Pub Date : 2026-01-02DOI: 10.1016/j.gsf.2025.102246
Runhong Zhang , Haoran Chang , Anthony Teck Chee Goh , Weixin Sun
The analysis of apparent earth pressure (AEP) in braced excavations in soft clay environments demands advanced methodologies to address complex soil-structure interactions and nonlinear parameter interdependencies. Traditional empirical approaches often oversimplify these critical factors, compromising design reliability. This study introduces a data-driven framework that merges machine learning (ML) techniques with finite element (FE) modeling to enhance AEP prediction and interpretation. A novel Dynamic Time Warping (DTW)-based KMeans clustering algorithm is employed to classify AEP distributions, validated against FE simulations and field-monitored data. By integrating FE modeling with data-driven clustering, the framework generates refined apparent pressure diagrams (APDs) tailored to Tsc-specific conditions, outperforming conventional Terzaghi-Peck and CIRIA diagrams. Results demonstrate that ML models reduce prediction errors compared to empirical approaches. This work underscores the transformative potential of ML in advancing geotechnical engineering, offering a paradigm for robust excavation design in heterogeneous soil strata.
{"title":"Data-driven apparent earth pressure prediction in braced excavations in stratified soft-stiff clay deposits","authors":"Runhong Zhang , Haoran Chang , Anthony Teck Chee Goh , Weixin Sun","doi":"10.1016/j.gsf.2025.102246","DOIUrl":"10.1016/j.gsf.2025.102246","url":null,"abstract":"<div><div>The analysis of apparent earth pressure (AEP) in braced excavations in soft clay environments demands advanced methodologies to address complex soil-structure interactions and nonlinear parameter interdependencies. Traditional empirical approaches often oversimplify these critical factors, compromising design reliability. This study introduces a data-driven framework that merges machine learning (ML) techniques with finite element (FE) modeling to enhance AEP prediction and interpretation. A novel Dynamic Time Warping (DTW)-based KMeans clustering algorithm is employed to classify AEP distributions, validated against FE simulations and field-monitored data. By integrating FE modeling with data-driven clustering, the framework generates refined apparent pressure diagrams (APDs) tailored to <em>T</em><sub>sc</sub>-specific conditions, outperforming conventional Terzaghi-Peck and CIRIA diagrams. Results demonstrate that ML models reduce prediction errors compared to empirical approaches. This work underscores the transformative potential of ML in advancing geotechnical engineering, offering a paradigm for robust excavation design in heterogeneous soil strata.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102246"},"PeriodicalIF":8.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972987","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 : 2026-01-02DOI: 10.1016/j.gsf.2026.102247
Yi Wang, Guang Yuan
Natural resource exploitation—particularly the extraction of minerals and related primary commodities—continues to shape patterns of economic expansion, structural transformation, and environmental strain across developing regions. Understanding how these resource dynamics interact with broader economic structures and institutional conditions is crucial for designing sustainable development pathways. In this context, productive capacity, economic policy uncertainty, and ecological pressure emerge as central dimensions through which the environmental consequences of development can be assessed. This study investigates the impact of the productive capacity index and economic policy uncertainty on the ecological footprint of 33 Asian developing countries from 2000 to 2022, explicitly considering mineral resource dependence, foreign direct investment, and economic growth as control variables. Using advanced econometric techniques—including slope heterogeneity diagnostics, the Westerlund cointegration test, Moment Quantile Regression (MMQR), and Kernel-Based Regularized Least Squares (KRLS)—the analysis reveals that productive capacity, policy uncertainty, and natural resources (including minerals) are negatively associated with the ecological footprint, suggesting that stronger institutional and productive structures mitigate environmental pressures. By contrast, economic growth and foreign direct investment are positively related to ecological footprint, highlighting the environmental trade-offs of rapid expansion and external capital flows. The findings underscore the need for sustainable mineral resource management and integrated policy frameworks that align productive capacity with environmental stewardship. The study concludes that resource-rich economies must balance mineral exploitation with long-term energy and environmental strategies, ensuring that productivity gains do not come at the cost of ecological degradation.
{"title":"Natural resource exploitation and productive capacity as drivers of ecological footprint: The roles of technology and economic policy uncertainty","authors":"Yi Wang, Guang Yuan","doi":"10.1016/j.gsf.2026.102247","DOIUrl":"10.1016/j.gsf.2026.102247","url":null,"abstract":"<div><div>Natural resource exploitation—particularly the extraction of minerals and related primary commodities—continues to shape patterns of economic expansion, structural transformation, and environmental strain across developing regions. Understanding how these resource dynamics interact with broader economic structures and institutional conditions is crucial for designing sustainable development pathways. In this context, productive capacity, economic policy uncertainty, and ecological pressure emerge as central dimensions through which the environmental consequences of development can be assessed. This study investigates the impact of the productive capacity index and economic policy uncertainty on the ecological footprint of 33 Asian developing countries from 2000 to 2022, explicitly considering mineral resource dependence, foreign direct investment, and economic growth as control variables. Using advanced econometric techniques—including slope heterogeneity diagnostics, the Westerlund cointegration test, Moment Quantile Regression (MMQR), and Kernel-Based Regularized Least Squares (KRLS)—the analysis reveals that productive capacity, policy uncertainty, and natural resources (including minerals) are negatively associated with the ecological footprint, suggesting that stronger institutional and productive structures mitigate environmental pressures. By contrast, economic growth and foreign direct investment are positively related to ecological footprint, highlighting the environmental trade-offs of rapid expansion and external capital flows. The findings underscore the need for sustainable mineral resource management and integrated policy frameworks that align productive capacity with environmental stewardship. The study concludes that resource-rich economies must balance mineral exploitation with long-term energy and environmental strategies, ensuring that productivity gains do not come at the cost of ecological degradation.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102247"},"PeriodicalIF":8.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034335","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 : 2025-12-29DOI: 10.1016/j.gsf.2025.102244
Meihong Ma , Ting Wang , Jianhua Yang , Zhuoran Chen , Jinqi Wang , Ronghua Liu , Xiaoyi Miao
Increasingly frequent extreme climate events have intensified urban flood risks, underscoring the urgent need for accurate, interpretable assessment methodologies. This study establishes an explainable artificial intelligence (XAI) framework for flood risk assessment in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), integrating the LISFLOOD-FP hydrodynamic model with Gradient Boosting Decision Tree (GBDT). To resolve model opacity, Local Interpretable Model-agnostic Explanations (LIME) quantifies the contributions of critical disaster-inducing indicators. The framework achieves over 91% predictive accuracy, revealing a 1.33% expansion of very high-risk zones and a 3.80% increase in high-risk areas under the 100-year flood scenario, with the most affected cities including Guangzhou, Shenzhen, Zhuhai, and Foshan. LIME-based interpretability analysis under this scenario underscores the dominant influence of hydrological and topographic variables, with FD (flood depth), SD (submerge duration), and DEM (Digital Elevation Model) collectively contributing over 60% of the total explanatory contribution. This XAI approach significantly enhances flood risk prediction precision, delivering actionable insights for evidence-based resilience planning across the GBA.
{"title":"XAI-driven flood risk assessment: Integrating machine learning and hydrological model","authors":"Meihong Ma , Ting Wang , Jianhua Yang , Zhuoran Chen , Jinqi Wang , Ronghua Liu , Xiaoyi Miao","doi":"10.1016/j.gsf.2025.102244","DOIUrl":"10.1016/j.gsf.2025.102244","url":null,"abstract":"<div><div>Increasingly frequent extreme climate events have intensified urban flood risks, underscoring the urgent need for accurate, interpretable assessment methodologies. This study establishes an explainable artificial intelligence (XAI) framework for flood risk assessment in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), integrating the LISFLOOD-FP hydrodynamic model with Gradient Boosting Decision Tree (GBDT). To resolve model opacity, Local Interpretable Model-agnostic Explanations (LIME) quantifies the contributions of critical disaster-inducing indicators. The framework achieves over 91% predictive accuracy, revealing a 1.33% expansion of very high-risk zones and a 3.80% increase in high-risk areas under the 100-year flood scenario, with the most affected cities including Guangzhou, Shenzhen, Zhuhai, and Foshan. LIME-based interpretability analysis under this scenario underscores the dominant influence of hydrological and topographic variables, with FD (flood depth), SD (submerge duration), and DEM (Digital Elevation Model) collectively contributing over 60% of the total explanatory contribution. This XAI approach significantly enhances flood risk prediction precision, delivering actionable insights for evidence-based resilience planning across the GBA.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102244"},"PeriodicalIF":8.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972983","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 : 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":"2025-12-29","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 : 2025-12-21DOI: 10.1016/j.gsf.2025.102241
Jie Yan , Qingfei Wang , Fei Xia , Jiayong Pan , Fujun Zhong , Renyu Zeng , Zhibai Chen , Chaogui Hu , Chengbiao Leng , Mingxing Ling
The South China Block (SCB) is recognized as one of the most significant uranium deposit clusters in the world, characterized by its complex genetic types and geodynamic drives. Based on host rocks, uranium deposits in the SCB can be categorized into three primary types, exhibiting a trend from black shale-related deposits in the west, to granite-related, and ultimately to volcanic-related deposits toward the eastern margin of the SCB. We identify that three types of deposits are primarily distributed within or along margins of ancient crustal domains. Geochronological data reveals large-scale uranium mineralization occurred predominantly during Cretaceous and Paleogene periods. Uranium mineralization was mainly controlled by structures in the extensional setting, developed particularly at subsidiary faults, lithological (unconformity, intrusion contacts) and physicochemical interfaces. Uranium mineralization is dominantly characterized by medium to low ore-forming temperature with pitchblende as the main industrial mineral, and with silicification, carbonatization, hematitization, fluoritization and chloritization as common alteration. Isotopic studies show that sulfur sourced from host rocks, while carbon isotopes distinguish mantle-derived signatures in granite- and volcanic-related deposits from primarily sedimentary organic matter sources in black shale-related deposit. Uranium was mainly contributed by host rocks which are relatively U-fertile geological formations. Magmatic and/or mantle-derived mineralizing agents promote the activation and migration of uranium in host rocks, and accelerate the accumulation of U in ore-forming fluids. Our study suggests that the coupling of shallow and deep-seated energy and conduit system within a crustal extension setting, together with the pre-enrichment of uranium in basement and host rocks, controlled the formation of uranium deposits in the SCB.
{"title":"Genetic types, mineralization styles, and geodynamic drive of uranium deposits in the South China Block","authors":"Jie Yan , Qingfei Wang , Fei Xia , Jiayong Pan , Fujun Zhong , Renyu Zeng , Zhibai Chen , Chaogui Hu , Chengbiao Leng , Mingxing Ling","doi":"10.1016/j.gsf.2025.102241","DOIUrl":"10.1016/j.gsf.2025.102241","url":null,"abstract":"<div><div>The South China Block (SCB) is recognized as one of the most significant uranium deposit clusters in the world, characterized by its complex genetic types and geodynamic drives. Based on host rocks, uranium deposits in the SCB can be categorized into three primary types, exhibiting a trend from black shale-related deposits in the west, to granite-related, and ultimately to volcanic-related deposits toward the eastern margin of the SCB. We identify that three types of deposits are primarily distributed within or along margins of ancient crustal domains. Geochronological data reveals large-scale uranium mineralization occurred predominantly during Cretaceous and Paleogene periods. Uranium mineralization was mainly controlled by structures in the extensional setting, developed particularly at subsidiary faults, lithological (unconformity, intrusion contacts) and physicochemical interfaces. Uranium mineralization is dominantly characterized by medium to low ore-forming temperature with pitchblende as the main industrial mineral, and with silicification, carbonatization, hematitization, fluoritization and chloritization as common alteration. Isotopic studies show that sulfur sourced from host rocks, while carbon isotopes distinguish mantle-derived signatures in granite- and volcanic-related deposits from primarily sedimentary organic matter sources in black shale-related deposit. Uranium was mainly contributed by host rocks which are relatively U-fertile geological formations. Magmatic and/or mantle-derived mineralizing agents promote the activation and migration of uranium in host rocks, and accelerate the accumulation of U in ore-forming fluids. Our study suggests that the coupling of shallow and deep-seated energy and conduit system within a crustal extension setting, together with the pre-enrichment of uranium in basement and host rocks, controlled the formation of uranium deposits in the SCB.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102241"},"PeriodicalIF":8.9,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880278","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 : 2025-12-21DOI: 10.1016/j.gsf.2025.102242
R. Amirthavarshini , A.I. Mohamed Shamil , P.S. Aswin Raaj , G. Kanimozhi
Landslides trigger high loss of life, damage to property and infrastructure, particularly in sensitive terrains like Kerala, India. Real-time monitoring and forecasting remain difficult due to rugged topography and low connectivity in remote terrain. The current work depicts a low-power, long-range IoT framework for monitoring applications utilizing LoRaWAN for data transmission and machine learning for forecasting. Soil moisture, accelerometer–gyroscope (MPU6050), humidity (DHT22), and simulated piezometer sensor nodes periodically store important slope-stability parameters. The sensed data are transmitted across LoRa to a base hub where the site-specific machine learning program analyzes the data in real time. Experimental results reveal soil moisture increasing from 2% to 10%, humidity from 89.8% to 91.5%, pore water pressure from 0.2 kPa to 0.5 kPa, and fluctuating accelerometer during simulated slope failure—variables closely related to landslide initiating factors. Machine learning outcomes reveal the ExtraTrees Classifier obtained 87.0% accuracy and gave the best results relative to different algorithms. The system provides automatic SOS messages to the Geological Survey of India (GSI) and executes site-based alarms for communities at risk. In comparison with the current GSM or satellite-based systems, the presented method provides longer-range communications and reduced energy consumption, along with quicker responses. The work presents a field-applicable and scalable solution for landslide risk management and disaster preparedness applications.
{"title":"Harnessing LoRa for real-time landslide monitoring and early alerts in Kerala’s terrain","authors":"R. Amirthavarshini , A.I. Mohamed Shamil , P.S. Aswin Raaj , G. Kanimozhi","doi":"10.1016/j.gsf.2025.102242","DOIUrl":"10.1016/j.gsf.2025.102242","url":null,"abstract":"<div><div>Landslides trigger high loss of life, damage to property and infrastructure, particularly in sensitive terrains like Kerala, India. Real-time monitoring and forecasting remain difficult due to rugged topography and low connectivity in remote terrain. The current work depicts a low-power, long-range IoT framework for monitoring applications utilizing LoRaWAN for data transmission and machine learning for forecasting. Soil moisture, accelerometer–gyroscope (MPU6050), humidity (DHT22), and simulated piezometer sensor nodes periodically store important slope-stability parameters. The sensed data are transmitted across LoRa to a base hub where the site-specific machine learning program analyzes the data in real time. Experimental results reveal soil moisture increasing from 2% to 10%, humidity from 89.8% to 91.5%, pore water pressure from 0.2 kPa to 0.5 kPa, and fluctuating accelerometer during simulated slope failure—variables closely related to landslide initiating factors. Machine learning outcomes reveal the ExtraTrees Classifier obtained 87.0% accuracy and gave the best results relative to different algorithms. The system provides automatic SOS messages to the Geological Survey of India (GSI) and executes site-based alarms for communities at risk. In comparison with the current GSM or satellite-based systems, the presented method provides longer-range communications and reduced energy consumption, along with quicker responses. The work presents a field-applicable and scalable solution for landslide risk management and disaster preparedness applications.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102242"},"PeriodicalIF":8.9,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972985","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 : 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":"2025-12-21","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 : 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.
{"title":"Potassium isotopic evidence for the petrogenesis of Precambrian granitoids and implications for early crustal evolution of the accretionary orogen","authors":"Xinyu Long , Wenliang Xu , Feng Wang , Chenyang Sun , Jie Tang","doi":"10.1016/j.gsf.2025.102239","DOIUrl":"10.1016/j.gsf.2025.102239","url":null,"abstract":"<div><div>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 <em>δ</em><sup>41</sup>K 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 (<em>δ</em><sup>41</sup>K = −0.39‰ to −0.18‰) and elevated zircon <em>δ</em><sup>18</sup>O values (7.28‰–8.93‰). These features demonstrate the incorporation of mantle components metasomatized by melts of altered oceanic crust (with elevated <em>δ</em><sup>41</sup>K values) into the lower crustal source. In contrast, the 1843 Ma syenogranite displays ultrapotassic affinity with lighter K isotopic compositions (<em>δ</em><sup>41</sup>K = −0.45‰ to −0.38‰) and strongly negative zircon <em>ε</em><sub>Hf</sub>(<em>t</em>) 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.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102239"},"PeriodicalIF":8.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034334","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 : 2025-12-13DOI: 10.1016/j.gsf.2025.102238
Xu Han , Yu Huang , Xiaoyan Jin , Liuyuan Zhao , Chung Yee Kwok
Slope engineering is an uncertain, dynamic, and complex nonlinear spatiotemporal system with time delays. High-fidelity prediction of slope seismic stability has long been a formidable challenge due to the inherent randomness and uncertainty associated with ground motion, geo-material properties, complex topography, etc. Traditional numerical modelling always takes a simplified model by forcedly ignoring those uncertainties, thus failing to replicate precisely the intricate nonlinear interactions between factors that affect slope instability. Notably, the newly emerging deep learning methods have the capability of handling multiple factors with uncertainties. However, these methods heavily rely on extensive and comprehensive sensor data, while arranging sensors at certain important positions is sometimes unachievable. Therefore, we propose a multi-task deep transfer learning (MT-DTL) framework in this study to enhance the prediction accuracy of slope seismic response especially in data-limited conditions. The dynamic response at the locations without sufficient accessible sensor data can be effectively predicted with a newly developed algorithm. To collect the necessary sensor data, we conduct a series of physics experiments with the world’s largest multifunctional shaking table equipment. We demonstrate the efficacy and accuracy of our approach on the shaking-table datasets through comparisons with traditional machine learning (ML) methods. Our findings reveal that the MT-DTL framework can improve the confidence level of prediction results (within 5%) from the highest 86.4% by the optimal traditional ML methods to 92.7%, achieving comparable results with two-thirds fewer data. Additionally, a single response example showed that the trained deep transfer learning model has significantly improved the computational efficiency (0.018 – 0.019 s) compared to the dynamic finite element calculation with GeoStudio (10 min). This highlights its potential for integration into geo-hazards digital twin systems, facilitating rapid risk analysis based on real-time monitoring data.
{"title":"Multi-task deep transfer learning for complicated seismic dynamic response prediction in slope systems","authors":"Xu Han , Yu Huang , Xiaoyan Jin , Liuyuan Zhao , Chung Yee Kwok","doi":"10.1016/j.gsf.2025.102238","DOIUrl":"10.1016/j.gsf.2025.102238","url":null,"abstract":"<div><div>Slope engineering is an uncertain, dynamic, and complex nonlinear spatiotemporal system with time delays. High-fidelity prediction of slope seismic stability has long been a formidable challenge due to the inherent randomness and uncertainty associated with ground motion, geo-material properties, complex topography, etc. Traditional numerical modelling always takes a simplified model by forcedly ignoring those uncertainties, thus failing to replicate precisely the intricate<!--> <!-->nonlinear interactions between factors that affect slope instability. Notably, the newly emerging deep learning methods have the capability of handling multiple factors with uncertainties. However, these methods heavily rely on extensive and comprehensive sensor data, while arranging sensors at certain important positions is sometimes unachievable. Therefore, we propose a multi-task deep transfer learning (MT-DTL) framework in this study to enhance the prediction accuracy of slope seismic response especially in data-limited conditions. The dynamic response at the locations without sufficient accessible sensor data can be effectively predicted with a newly developed algorithm. To collect the necessary sensor data, we conduct a series of physics experiments with the world’s largest multifunctional shaking table equipment. We demonstrate the efficacy and accuracy of our approach on the shaking-table datasets through comparisons with traditional machine learning (ML) methods. Our findings reveal that the MT-DTL framework can improve the confidence level of prediction results (within 5%) from the highest 86.4% by the optimal traditional ML methods to 92.7%, achieving comparable results with two-thirds fewer data. Additionally, a single response example showed that the trained deep transfer learning model has significantly improved the computational efficiency (0.018 – 0.019 s) compared to the dynamic finite element calculation with GeoStudio (10 min). This highlights its potential for integration into geo-hazards digital twin systems, facilitating rapid risk analysis based on real-time monitoring data.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102238"},"PeriodicalIF":8.9,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837350","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 : 2025-12-09DOI: 10.1016/j.gsf.2025.102233
Boyang Wang , Dengfu Yuan , Jingjing Li , Shichao Li , Fei Xiao , Shansi Tian , Mengjing Yin , Jianguo Yang
Micro-nano fractures serve as the bridge connecting nanopores and macro-fractures. The unclear understanding of their developmental characteristics and controlling factors significantly hinders the large-scale, efficient development of continental shale oil. To address this, this study employs the entropy weight method to establish an evaluation model for fracture development strength that comprehensively considers fracture number, average width, areal density, and areal porosity. Additionally, topology is introduced to evaluate fracture connectivity. The research clarifies the differences in micro-nano fracture developmental characteristics and primary controlling factors among different lithofacies and elucidates the impact of micro-nano fracture development on pore structure and hydrocarbon accumulation in Gulong shale. The results indicate that the HQS (high-organic laminated felsic shale) lithofacies exhibits high micro-nano fracture development strength and connectivity, yielding the highest comprehensive evaluation index. The HCS (high-organic laminated mixed shale) shows high development strength but low connectivity, resulting in a secondary comprehensive evaluation index. Higher organic matter content correlates with greater fracture development strength; clay mineral content controls the characteristics of nano-fracture development; felsic mineral content positively influences fracture connectivity. The development of micro-nano fractures not only enhances macropore content and average pore size but also effectively connects pores of various scales, increasing the effectiveness of the pore-fracture system. Lithofacies with low fracture connectivity (primarily HCS) exhibit more complex pore structures. Shale oil in such lithofacies mainly accumulates via a self-sealing model, making it difficult to form complex fracture networks during hydraulic fracturing and hindering efficient development. Conversely, the HQS lithofacies demonstrates optimal pore-fracture connectivity, favorable oil content, and represents the most favorable lithofacies for Gulong shale oil development. These findings contribute to the optimization of sweet-spot intervals for shale oil exploration in the study area.
{"title":"Fine characterization of micro-nano fractures and analysis of network connectivity: Mechanistic controls on hydrocarbon enrichment in shale reservoirs","authors":"Boyang Wang , Dengfu Yuan , Jingjing Li , Shichao Li , Fei Xiao , Shansi Tian , Mengjing Yin , Jianguo Yang","doi":"10.1016/j.gsf.2025.102233","DOIUrl":"10.1016/j.gsf.2025.102233","url":null,"abstract":"<div><div>Micro-nano fractures serve as the bridge connecting nanopores and macro-fractures. The unclear understanding of their developmental characteristics and controlling factors significantly hinders the large-scale, efficient development of continental shale oil. To address this, this study employs the entropy weight method to establish an evaluation model for fracture development strength that comprehensively considers fracture number, average width, areal density, and areal porosity. Additionally, topology is introduced to evaluate fracture connectivity. The research clarifies the differences in micro-nano fracture developmental characteristics and primary controlling factors among different lithofacies and elucidates the impact of micro-nano fracture development on pore structure and hydrocarbon accumulation in Gulong shale. The results indicate that the HQS (high-organic laminated felsic shale) lithofacies exhibits high micro-nano fracture development strength and connectivity, yielding the highest comprehensive evaluation index. The HCS (high-organic laminated mixed shale) shows high development strength but low connectivity, resulting in a secondary comprehensive evaluation index. Higher organic matter content correlates with greater fracture development strength; clay mineral content controls the characteristics of nano-fracture development; felsic mineral content positively influences fracture connectivity. The development of micro-nano fractures not only enhances macropore content and average pore size but also effectively connects pores of various scales, increasing the effectiveness of the pore-fracture system. Lithofacies with low fracture connectivity (primarily HCS) exhibit more complex pore structures. Shale oil in such lithofacies mainly accumulates via a self-sealing model, making it difficult to form complex fracture networks during hydraulic fracturing and hindering efficient development. Conversely, the HQS lithofacies demonstrates optimal pore-fracture connectivity, favorable oil content, and represents the most favorable lithofacies for Gulong shale oil development. These findings contribute to the optimization of sweet-spot intervals for shale oil exploration in the study area.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102233"},"PeriodicalIF":8.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797718","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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