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}
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