Geoscience frontiers最新文献

{"title":"Genetic types, mineralization styles, and geodynamic drive of uranium deposits in the South China Block","authors":"Jie Yan ,&nbsp;Qingfei Wang ,&nbsp;Fei Xia ,&nbsp;Jiayong Pan ,&nbsp;Fujun Zhong ,&nbsp;Renyu Zeng ,&nbsp;Zhibai Chen ,&nbsp;Chaogui Hu ,&nbsp;Chengbiao Leng ,&nbsp;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}

{"title":"Harnessing LoRa for real-time landslide monitoring and early alerts in Kerala’s terrain","authors":"R. Amirthavarshini ,&nbsp;A.I. Mohamed Shamil ,&nbsp;P.S. Aswin Raaj ,&nbsp;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}

{"title":"Transport of colloidal Au-bearing nanoparticles driven by metamorphic decarbonization","authors":"Zhenjiang Wang ,&nbsp;Shaorui Zhao ,&nbsp;Jingbo Li ,&nbsp;Yanfei Zhang ,&nbsp;Chao Wang ,&nbsp;Dan Li ,&nbsp;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 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 (<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}

{"title":"Potassium isotopic evidence for the petrogenesis of Precambrian granitoids and implications for early crustal evolution of the accretionary orogen","authors":"Xinyu Long ,&nbsp;Wenliang Xu ,&nbsp;Feng Wang ,&nbsp;Chenyang Sun ,&nbsp;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>⁴¹K 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>⁴¹K = −0.39‰ to −0.18‰) and elevated zircon <em>δ</em>¹⁸O 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>⁴¹K values) into the lower crustal source. In contrast, the 1843 Ma syenogranite displays ultrapotassic affinity with lighter K isotopic compositions (<em>δ</em>⁴¹K = −0.45‰ to −0.38‰) and strongly negative zircon <em>ε</em>_Hf(<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}

{"title":"Multi-task deep transfer learning for complicated seismic dynamic response prediction in slope systems","authors":"Xu Han ,&nbsp;Yu Huang ,&nbsp;Xiaoyan Jin ,&nbsp;Liuyuan Zhao ,&nbsp;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}

{"title":"Fine characterization of micro-nano fractures and analysis of network connectivity: Mechanistic controls on hydrocarbon enrichment in shale reservoirs","authors":"Boyang Wang ,&nbsp;Dengfu Yuan ,&nbsp;Jingjing Li ,&nbsp;Shichao Li ,&nbsp;Fei Xiao ,&nbsp;Shansi Tian ,&nbsp;Mengjing Yin ,&nbsp;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}

{"title":"Probabilistic quantification of global drought risk amplification from temperature-enhanced evapotranspiration under climate change","authors":"Akinwale T. Ogunrinde ,&nbsp;Paul Adigun ,&nbsp;Xian Xue ,&nbsp;Koji Dairaku ,&nbsp;Sabab Ali Shah ,&nbsp;Ifeoluwa S. Adawa","doi":"10.1016/j.gsf.2025.102235","DOIUrl":"10.1016/j.gsf.2025.102235","url":null,"abstract":"<div><div>Droughts pose escalating threats to global water security, agriculture, and socioeconomic stability amid anthropogenic climate change, with projections indicating an increase in frequency, duration, and severity driven by altered precipitation patterns and amplified evaporative demand. This study introduces a probabilistic framework to quantify drought risk amplification, employing the Risk Ratio (RR) methodology integrated with extreme value theory and non-parametric inference. Utilizing multi-model ensemble (MME) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), we evaluate changes in drought characteristics—duration, frequency, and severity — via the Standardized Precipitation Evapotranspiration Index (SPEI) at 3- and 12-month timescales for near-future (NF) and far-future (FF) periods. Our analyses reveal pervasive global intensification, with over 90% of land grids exhibiting positive severity shifts under SSP5-8.5 in the FF, attributed to atmospheric evaporative demand, which accounts for approximately 44% of the trends in SPEI. Threshold-stratified RR assessments reveal nonlinear escalations at higher percentiles (P90 vs. P75), compressing the return periods of extreme events by 20%–30% under high-emission scenarios. Regional hotspots, including the Amazon basin, sub-Saharan Africa, southwestern North America, and Central Asian drylands, exhibit frequency risks that are 4-fold or more amplified, signaling transitions to chronic water stress and potential ecosystem tipping points. These findings underscore the dominance of thermodynamic drivers in drought dynamics, advocating for emissions mitigation to curtail risks by 15%–25% under moderate pathways. By addressing uncertainties in non-stationary regimes, this framework provides adaptive strategies for resilient water management, offering policymakers critical insights to mitigate cascading impacts on global food security and biodiversity in a warming world.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102235"},"PeriodicalIF":8.9,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First observation of coexisting crystalline and amorphous mineral phases in the Bhawad LL6 chondrite: Evidence from Micro-Raman spectroscopic studies","authors":"Bhaskar J. Saikia ,&nbsp;G. Parthasarathy ,&nbsp;Binoy K. Saikia ,&nbsp;Puja Bordoloi ,&nbsp;Rashmi R. Borah","doi":"10.1016/j.gsf.2025.102236","DOIUrl":"10.1016/j.gsf.2025.102236","url":null,"abstract":"<div><div>We report here for the first time the detailed spectroscopic investigations on Bhawad meteorite using micro-Raman spectroscopic and high-resolution transmission electron microscopy (HR-TEM) investigation of the Bhawad LL6 ordinary chondrite, focusing on its mineralogical composition and carbonaceous phases. Raman spectroscopy reveals crystalline silicates including olivine, pyroxene, and plagioclase, along with accessory chromite containing ≤20% of Al. Carbonaceous material exhibits broad <em>I</em>_D (∼1336 cm⁻¹) and <em>I</em>_G (∼1587 cm⁻¹) bands with an <em>I</em>_D/<em>I</em>_G ratio of ∼1.04, indicative of disordered graphite and nanocrystalline carbon, reflecting shock-induced metamorphism. High-pressure TiO₂ polymorphs are identified by characteristic Raman modes at 146, 394, 446, and 610 cm⁻¹. HR-TEM imaging confirms the presence of nanocrystalline TiO₂ particles embedded within amorphous carbonaceous matrices, demonstrating the coexistence of crystalline and amorphous phases. The Raman spectra of the Bhawad meteorite reveal the presence of high-temperature plagioclase phases, characterized by these distinct vibrational features. This observation indicates possible quenching of the melts having feldspar components, representing the complex thermal and shock metamorphic history of the meteorite. This coexistence of crystalline and amorphous phases highlights the complex thermal and shock history of the Bhawad meteorite, revealing insights into phase transitions and structural order–disorder phase transition induced by impact processes.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102236"},"PeriodicalIF":8.9,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed dictionary learning of time-varying 3D settlements from sparse monitoring data and 2D numerical models with consideration of complex stratigraphy","authors":"Dan-Ni Zhang ,&nbsp;Hua-Ming Tian ,&nbsp;Yu Wang ,&nbsp;Chao Shi ,&nbsp;Kostas Senetakis","doi":"10.1016/j.gsf.2025.102222","DOIUrl":"10.1016/j.gsf.2025.102222","url":null,"abstract":"<div><div>Digital twins of geotechnical structures replicate their physical counterparts, such as underground spaces developed from land reclamations. These spaces often exhibit intricate three-dimensional (3D) stratigraphic distributions, including irregular and interbedded soil layers. Developing a virtual 3D model, such as finite element model (FEM), with complex stratigraphy poses significant computational challenges due to the necessity for numerous stratum voxels, high-resolution meshing, and prohibitive analysis times. Incorporating field settlement data for model updating escalates the computational burden, as repeated evaluations of 3D FEM models are required for each model updating. To address this challenge, this study develops a novel approach for efficiently predicting time-varying 3D settlement from two-dimensional (2D) numerical models with sparsely measured monitoring data. Settlements from 2D FEM analyses, which account for complex stratigraphy, are compiled within a dictionary learning framework and combined with limited monitoring data to estimate time-varying settlements at multiple 2D cross-sections. The 2D settlements are then utilized to reconstruct high-resolution 3D settlements through 3D compressive sampling (3D-CS), eliminating a need for additional numerical model evaluations when integrating new monitoring data. The proposed approach is illustrated using a reclamation project in Hong Kong, China.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102222"},"PeriodicalIF":8.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast production of large lithologic maps using lithologic sample generation strategies based on 3D spatial positioning","authors":"Tao Zhang ,&nbsp;Zhifang Zhao ,&nbsp;Min Zeng ,&nbsp;Haiying Yang","doi":"10.1016/j.gsf.2025.102217","DOIUrl":"10.1016/j.gsf.2025.102217","url":null,"abstract":"<div><div>The acquisition of spatiotemporal information for lithological mapping with timeliness, accuracy, and high precision is crucial for mineral resource exploration and geological hazard prevention. However, large-scale lithological mapping remains severely constrained by the limitations of visual interpretation in obtaining representative samples from remote sensing data and error propagation during sample collection based on existing geological maps. To address this, we propose a three-dimensional spatial dual-positioning sample generation methodology (SG-3DSD) using Sentinel-2 (S2) and Landsat 8 (L8) data on the Google Earth Engine (GEE) platform, enabling automated generation of 11 lithological class samples across the Beishan region of Gansu Province, China (covering approximately 6,000 km²). First, boundary association rules were established to reconstruct 1:200,000-scale geological maps, mitigating data acquisition biases and cartographic compilation errors. Second, principal component analysis (PCA) was performed on seven S2 spectral bands, with the first three principal components (capturing &gt; 98% information variance) constituting a 3D feature space for localized clustering. Concurrently, four L8 bands were selected through lithological spectral curve analysis to implement band ratio (BR) transformations for secondary positioning. Finally, a two-step refinement strategy was implemented to filter high-confidence samples across 11 lithological classes, balancing intraclass feature consistency and sample purity. Applying SG-3DSD-derived samples to multiple machine learning models revealed that (1) the Stacking ensemble model demonstrated superior lithological discrimination capability compared to conventional algorithms, achieving peak accuracy of 94.15% and mean F1-score of 93.87%; (2) integrating topographic data (especially Elevation) enhanced lithological positioning accuracy by 4.43% ± 1.13%; (3) PCA and BR transformations effectively enhanced lithological separability, particularly at lithological boundary zones; (4) while SG-3DSD enables efficient large-scale sample generation, it is advisable to avoid using excessively large training samples for regional-scale mapping. This methodology mitigates the weighting dependence on geological maps during sample selection and dilutes inherent cartographic error propagation, providing a novel paradigm for large-scale lithological mapping with broad application potential.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102217"},"PeriodicalIF":8.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748586","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}