Pub Date : 2026-03-01Epub Date: 2026-01-17DOI: 10.1016/j.gsf.2026.102255
Shiji Dong , Yan Li , Xiaobin Yin , Qing Xu , Peng Mao
Accurate sea surface temperature (SST) forecasting across multiple timescales remains challenging. Daily forecasting frequently relies on autoregressive models prone to instability and over-smoothing, whereas monthly forecasting suffers from sparse data and the complex dynamics of ocean systems. Existing deep learning methods struggle to address these diverse challenges simultaneously. We introduce SSTFormer, a novel physics-guided deep learning framework that achieves leading results, with root mean squared error of 0.17 °C for daily forecasts and 0.60 °C for monthly forecasts, yielding lower bias and improved spatial coherence. The model’s core innovation is its unified and flexible architecture. For multi-step daily forecasts (1–15 days), it deploys as a “two-phase sequential ensemble” that replaces conventional autoregression and uses ocean current to solve instability and mitigate error accumulation. For single-step monthly forecasts, it is used in a direct forecasting configuration, proving effective at handling “sparse data” and “complex ocean dynamics.” SSTFormer demonstrates how a single architecture, through flexible deployment, can address the unique challenges of multi-scale SST forecasting, highlighting its potential as a unified and robust framework.
{"title":"Physics-guided deep learning for global sea surface temperature forecasting: Balancing accuracy and stability across timescales","authors":"Shiji Dong , Yan Li , Xiaobin Yin , Qing Xu , Peng Mao","doi":"10.1016/j.gsf.2026.102255","DOIUrl":"10.1016/j.gsf.2026.102255","url":null,"abstract":"<div><div>Accurate sea surface temperature (SST) forecasting across multiple timescales remains challenging. Daily forecasting frequently relies on autoregressive models prone to instability and over-smoothing, whereas monthly forecasting suffers from sparse data and the complex dynamics of ocean systems. Existing deep learning methods struggle to address these diverse challenges simultaneously. We introduce SSTFormer, a novel physics-guided deep learning framework that achieves leading results, with root mean squared error of 0.17 °C for daily forecasts and 0.60 °C for monthly forecasts, yielding lower bias and improved spatial coherence. The model’s core innovation is its unified and flexible architecture. For multi-step daily forecasts (1–15 days), it deploys as a “two-phase sequential ensemble” that replaces conventional autoregression and uses ocean current to solve instability and mitigate error accumulation. For single-step monthly forecasts, it is used in a direct forecasting configuration, proving effective at handling “sparse data” and “complex ocean dynamics.” SSTFormer demonstrates how a single architecture, through flexible deployment, can address the unique challenges of multi-scale SST forecasting, highlighting its potential as a unified and robust framework.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102255"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034337","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}
Pub Date : 2026-03-01Epub Date: 2025-10-22DOI: 10.1016/j.gsf.2025.102188
Lei-Lei Liu , Can Duan , Jun-Hua Gao , Hao Xiao , Wen-Qing Zhu , Can Yang
The landslide and non-landslide samples are important inputs for machine learning-based landslide susceptibility assessment. Compared to landslide samples, non-landslide samples generally present higher uncertainty due to random sampling. However, most sampling strategies (e.g., the feature space-based) for non-landslides only consider the characteristics of a single factor or the overall characteristics of all factors, which subsequently leads to either excessive artificial concentration of non-landslide samples or sampling information redundancy. To address these issues, a SHapley Additive exPlanations (SHAP) based sampling strategy considering combined characteristics of landslide conditioning factors (LCFs) is proposed. This strategy sorts the importance of LCFs based on SHAP algorithm and generates multiple sampling spaces using different numbers of LCFs in the sense of importance order. The optimal sampling space is selected according to the Bayesian optimization algorithm. Then, random forest (RF) and extreme gradient boosting (XGBoost) models are utilized to assess the susceptibility of Chaling County, Yanling County, and Guidong County, China, based on the proposed strategy and traditional random sampling. The results indicate that, compared with the traditional RF and XGBoost models, the improved models show better performance with an 8.2% and 9.0% increase in the AUC, respectively. Furthermore, the SHAP-based sampling framework demonstrates good adaptability across the study areas with different geological and geomorphic conditions, suggesting its potential transferability to other regions, although local optimization of parameter settings may still be required.
{"title":"Landslide susceptibility assessment using machine learning with a novel SHAP-based sampling strategy","authors":"Lei-Lei Liu , Can Duan , Jun-Hua Gao , Hao Xiao , Wen-Qing Zhu , Can Yang","doi":"10.1016/j.gsf.2025.102188","DOIUrl":"10.1016/j.gsf.2025.102188","url":null,"abstract":"<div><div>The landslide and non-landslide samples are important inputs for machine learning-based landslide susceptibility assessment. Compared to landslide samples, non-landslide samples generally present higher uncertainty due to random sampling. However, most sampling strategies (e.g., the feature space-based) for non-landslides only consider the characteristics of a single factor or the overall characteristics of all factors, which subsequently leads to either excessive artificial concentration of non-landslide samples or sampling information redundancy. To address these issues, a SHapley Additive exPlanations (SHAP) based sampling strategy considering combined characteristics of landslide conditioning factors (LCFs) is proposed. This strategy sorts the importance of LCFs based on SHAP algorithm and generates multiple sampling spaces using different numbers of LCFs in the sense of importance order. The optimal sampling space is selected according to the Bayesian optimization algorithm. Then, random forest (RF) and extreme gradient boosting (XGBoost) models are utilized to assess the susceptibility of Chaling County, Yanling County, and Guidong County, China, based on the proposed strategy and traditional random sampling. The results indicate that, compared with the traditional RF and XGBoost models, the improved models show better performance with an 8.2% and 9.0% increase in the AUC, respectively. Furthermore, the SHAP-based sampling framework demonstrates good adaptability across the study areas with different geological and geomorphic conditions, suggesting its potential transferability to other regions, although local optimization of parameter settings may still be required.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102188"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147448629","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}
Pub Date : 2026-03-01Epub Date: 2025-11-17DOI: 10.1016/j.gsf.2025.102212
Yunhao Wang , Wengang Zhang , Luqi Wang , Songlin Liu , Kaiqiang Zhang , Pengfei Liu , Weixin Sun , Shuihua Jiang
Landslide susceptibility mapping (LSM) is an essential tool for the prevention and management of landslide-related disasters. Conventional machine learning-based LSM method faces significant limitations in cross-regional extrapolation. To address this challenge, this study develops a transfer learning (TL) model based on the Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) framework, specifically designed for cross-regional LSM. A total of 11 modelling scenarios is established to compare the cross-regional extrapolation performance of Random Forest (RF), CNN-BiLSTM, and TL models, with Wanzhou District and Wushan County in Chongqing used as case studies. The results indicate that, compared to the strategy of directly expanding training dataset used by RF and CNN-BiLSTM models, the pre-training and fine-tuning strategy employed by the TL model is more suitable for county-scale LSM and its cross-regional extrapolation. Additionally, the cross-regional extrapolation performance of the TL model improves as the volume of source domain data increases. Finally, the SHAP algorithm is used to provide a global interpretation of the TL #3 model, which demonstrates the best performance in cross-regional model extrapolation.
{"title":"Cross-regional extrapolation of landslide susceptibility mapping via transfer learning","authors":"Yunhao Wang , Wengang Zhang , Luqi Wang , Songlin Liu , Kaiqiang Zhang , Pengfei Liu , Weixin Sun , Shuihua Jiang","doi":"10.1016/j.gsf.2025.102212","DOIUrl":"10.1016/j.gsf.2025.102212","url":null,"abstract":"<div><div>Landslide susceptibility mapping (LSM) is an essential tool for the prevention and management of landslide-related disasters. Conventional machine learning-based LSM method faces significant limitations in cross-regional extrapolation. To address this challenge, this study develops a transfer learning (TL) model based on the Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) framework, specifically designed for cross-regional LSM. A total of 11 modelling scenarios is established to compare the cross-regional extrapolation performance of Random Forest (RF), CNN-BiLSTM, and TL models, with Wanzhou District and Wushan County in Chongqing used as case studies. The results indicate that, compared to the strategy of directly expanding training dataset used by RF and CNN-BiLSTM models, the pre-training and fine-tuning strategy employed by the TL model is more suitable for county-scale LSM and its cross-regional extrapolation. Additionally, the cross-regional extrapolation performance of the TL model improves as the volume of source domain data increases. Finally, the SHAP algorithm is used to provide a global interpretation of the TL #3 model, which demonstrates the best performance in cross-regional model extrapolation.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102212"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147448630","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}
Pub Date : 2026-03-01Epub Date: 2025-12-21DOI: 10.1016/j.gsf.2025.102240
Zhenjiang Wang , Shaorui Zhao , Jingbo Li , Yanfei Zhang , Chao Wang , Dan Li , Zhenmin Jin
The genesis of bonanza-style gold deposits, characterized by weight-percent-level Au enrichment, challenges conventional models of chemical transport via aqueous complexes. Through high-pressure experiments (0.5–1.5 GPa, 600–1150 °C) combined with thermodynamic modeling and transmission electron microscopy (TEM) analyses, we demonstrate that CO2-rich fluids generated by metamorphic decarbonization create overpressures exceeding ∼ 200 MPa. This initiates explosive upward migration of sulfide liquids containing Au-Ag nanoparticles (NPs) into porous peridotite at velocities up to 55.9 ± 12.9 μm/h. High-resolution TEM analyses furthermore confirm the mechanical entrainment of Au-Ag NPs within sulfides. Fractal analysis (FD = 1.55–1.62) of dendritic sulfide networks reveals that viscous fingering dominates fluid dynamics. We propose a unified model where gas-driven filter pressing extracts Au-bearing sulfides from subducted slabs, while viscous fingering further facilitates kilometer-scale transport through lithospheric faults. This novel mechanism bridges mantle-derived carbon fluxes with crustal mineralization, offering new insights into the formation of ultrahigh-grade gold deposits.
{"title":"Transport of colloidal Au-bearing nanoparticles driven by metamorphic decarbonization","authors":"Zhenjiang Wang , Shaorui Zhao , Jingbo Li , Yanfei Zhang , Chao Wang , Dan Li , Zhenmin Jin","doi":"10.1016/j.gsf.2025.102240","DOIUrl":"10.1016/j.gsf.2025.102240","url":null,"abstract":"<div><div>The genesis of bonanza-style gold deposits, characterized by weight-percent-level Au enrichment, challenges conventional models of chemical transport via aqueous complexes. Through high-pressure experiments (0.5–1.5 GPa, 600–1150 °C) combined with thermodynamic modeling and transmission electron microscopy (TEM) analyses, we demonstrate that CO<sub>2</sub>-rich fluids generated by metamorphic decarbonization create overpressures exceeding ∼ 200 MPa. This initiates explosive upward migration of sulfide liquids containing Au-Ag nanoparticles (NPs) into porous peridotite at velocities up to 55.9 ± 12.9 μm/h. High-resolution TEM analyses furthermore confirm the mechanical entrainment of Au-Ag NPs within sulfides. Fractal analysis (<em>FD</em> = 1.55–1.62) of dendritic sulfide networks reveals that viscous fingering dominates fluid dynamics. We propose a unified model where gas-driven filter pressing extracts Au-bearing sulfides from subducted slabs, while viscous fingering further facilitates kilometer-scale transport through lithospheric faults. This novel mechanism bridges mantle-derived carbon fluxes with crustal mineralization, offering new insights into the formation of ultrahigh-grade gold deposits.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102240"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880277","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}
Pub Date : 2026-03-01Epub Date: 2025-11-24DOI: 10.1016/j.gsf.2025.102215
Wenchao Huangfu , Haijun Qiu , Jiading Wang , Ninglian Wang , Yang Zhang , Ya Liu , Ali Darvishi Boloorani , Mohib Ullah
Landslides have different topographic and morphological characteristics due to their different triggering mechanisms. However, the differences in the characteristics of earthquake- and rainstorm-induced landslides remain unclear. In this paper, we collect 12 cases of earthquake- and rainstorm-induced landslides around the world and reveal the differences in characteristics of the two types of landslides. By examining the geometric characteristics and location distribution of the landslides, the results show that earthquake-induced landslides tend to have larger areas, perimeter, lengths, widths, area to perimeter ratios (area/perimeter), major axis (SM), and minor axis (sm) than rainstorm-induced landslides. In addition, earthquake-induced landslides have more complex, rounded, and compact shapes than rainstorm-induced landslides. Earthquake-induced landslides are predominantly clustered near ridges, whereas rainstorm-induced landslides are predominantly clustered near valleys. The results also indicate that earthquake- and rainstorm-induced landslides mostly occur on 30°–50° and 10°–30° slopes, respectively, and both types are more likely to occur on sunny slopes. Moreover, the compactness and major axis are negatively logarithmically correlated for earthquake-induced landslides, while they are negatively exponentially correlated for rainstorm-induced landslides. Additional earthquake- and rainstorm-induced landslide events have verified the reliability and extensibility of the research conclusions. This work is beneficial for the management of landslide hazards and the effective implementation of landslide prediction and risk assessment.
{"title":"Topographic and morphological effects of global earthquake- and rainstorm-induced landslides","authors":"Wenchao Huangfu , Haijun Qiu , Jiading Wang , Ninglian Wang , Yang Zhang , Ya Liu , Ali Darvishi Boloorani , Mohib Ullah","doi":"10.1016/j.gsf.2025.102215","DOIUrl":"10.1016/j.gsf.2025.102215","url":null,"abstract":"<div><div>Landslides have different topographic and morphological characteristics due to their different triggering mechanisms. However, the differences in the characteristics of earthquake- and rainstorm-induced landslides remain unclear. In this paper, we collect 12 cases of earthquake- and rainstorm-induced landslides around the world and reveal the differences in characteristics of the two types of landslides. By examining the geometric characteristics and location distribution of the landslides, the results show that earthquake-induced landslides tend to have larger areas, perimeter, lengths, widths, area to perimeter ratios (area/perimeter), major axis (<em>S</em><sub>M</sub>), and minor axis (<em>s</em><sub>m</sub>) than rainstorm-induced landslides. In addition, earthquake-induced landslides have more complex, rounded, and compact shapes than rainstorm-induced landslides. Earthquake-induced landslides are predominantly clustered near ridges, whereas rainstorm-induced landslides are predominantly clustered near valleys. The results also indicate that earthquake- and rainstorm-induced landslides mostly occur on 30°–50° and 10°–30° slopes, respectively, and both types are more likely to occur on sunny slopes. Moreover, the compactness and major axis are negatively logarithmically correlated for earthquake-induced landslides, while they are negatively exponentially correlated for rainstorm-induced landslides. Additional earthquake- and rainstorm-induced landslide events have verified the reliability and extensibility of the research conclusions. This work is beneficial for the management of landslide hazards and the effective implementation of landslide prediction and risk assessment.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102215"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645721","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}
Pub Date : 2026-03-01Epub Date: 2025-11-30DOI: 10.1016/j.gsf.2025.102220
Jiejie Li , Emma Black , Christopher Miller , Kunning Tang , Peyman Mostaghimi , Andrew Feitz , T.David Waite , Ryan T. Armstrong
Natural hydrogen (H2) generated by the reaction of ultramafic rocks with water is increasingly recognized as a promising low-carbon energy resource with the analysis of rock mineralogy and structural characteristics recognized to play a crucial role in assessing its subsurface generation potential. In this study, micro-computed tomography (micro-CT), micro-X-ray fluorescence spectroscopy (micro-XRF), X-ray diffraction (XRD), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) are employed to analyze the density, elemental distribution, mineral composition, and surface spatial relationships of an ultramafic rock sample. In addition, deep learning-based image analysis is employed to achieve high-resolution mineral phase characterization, enabling quantitative analysis of the spatial distribution, co-location, and contact surfaces of the mineral phases. Focusing on a particular sample that was considered a likely initiator of hydrogen generation due to its mineral contents, our results indicate that the sample is primarily composed of Fe-Mg-rich olivine and silicate minerals, with most olivine phases being Mg-rich forsterite or mixtures of forsterite and Fe-rich fayalite. The sample also contains Fe-S sulfides and high-density metal-enriched phases, including Ni-rich phases that may enhance the H2-generating potential of serpentinization reactions. These findings highlight the mineralogical complexity of the studied ultramafic rock and the value of integrating compositional and spatial data when considering the potential of particular materials for hydrogen generation. The integrated analytical approach proposed in this study provides new insights and practical tools for evaluating the hydrogen generation potential associated with subsurface serpentinization in ultramafic rock.
{"title":"Multi-modal characterization of ultramafic rock: Precursors relevant to serpentinization and hydrogen generation","authors":"Jiejie Li , Emma Black , Christopher Miller , Kunning Tang , Peyman Mostaghimi , Andrew Feitz , T.David Waite , Ryan T. Armstrong","doi":"10.1016/j.gsf.2025.102220","DOIUrl":"10.1016/j.gsf.2025.102220","url":null,"abstract":"<div><div>Natural hydrogen (H<sub>2</sub>) generated by the reaction of ultramafic rocks with water is increasingly recognized as a promising low-carbon energy resource with the analysis of rock mineralogy and structural characteristics recognized to play a crucial role in assessing its subsurface generation potential. In this study, micro-computed tomography (micro-CT), micro-X-ray fluorescence spectroscopy (micro-XRF), X-ray diffraction (XRD), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) are employed to analyze the density, elemental distribution, mineral composition, and surface spatial relationships of an ultramafic rock sample. In addition, deep learning-based image analysis is employed to achieve high-resolution mineral phase characterization, enabling quantitative analysis of the spatial distribution, co-location, and contact surfaces of the mineral phases. Focusing on a particular sample that was considered a likely initiator of hydrogen generation due to its mineral contents, our results indicate that the sample is primarily composed of Fe-Mg-rich olivine and silicate minerals, with most olivine phases being Mg-rich forsterite or mixtures of forsterite and Fe-rich fayalite. The sample also contains Fe-S sulfides and high-density metal-enriched phases, including Ni-rich phases that may enhance the H<sub>2</sub>-generating potential of serpentinization reactions. These findings highlight the mineralogical complexity of the studied ultramafic rock and the value of integrating compositional and spatial data when considering the potential of particular materials for hydrogen generation. The integrated analytical approach proposed in this study provides new insights and practical tools for evaluating the hydrogen generation potential associated with subsurface serpentinization in ultramafic rock.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102220"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748580","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}
Pub Date : 2026-03-01Epub Date: 2026-01-08DOI: 10.1016/j.gsf.2026.102251
Daniel Carrizo , Mohamed Beraaouz , Mohamed Hssaisoune , Laura Sánchez-García , Olga Prieto-Ballesteros , Víctor Parro
During the Ediacaran Period (635–538.8 Ma), the photosynthetic activity due to cyanobacterial communities and early photosynthetic eukaryotes prompted the wide oxygenation of the terrestrial atmosphere. Biogeochemical evidence of this type of communities and activity in different terrestrial environments is very scarce. In this work, we search for lipid biomarkers and their carbon specific isotopic composition in stromatolites from an Ediacaran volcanic alkaline lake in the Anti-Atlas Mountains, in Morocco. Molecular analysis reveals the presence of n-alkanes, isoprenoids, hopanes and steranes in the Amane-n’Tourhart stromatolites, with compound-specific δ13C values for n-alkanes and isoprenoids within the range of autotrophic organisms using the Calvin-Benson-Bassham cycle. Results from contamination controls and laboratory tests attest for the indigeneity and syngenicity of the detected biomarkers. In addition, molecular and isotopic analysis of hydrocarbons allows for the recognition of phototrophic activity from the prokaryotic and eukaryotic communities developed in this extreme alkaline lake in anoxic conditions. These unique results shed light on a key Period in the evolution of life on Earth in the particular region of Amane-n’Tourhart. The set of molecular and isotopic biomarkers detected in the Amane-n’Tourhart stromatolites supports the presence of some of the first complex organisms (i.e. fungi and early animals) and the relevance of the most prominent metabolism in present day biology (i.e. Calvin cycle), and expands the catalog of biomarkers preserved from that geological Period to reconstruct its paleobiology.
{"title":"Contrasted detection of lipid biomarkers in Ediacaran stromatolites from Amane-n’Tourhart in the Moroccan Anti-Atlas","authors":"Daniel Carrizo , Mohamed Beraaouz , Mohamed Hssaisoune , Laura Sánchez-García , Olga Prieto-Ballesteros , Víctor Parro","doi":"10.1016/j.gsf.2026.102251","DOIUrl":"10.1016/j.gsf.2026.102251","url":null,"abstract":"<div><div>During the Ediacaran Period (635<em>–</em>538.8 Ma), the photosynthetic activity due to cyanobacterial communities and early photosynthetic eukaryotes prompted the wide oxygenation of the terrestrial atmosphere. Biogeochemical evidence of this type of communities and activity in different terrestrial environments is very scarce. In this work, we search for lipid biomarkers and their carbon specific isotopic composition in stromatolites from an Ediacaran volcanic alkaline lake in the Anti-Atlas Mountains, in Morocco. Molecular analysis reveals the presence of <em>n</em>-alkanes, isoprenoids, hopanes and steranes in the Amane-n’Tourhart stromatolites, with compound-specific <em>δ</em><sup>13</sup>C values for <em>n</em>-alkanes and isoprenoids within the range of autotrophic organisms using the Calvin-Benson-Bassham cycle. Results from contamination controls and laboratory tests attest for the indigeneity and syngenicity of the detected biomarkers. In addition, molecular and isotopic analysis of hydrocarbons allows for the recognition of phototrophic activity from the prokaryotic and eukaryotic communities developed in this extreme alkaline lake in anoxic conditions. These unique results shed light on a key Period in the evolution of life on Earth in the particular region of Amane-n’Tourhart. The set of molecular and isotopic biomarkers detected in the Amane-n’Tourhart stromatolites supports the presence of some of the first complex organisms (i.e. fungi and early animals) and the relevance of the most prominent metabolism in present day biology (i.e. Calvin cycle), and expands the catalog of biomarkers preserved from that geological Period to reconstruct its paleobiology.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102251"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972982","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}
Pub Date : 2026-03-01Epub Date: 2025-12-29DOI: 10.1016/j.gsf.2025.102243
Yu-Min Shi , Fu-Ping Gao , Ning Wang , Wen-Gang Qi , Jian-Tao Liu , Jun-Qin Wang
An innovative framework for correlating physical–mechanical properties of deep-sea sediments is established through a comprehensive database integrating microstructural, mineralogical, and geotechnical data from over 300 samples. Advanced cold field emission SEM analyses reveal unique flocculated-laminated microstructures dominated by organic components and smectite-rich clay minerals. Microstructural parameters and relationships between macroscopic and microscopic characteristics are further examined, which enhances the fundamental understanding of the correlations between physical and mechanical properties. Statistical analyses demonstrate strong interdependencies among water content, buoyant unit weight, and void ratio, confirming their equivalence as physical descriptors. Crucially, conventional terrestrial soil models show limited applicability for predicting undrained shear strength in deep-sea environments, particularly underestimating strength parameters by neglecting sediment sensitivity and liquidity index. Through multiple nonlinear regression and the construction of multivariate distribution, predictive models are developed incorporating buoyant unit weight, liquidity index, and sensitivity as key governing factors, achieving superior accuracy compared to existing methods. This investigation advances the understanding of physical–mechanical properties of deep-sea sediments, thus providing critical insights for assessing subsea geo-hazards.
{"title":"Microstructure-driven prediction of undrained shear strength of deep-sea sediments: A multivariate approach bridging physical–mechanical properties","authors":"Yu-Min Shi , Fu-Ping Gao , Ning Wang , Wen-Gang Qi , Jian-Tao Liu , Jun-Qin Wang","doi":"10.1016/j.gsf.2025.102243","DOIUrl":"10.1016/j.gsf.2025.102243","url":null,"abstract":"<div><div>An innovative framework for correlating physical–mechanical properties of deep-sea sediments is established through a comprehensive database integrating microstructural, mineralogical, and geotechnical data from over 300 samples. Advanced cold field emission SEM analyses reveal unique flocculated-laminated microstructures dominated by organic components and smectite-rich clay minerals. Microstructural parameters and relationships between macroscopic and microscopic characteristics are further examined, which enhances the fundamental understanding of the correlations between physical and mechanical properties. Statistical analyses demonstrate strong interdependencies among water content, buoyant unit weight, and void ratio, confirming their equivalence as physical descriptors. Crucially, conventional terrestrial soil models show limited applicability for predicting undrained shear strength in deep-sea environments, particularly underestimating strength parameters by neglecting sediment sensitivity and liquidity index. Through multiple nonlinear regression and the construction of multivariate distribution, predictive models are developed incorporating buoyant unit weight, liquidity index, and sensitivity as key governing factors, achieving superior accuracy compared to existing methods. This investigation advances the understanding of physical–mechanical properties of deep-sea sediments, thus providing critical insights for assessing subsea geo-hazards.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102243"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972984","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}
Pub Date : 2026-03-01Epub Date: 2025-12-19DOI: 10.1016/j.gsf.2025.102239
Xinyu Long , Wenliang Xu , Feng Wang , Chenyang Sun , Jie Tang
Stable potassium (K) isotopes are emerging as a novel geochemical tracer for investigating magmatic differentiation and source characteristics. This study presents the K isotopic analyses of Neoarchean–Paleoproterozoic granitoids from the Xing’an Massif, a key microcontinent within the eastern Central Asian Orogenic Belt (CAOB), providing new insights into the granitoid petrogenesis and early crustal evolution of this accretionary orogen. The 2568 Ma peraluminous A-type monzogranite exhibits significantly heavier δ41K values (−0.22‰ to −0.05‰) compared to the range of the upper continental crust. Subduction zones can effectively transfer heavy K isotopic signature to the mantle wedge through slab-derived fluids/melts. The monzogranite could be formed through co-melting and mixing of previously metasomatized mantle materials and recycled supracrustal metapelites, followed by high degree of fractional crystallization in a post-collisional extensional setting. Although both the 1881 Ma monzogranite and 1843 Ma syenogranite share geochemical affinities with adakites, their markedly different K isotopic compositions and distinct geochemical fingerprints point to substantial heterogeneity within their source regions. The 1881 Ma monzogranite shows more pronounced heavy K isotopic enrichment (δ41K = −0.39‰ to −0.18‰) and elevated zircon δ18O values (7.28‰–8.93‰). These features demonstrate the incorporation of mantle components metasomatized by melts of altered oceanic crust (with elevated δ41K values) into the lower crustal source. In contrast, the 1843 Ma syenogranite displays ultrapotassic affinity with lighter K isotopic compositions (δ41K = −0.45‰ to −0.38‰) and strongly negative zircon εHf(t) values (−11.5 to −10.2), indicating a thickened lower crustal source with contributions from ancient supracrustal sediments. Collectively, K isotopic compositions of the ca. 1.8 Ga adakitic granitoids overcome the limitations of traditional geochemical and isotopic proxies in revealing the complex granite petrogenesis, and they potentially provide evidence for a cycle of plate tectonics, from oceanic crust alteration at mid-ocean ridges through slab subduction to continental collision. The onset of plate tectonics promoted remelting of Archean igneous and sedimentary crust, generating abundant peraluminous and potassic granitoids during the late Archean to Paleoproterozoic and driving crustal compositional maturation in this accretionary orogen.
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Pub Date : 2026-01-01Epub Date: 2025-11-25DOI: 10.1016/j.gsf.2025.102214
Xiajie Zhai , Lijuan Cui , Wei Li , Xinsheng Zhao , Chenxi Liu , Hua Ma , Mingshuo Xiong
The Yellow River provides an important foundation for the sustainable development of Chinese civilization. Compared with the upper part (dominated by the Tibetan Plateau) and the lower part (represented by the Yellow River Delta), the central part of the Yellow River Basin (encompassing most of the Loess Plateau) is the most arid and exhibits the most complex relationship between humans and nature. The Chinese government is continuously promoting the protection and management of the ecological environment in the central part of the Yellow River Basin, as it is related to the country’s food security and people’s health, biodiversity conservation and sustainable socio-economic development. However, the distribution patterns and evolution of key ecological elements in the region, which are important determinants of ecosystem productivity and health, have yet to be revealed. This study focused on three key ecological elements, namely, macronutrients (sediment organic carbon, SOC, total nitrogen, TN and total phosphorous, TP), heavy metals (Cu, Ni, Pb, Zn, Cr, Cd, Hg, and As) and microplastics, and aimed to systematically elucidate the change patterns of their concentrations and compositions in sediments from the mainstem of the Yellow River and neighboring typical lakes. The results revealed that the TN content was mostly greater than the SOC content in the sediments from the mainstem of the Yellow River. Moreover, the TN, SOC and heavy metal concentrations increased significantly as a result of agricultural cultivation. Among the six typical lakes, the highest concentrations of both macronutrients and heavy metals were observed in sediment samples from Mingcui Lake (MC; an urban wetland), followed by those in sediment samples from Wuliangsuhai Lake (WLS; surrounded by agricultural fields). Among the heavy metals, the concentrations of Zn and Cr were highest. The abundance of microplastics in the sediments from the mainstream of the Yellow River ranged from 233 to 3333 items kg−1, while the abundance of microplastics in lake sediments ranged from 967 to 1556 items kg−1. The other characteristics of microplastics were consistent, including the concentration of microplastic particles within the 0.2–2 mm range. The main colors of the sampled microplastics were blue, transparent, and gray-black. In addition, rayon accounted for the highest proportion among all polymer types, followed by PET and PE + PP. In general, the amount of the above three environmental elements is closely correlated with the intensity of human activities such as agriculture and urbanization. Stronger correlations were obtained between the concentrations of macronutrients and heavy metals. This study systematically reveals the change patterns of key ecological elements in the study area and advances the understanding of environmental changes, ecosystem evolution and sustainable development in the Yellow River Basin.
{"title":"Spatial distributions of macronutrients, heavy metals and microplastics in surface sediments of the mainstem and lakes in the middle part of the Yellow River Basin","authors":"Xiajie Zhai , Lijuan Cui , Wei Li , Xinsheng Zhao , Chenxi Liu , Hua Ma , Mingshuo Xiong","doi":"10.1016/j.gsf.2025.102214","DOIUrl":"10.1016/j.gsf.2025.102214","url":null,"abstract":"<div><div>The Yellow River provides an important foundation for the sustainable development of Chinese civilization. Compared with the upper part (dominated by the Tibetan Plateau) and the lower part (represented by the Yellow River Delta), the central part of the Yellow River Basin (encompassing most of the Loess Plateau) is the most arid and exhibits the most complex relationship between humans and nature. The Chinese government is continuously promoting the protection and management of the ecological environment in the central part of the Yellow River Basin, as it is related to the country’s food security and people’s health, biodiversity conservation and sustainable socio-economic development. However, the distribution patterns and evolution of key ecological elements in the region, which are important determinants of ecosystem productivity and health, have yet to be revealed. This study focused on three key ecological elements, namely, macronutrients (sediment organic carbon, SOC, total nitrogen, TN and total phosphorous, TP), heavy metals (Cu, Ni, Pb, Zn, Cr, Cd, Hg, and As) and microplastics, and aimed to systematically elucidate the change patterns of their concentrations and compositions in sediments from the mainstem of the Yellow River and neighboring typical lakes. The results revealed that the TN content was mostly greater than the SOC content in the sediments from the mainstem of the Yellow River. Moreover, the TN, SOC and heavy metal concentrations increased significantly as a result of agricultural cultivation. Among the six typical lakes, the highest concentrations of both macronutrients and heavy metals were observed in sediment samples from Mingcui Lake (MC; an urban wetland), followed by those in sediment samples from Wuliangsuhai Lake (WLS; surrounded by agricultural fields). Among the heavy metals, the concentrations of Zn and Cr were highest. The abundance of microplastics in the sediments from the mainstream of the Yellow River ranged from 233 to 3333 items kg<sup>−1</sup>, while the abundance of microplastics in lake sediments ranged from 967 to 1556 items kg<sup>−1</sup>. The other characteristics of microplastics were consistent, including the concentration of microplastic particles within the 0.2–2 mm range. The main colors of the sampled microplastics were blue, transparent, and gray-black. In addition, rayon accounted for the highest proportion among all polymer types, followed by PET and PE + PP. In general, the amount of the above three environmental elements is closely correlated with the intensity of human activities such as agriculture and urbanization. Stronger correlations were obtained between the concentrations of macronutrients and heavy metals. This study systematically reveals the change patterns of key ecological elements in the study area and advances the understanding of environmental changes, ecosystem evolution and sustainable development in the Yellow River Basin.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 1","pages":"Article 102214"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690947","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}
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