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Data-driven apparent earth pressure prediction in braced excavations in stratified soft-stiff clay deposits 数据驱动的层状软硬粘土层支撑开挖视土压力预测
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2026-01-02 DOI: 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.
软粘土环境下支撑开挖的视土压力分析需要先进的方法来处理复杂的土-结构相互作用和非线性参数相互依赖关系。传统的经验方法往往过于简化这些关键因素,损害了设计的可靠性。本研究引入了一个数据驱动的框架,该框架将机器学习(ML)技术与有限元(FE)建模相结合,以增强AEP的预测和解释。采用一种新的基于动态时间扭曲(DTW)的KMeans聚类算法对AEP分布进行分类,并通过FE模拟和现场监测数据进行验证。通过将有限元建模与数据驱动的聚类相结合,该框架生成了针对tsc特定条件量身定制的精细表观压力图(apd),优于传统的Terzaghi-Peck图和CIRIA图。结果表明,与经验方法相比,机器学习模型减少了预测误差。这项工作强调了机器学习在推进岩土工程方面的变革潜力,为异质土壤地层的稳健挖掘设计提供了范例。
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引用次数: 0
Natural resource exploitation and productive capacity as drivers of ecological footprint: The roles of technology and economic policy uncertainty 自然资源开发和生产能力作为生态足迹的驱动因素:技术和经济政策不确定性的作用
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2026-01-02 DOI: 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.
自然资源开发——尤其是矿产和相关初级商品的开采——继续影响着发展中地区的经济扩张、结构转型和环境压力。了解这些资源动态如何与更广泛的经济结构和体制条件相互作用,对于设计可持续发展途径至关重要。在这种情况下,生产能力、经济政策的不确定性和生态压力成为可以评估发展的环境后果的中心方面。本文以矿产资源依存度、外商直接投资和经济增长为控制变量,考察了2000 - 2022年亚洲33个发展中国家的生产能力指数和经济政策不确定性对生态足迹的影响。利用先进的计量经济学技术——包括斜坡异质性诊断、Westerlund协整检验、矩分位数回归(MMQR)和基于核的正则化最小二乘(KRLS)——分析表明,生产能力、政策不确定性和自然资源(包括矿物)与生态足迹呈负相关,这表明更强的制度和生产结构减轻了环境压力。相比之下,经济增长和外国直接投资与生态足迹呈正相关,突出了快速扩张和外部资本流动的环境权衡。调查结果强调需要可持续的矿物资源管理和使生产能力与环境管理相结合的综合政策框架。该研究的结论是,资源丰富的经济体必须在矿产开采与长期能源和环境战略之间取得平衡,确保生产力的提高不会以生态退化为代价。
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引用次数: 0
XAI-driven flood risk assessment: Integrating machine learning and hydrological model 基于人工智能的洪水风险评估:整合机器学习和水文模型
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-29 DOI: 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.
日益频繁的极端气候事件加剧了城市洪水风险,因此迫切需要准确、可解释的评估方法。本研究将LISFLOOD-FP水动力模型与梯度提升决策树(GBDT)相结合,建立了粤港澳大湾区洪水风险评估的可解释人工智能(XAI)框架。为了解决模型的不透明性,局部可解释模型不可知解释(LIME)量化了关键诱发灾害指标的贡献。该框架的预测准确率超过91%,在百年一遇洪水情景下,非常高风险区域扩大1.33%,高风险区域增加3.80%,受影响最严重的城市包括广州、深圳、珠海和佛山。在这种情景下,基于lime的可解释性分析强调了水文和地形变量的主导影响,FD(洪水深度)、SD(淹没持续时间)和DEM(数字高程模型)共同贡献了超过60%的总解释贡献。这种XAI方法显著提高了洪水风险预测的精度,为整个大湾区的循证韧性规划提供了可操作的见解。
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引用次数: 0
Microstructure-driven prediction of undrained shear strength of deep-sea sediments: A multivariate approach bridging physical–mechanical properties 深海沉积物不排水剪切强度的微观结构驱动预测:一种连接物理-力学特性的多元方法
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-29 DOI: 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.
通过整合来自300多个样品的微观结构、矿物学和岩土数据的综合数据库,建立了一个创新的深海沉积物物理力学特性关联框架。先进的冷场发射SEM分析揭示了独特的絮凝层状微观结构,主要由有机成分和富蒙脱石粘土矿物组成。进一步研究了微观结构参数以及宏观和微观特征之间的关系,从而增强了对物理和力学性能之间相关性的基本理解。统计分析表明,含水量、浮力单位重量和空隙比之间存在很强的相互依赖性,证实了它们作为物理描述符的等效性。重要的是,传统的陆地土壤模型在预测深海环境不排水剪切强度方面适用性有限,特别是由于忽略了沉积物敏感性和流动性指数而低估了强度参数。通过多元非线性回归和多元分布的构建,建立了以浮力单位重量、流动性指数和灵敏度为主要控制因素的预测模型,与现有方法相比,预测精度更高。这项研究促进了对深海沉积物物理力学特性的理解,从而为评估海底地质灾害提供了重要的见解。
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引用次数: 0
Genetic types, mineralization styles, and geodynamic drive of uranium deposits in the South China Block 华南地块铀矿床成因类型、成矿样式及地球动力学驱动
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-21 DOI: 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.
华南地块以其复杂的成因类型和地球动力学驱动为特征,是世界上最重要的铀矿床群之一。根据铀矿床的寄主岩类型,可将南海东部铀矿床划分为3种主要类型,呈现出从西部的黑色页岩型铀矿床到东部边缘的花岗岩型铀矿床,最后向东部边缘的火山型铀矿床发展的趋势。我们发现三种类型的矿床主要分布在古地壳域内或沿边缘。地质年代学资料显示,大规模铀矿化主要发生在白垩纪和古近纪。铀矿化主要受伸展构造控制,主要发育于次级断裂、岩性(不整合面、侵入接触面)和物化界面。铀矿化以中低成矿温度为主要特征,以沥青铀矿为主要工业矿物,常见蚀变为硅化、碳化、赤铁矿、氟矿化和绿泥矿化。同位素研究表明,硫来自寄主岩,而碳同位素区分花岗岩和火山相关矿床的幔源特征与黑色页岩相关矿床的主要沉积有机质来源。铀矿主要由含铀量相对丰富的寄主岩贡献。岩浆和(或)幔源矿化剂促进了铀在宿主岩中的活化和迁移,加速了铀在成矿流体中的富集。研究认为,地壳伸展背景下的深、浅能量耦合和管道系统,以及基底和寄主岩中铀的预富集控制了南海铀矿床的形成。
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引用次数: 0
Harnessing LoRa for real-time landslide monitoring and early alerts in Kerala’s terrain 利用LoRa进行喀拉拉邦地形的实时滑坡监测和预警
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-21 DOI: 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.
山体滑坡会造成巨大的生命损失、财产损失和基础设施破坏,尤其是在印度喀拉拉邦这样的敏感地区。由于崎岖的地形和偏远地区的低连通性,实时监测和预测仍然很困难。目前的工作描述了一个低功耗、远程物联网框架,用于监控应用程序,利用LoRaWAN进行数据传输和机器学习进行预测。土壤湿度、加速度计-陀螺仪(MPU6050)、湿度(DHT22)和模拟的压力计传感器节点定期存储重要的边坡稳定性参数。感知到的数据通过LoRa传输到一个基础集线器,在那里特定站点的机器学习程序实时分析数据。试验结果表明,在模拟边坡破坏过程中,土壤湿度从2%增加到10%,湿度从89.8%增加到91.5%,孔隙水压力从0.2 kPa增加到0.5 kPa,波动加速度计等变量与滑坡发生因素密切相关。机器学习结果显示,ExtraTrees分类器获得了87.0%的准确率,并且相对于不同的算法给出了最好的结果。该系统向印度地质调查局(GSI)提供自动SOS信息,并为处于危险中的社区执行基于现场的警报。与目前的GSM或基于卫星的系统相比,所提出的方法提供了更远距离的通信和更低的能耗,以及更快的响应。这项工作为滑坡风险管理和备灾应用提供了一个适用于现场和可扩展的解决方案。
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引用次数: 0
Transport of colloidal Au-bearing nanoparticles driven by metamorphic decarbonization 变质脱碳驱动胶体含金纳米颗粒的输运
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-21 DOI: 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.
以金富集为特征的富矿型金矿床的成因挑战了传统的通过水络合物进行化学输运的模式。通过高压实验(0.5-1.5 GPa, 600-1150°C),结合热力学模型和透射电镜(TEM)分析,我们证明了变质脱碳产生的富含co2的流体产生超过~ 200 MPa的超压。这引发了含有Au-Ag纳米颗粒(NPs)的硫化液体以55.9±12.9 μm/h的速度向上迁移到多孔橄榄岩中。高分辨率TEM分析进一步证实了Au-Ag NPs在硫化物中的机械夹带。枝状硫化物网络的分形分析(FD = 1.55 ~ 1.62)表明,黏性指指在流体动力学中占主导地位。我们提出了一个统一的模型,即气体驱动的压滤从俯冲板块中提取含金硫化物,而粘性指移进一步促进了通过岩石圈断层的千米尺度运输。这一新的机制将地幔源碳通量与地壳成矿作用联系起来,为超品位金矿床的形成提供了新的认识。
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引用次数: 0
Potassium isotopic evidence for the petrogenesis of Precambrian granitoids and implications for early crustal evolution of the accretionary orogen 前寒武纪花岗岩类岩石成因的钾同位素证据及其对增生造山带早期地壳演化的意义
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-19 DOI: 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.
稳定钾同位素作为一种新的地球化学示踪剂,正逐渐成为研究岩浆分异和来源特征的重要手段。本文对中亚造山带东部重要微大陆——兴安地块新太古代-古元古代花岗岩进行了钾同位素分析,为该增生造山带的花岗岩成因和早期地壳演化提供了新的认识。2568 Ma过铝a型二长花岗岩的δ41K值明显高于上陆地壳(- 0.22‰~ - 0.05‰)。俯冲带可以通过板块衍生的流体/熔体有效地将重K同位素特征转移到地幔楔上。二长花岗岩可能是在前交代的地幔物质与再循环的壳上变质岩共融混合后,在碰撞后的伸展环境中发生了高度的分离结晶作用。尽管1881 Ma二长花岗岩和1843 Ma正长花岗岩与埃达岩具有地球化学上的相似性,但它们明显不同的钾同位素组成和不同的地球化学指纹表明它们的来源区域具有明显的非均质性。1881 Ma二长花岗岩重K同位素富集(δ41K = - 0.39‰~ - 0.18‰),锆石δ18O值升高(7.28‰~ 8.93‰)。这些特征表明,变质洋壳(δ41K值升高)熔体交代的地幔成分被纳入下地壳源。而1843 Ma正长花岗岩则表现出较轻的K同位素(δ41K = - 0.45‰~ - 0.38‰)和较强的负锆石εHf(t)值(- 11.5 ~ - 10.2)的超古典亲和性,表明下地壳源区增厚,并有古表壳上沉积物的贡献。总的来说,约1.8 Ga阿达克岩花岗岩的钾同位素组成克服了传统地球化学和同位素指标在揭示复杂花岗岩岩石成因方面的局限性,并可能为板块构造循环提供证据,从洋中脊的洋壳蚀变到板块俯冲再到大陆碰撞。板块构造的发生促进了太古宙火成岩和沉积地壳的重熔,在晚太古宙至古元古代形成了丰富的过铝质和钾质花岗岩,推动了该增生造山带地壳成分的成熟。
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引用次数: 0
Multi-task deep transfer learning for complicated seismic dynamic response prediction in slope systems 斜坡系统复杂地震动力响应预测的多任务深度迁移学习
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-13 DOI: 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.
边坡工程是一个不确定的、动态的、复杂的、具有时滞的非线性时空系统。由于地面运动、地质材料性质、复杂地形等因素的随机性和不确定性,边坡地震稳定性的高保真预测一直是一项艰巨的挑战。传统的数值模拟总是采用简化的模型,强行忽略了这些不确定性因素,从而无法精确地复制影响边坡失稳因素之间复杂的非线性相互作用。值得注意的是,新兴的深度学习方法具有处理多个不确定因素的能力。然而,这些方法严重依赖于广泛而全面的传感器数据,而在某些重要位置布置传感器有时是无法实现的。因此,本研究提出了一个多任务深度迁移学习(MT-DTL)框架,以提高边坡地震反应的预测精度,特别是在数据有限的条件下。本文提出的一种新算法可以有效地预测传感器数据不足位置的动态响应。为了收集必要的传感器数据,我们使用世界上最大的多功能振动台设备进行了一系列物理实验。通过与传统机器学习(ML)方法的比较,我们证明了我们的方法在振动台数据集上的有效性和准确性。我们的研究结果表明,MT-DTL框架可以将预测结果的置信度(5%以内)从最优传统ML方法的最高86.4%提高到92.7%,在减少三分之二数据的情况下获得可比结果。此外,单个响应示例表明,与GeoStudio动态有限元计算(10 min)相比,训练后的深度迁移学习模型的计算效率显著提高(0.018 - 0.019 s)。这突出了它与地质灾害数字孪生系统集成的潜力,促进了基于实时监测数据的快速风险分析。
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引用次数: 0
Fine characterization of micro-nano fractures and analysis of network connectivity: Mechanistic controls on hydrocarbon enrichment in shale reservoirs 微纳裂缝精细表征及网络连通性分析:页岩储层油气富集的机理控制
IF 8.9 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-09 DOI: 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.
微纳裂缝是连接纳米孔和宏观裂缝的桥梁。对陆相页岩油的发育特征和控制因素认识不清,严重阻碍了陆相页岩油的大规模高效开发。针对这一问题,本文采用熵权法建立了综合考虑裂缝数、平均宽度、面密度、面孔隙度的裂缝发育强度评价模型。此外,还引入了拓扑学来评估裂缝连通性。研究明确了不同岩相间微纳裂缝发育特征的差异及主控因素,阐明了微纳裂缝发育对古龙页岩孔隙结构和油气成藏的影响。结果表明,HQS(高有机质层状长英质页岩)岩相具有较高的微纳裂缝发育强度和连通性,综合评价指标最高。高有机质层状混合页岩发育强度高,但连通性低,形成二级综合评价指标。有机质含量越高,裂缝发育强度越大;黏土矿物含量控制着纳米裂缝发育特征;长硅矿物含量正影响裂缝连通性。微纳裂缝的发育不仅提高了大孔隙含量和平均孔径,而且有效连接了不同尺度的孔隙,提高了孔隙-裂缝系统的有效性。裂缝连通性低的岩相(主要是HCS)孔隙结构更为复杂。该岩相页岩油主要以自封闭模式聚集,水力压裂时难以形成复杂的裂缝网络,阻碍了高效开发。相反,HQS岩相孔缝连通性最佳,含油量有利,是古龙页岩油开发的最有利岩相。研究结果为研究区页岩油甜点层段的优选提供了理论依据。
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Geoscience frontiers
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