the Shiquanhe ophiolite, providing a representative ophiolite example in the western end of SJO. Based on investigation of the petrogenesis and tectonic setting of different rock types, combined with the U-Pb dating, we propose a two-stage subduction model for explaining the tectonic evolution of SJO as well as the wither away of a back-arc basin. Geochemical and geochronological data indicate that the ca. 183 Ma LAN (north of Lameila) gabbros formed in the forearc setting and represent the early-stage subduction of the Bangong Meso-Tethys. This subduction induced the back-arc spreading recorded in the ca. 170 Ma gabbros and lower pillow basalts of PL-SDN (Pagelizanong-Shiquanhe Dam Nan) ophiolitic fragments in the Shiquanhe ophiolite. The basaltic lavas overlying the lower basalts, represented by the ca. 168–164 Ma diabasic and boninite dikes have forearc characteristics, and they represent the back-arc basin subduction initiation at a late stage. This work thus recovered the multiple tectonic evolution of SJO sub-belt and emphasise the importance of the back-arc basin subduction in the evolution of ancient oceans.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 5","pages":"Article 102088"},"PeriodicalIF":8.5,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient rock joint detection from large-scale 3D point clouds using vectorization and parallel computing approaches","authors":"Yunfeng Ge ,&nbsp;Zihao Li ,&nbsp;Huiming Tang ,&nbsp;Qian Chen ,&nbsp;Zhongxu Wen","doi":"10.1016/j.gsf.2025.102085","DOIUrl":"10.1016/j.gsf.2025.102085","url":null,"abstract":"<div><div>The application of three-dimensional (3D) point cloud parametric analyses on exposed rock surfaces, enabled by Light Detection and Ranging (LiDAR) technology, has gained significant popularity due to its efficiency and the high quality of data it provides. However, as research extends to address more regional and complex geological challenges, the demand for algorithms that are both robust and highly efficient in processing large datasets continues to grow. This study proposes an advanced rock joint identification algorithm leveraging artificial neural networks (ANNs), incorporating parallel computing and vectorization of high-performance computing. The algorithm utilizes point cloud attributes—specifically point normal and point curvatures—as input parameters for ANNs, which classify data into rock joints and non-rock joints. Subsequently, individual rock joints are extracted using the density-based spatial clustering of applications with noise (DBSCAN) technique. Principal component analysis (PCA) is subsequently employed to calculate their orientations. By fully utilizing the computational power of parallel computing and vectorization, the algorithm increases the running speed by 3–4 times, enabling the processing of large-scale datasets within seconds. This breakthrough maximizes computational efficiency while maintaining high accuracy (compared with manual measurement, the deviation of the automatic measurement is within 2°), making it an effective solution for large-scale rock joint detection challenges.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 5","pages":"Article 102085"},"PeriodicalIF":8.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global projections of future landslide susceptibility under climate change","authors":"Yu Duan ,&nbsp;Mingtao Ding ,&nbsp;Yufeng He ,&nbsp;Hao Zheng ,&nbsp;Ricardo Delgado-Téllez ,&nbsp;Sergey Sokratov ,&nbsp;Francisco Dourado ,&nbsp;Sven Fuchs","doi":"10.1016/j.gsf.2025.102074","DOIUrl":"10.1016/j.gsf.2025.102074","url":null,"abstract":"<div><div>Landslides pose a significant threat to both human society and environmental sustainability, yet, their spatiotemporal evolution and impacts on global scales in the context of a warming climate remain poorly understood. In this study, we projected global landslide susceptibility under four shared socioeconomic pathways (SSPs) from 2021 to 2100, utilizing multiple machine learning models based on precipitation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and static metrics. Our results indicate an overall upward trend in global landslide susceptibility under the SSPs compared to the baseline period (2001–2020), with the most significant increase of about 1% in the very far future (2081–2100) under the high emissions scenario (SSP5-8.5). Currently, approximately 13% of the world’s land area is at very high risk of landslide, mainly in the Cordillera of the Americas and the Andes in South America, the Alps in Europe, the Ethiopian Highlands in Africa, the Himalayas in Asia, and the countries of East and South-East Asia. Notably, India is the country most adversely affected by climate change, particularly during 2081–2100 under SSP3-7.0, with approximately 590 million people—23 times the global average—living in areas categorized as having very high susceptibility.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 4","pages":"Article 102074"},"PeriodicalIF":8.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}