Geoscience frontiers最新文献

{"title":"Lithospheric thickness controls asymmetric mantle plume spreading and metallogenesis in the Tarim Large Igneous Province–Central Asian Orogenic Belt System","authors":"Xiuhui An ,&nbsp;Zhaochong Zhang ,&nbsp;Hengxu Li ,&nbsp;Mingde Lang ,&nbsp;Ruixuan Zhang ,&nbsp;Zhiguo Cheng","doi":"10.1016/j.gsf.2026.102249","DOIUrl":"10.1016/j.gsf.2026.102249","url":null,"abstract":"<div><div>The dynamic interactions between mantle plumes and continental collision zones are still inadequately defined or poorly understood. Focusing on the Early Permian Tarim LIP and the adjacent Central Asian Orogenic Belt (CAOB), this study employs a Random Forest–based tectonic affinity prediction model (98% accuracy) to quantitatively evaluate the relative contributions of distinct mantle components (subduction-modified, asthenospheric, and plume-related) in 461 basalt samples. Combined with lithospheric thickness reconstruction via Y/Yb ratios, we demonstrate that: (1) the influence of the Tarim mantle plume extended northward into the CAOB, but was deflected into an east–west trajectory upon encountering the thick lithosphere (&gt;70 km) of the Yili Block; (2) within the orogen, ocean island basalt (OIB)-affinity anomalies (e.g., East Tianshan, Junggar) are spatially consistent with thin lithosphere zones (55–65 km), and clusters of Ni–Cu sulfide deposits; and (3) major <em>trans</em>-lithospheric faults served as preferential conduits for plume upwelling. These findings provide a “channel–barrier” model where lithospheric thickness variations control plume spreading asymmetry, with preexisting structural weaknesses regulating spatial distribution. This study establishes a methodological framework for plume identification and Ni–Cu sulfide exploration in analogous settings, with broad implications for deep Earth material cycles and lithosphere–mineralization interactions.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102249"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972844","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":"Linking Gondwana inheritance to Alpine paleogeography in the Northern Dora-Maira Massif (Western Alps)","authors":"Gianni Balestro ,&nbsp;Matthieu Roà ,&nbsp;Carlo Bertok ,&nbsp;Marco Gattiglio ,&nbsp;Stefano Ghignone ,&nbsp;Chiara Groppo ,&nbsp;Valby van Schijndel ,&nbsp;Andrea Festa","doi":"10.1016/j.gsf.2025.102245","DOIUrl":"10.1016/j.gsf.2025.102245","url":null,"abstract":"<div><div>Inherited structures in rifted continental margins strongly influence the architecture and evolution of collisional orogens. The northern Dora-Maira Massif in the Western Alps (NW Italy) preserves records of such inheritances, capturing the transition from Gondwana inheritance to Alpine convergence. New lithostratigraphic and structural data, together with U–Pb zircon dating, reveal a long-lasting tectonostratigraphic and/or magmatic evolution during (i) pre-Permian, (ii) Permian, (iii) Triassic and (iv) Jurassic time intervals. The heterogeneous Paleozoic basement consists of pre-Variscan micaschist and metabasite, and was intruded by Permian igneous bodies now corresponding to the Borgone metagranite and Luserna augen gneiss. The basement was later overlain by a Mesozoic cover made up of Lower Triassic siliciclastic sediments, a Middle to Upper Triassic carbonate platform and Lower to Middle Jurassic <em>syn</em>-rift deposits linked to the opening of the Ligurian–Piedmont Ocean Basin. Our results highlight that the Dora-Maira Massif was located within a transitional paleogeographic domain, emphasizing the role of pre-rift architecture in governing margin segmentation. Successive cycles of sedimentation, magmatism, and rifting created structural and rheological heterogeneities that may have localized strain during the Cenozoic Alpine-related overprinting. The Dora-Maira case illustrates that deep-time inherited structures and tectonostratigraphic settings continue to influence rifting, subduction, and collision, offering a broader framework for understanding the dynamics of orogens worldwide.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102245"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972986","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":"2026-03-01","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":"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":"2026-03-01","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":"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":"2026-03-01","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":"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":"2026-03-01","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":"Data-driven apparent earth pressure prediction in braced excavations in stratified soft-stiff clay deposits","authors":"Runhong Zhang ,&nbsp;Haoran Chang ,&nbsp;Anthony Teck Chee Goh ,&nbsp;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>_sc-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":"2026-03-01","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}

{"title":"XAI-driven flood risk assessment: Integrating machine learning and hydrological model","authors":"Meihong Ma ,&nbsp;Ting Wang ,&nbsp;Jianhua Yang ,&nbsp;Zhuoran Chen ,&nbsp;Jinqi Wang ,&nbsp;Ronghua Liu ,&nbsp;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":"2026-03-01","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}

{"title":"Aquifer characterization and salinization origin using unsupervised machine learning and 3D gravity inversion modeling, Siwa Oasis, Egypt","authors":"Mohamed Hamdy Eid ,&nbsp;Khouloud Jlaiel ,&nbsp;Mohamed Ayed Elbalawy ,&nbsp;Yetzabbel G. Flores ,&nbsp;Ali A. Mohieldain ,&nbsp;Tamer Nassar ,&nbsp;Mostafa R. Abukhadra ,&nbsp;Haifa A. Alqhtani ,&nbsp;Attila Kovács ,&nbsp;Péter Szűcs","doi":"10.1016/j.gsf.2026.102258","DOIUrl":"10.1016/j.gsf.2026.102258","url":null,"abstract":"<div><div>Groundwater salinization in arid oasis environments poses significant challenges for sustainable water resource management. In Siwa Oasis, Egypt, the deep Nubian Sandstone Aquifer System (NSSA) and the shallow Tertiary Carbonate Aquifer (TCA) interact through fault systems. At the same time, the potential leakage from hypersaline surface lakes creates complex hydrogeological conditions that require comprehensive characterization. Despite the critical importance of understanding aquifer connectivity and salinization processes, there remains a significant knowledge gap in the quantitative integration of multiple geophysical datasets for objective aquifer characterization and structural control identification. Traditional methods lack the spatial resolution and objective framework necessary to map lithofacies distributions and identify structural pathways controlling groundwater flow in complex multi-aquifer systems.</div><div>This study presents an integrated approach, combining machine learning clustering with gravity data analysis, to characterize the region’s aquifer systems. K-means and Self-Organizing Maps (SOM) were applied to well log data, including Gamma Ray (GR), Spontaneous Potential (SP), and resistivity (R), to delineate lithofacies. Three distinct units were identified: clean sand, shaly sand, and clay-rich facies. The SOM algorithm outperformed the clustering of K-means in accurately estimating layer thickness and resolving lithological transitions. A 3D lithofacies model revealed spatial heterogeneity within the NSSA, highlighting clean sand layers as primary groundwater extraction zones.</div><div>Gravity data analysis using upward continuation and edge-filtering techniques identified dominant NE-SW, NW-SE, and E-W lineaments controlling groundwater flow dynamics. The 3D gravity inversion model revealed density contrasts associated with structural features, providing insights into potential groundwater flow between aquifers. Spatial analysis reveals lower groundwater salinity in the southern part of the Oasis, coinciding with areas of reduced structural complexity. Higher salinity zones in central and northeastern regions show spatial correlation with gravity-derived structural systems, though causal relationships require additional validation through hydrochemical studies. This integrated approach provides critical insights for sustainable groundwater management in structurally complex arid environments.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102258"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034336","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":"Optimized inversion of Chang’e-2 gamma-ray spectrum data into heat production rate for thermal evolution study: Imbrium Basin as an example","authors":"Zhenghe Li,&nbsp;Yuyan Zhao,&nbsp;Xiaodan Tang,&nbsp;Zhiguo Meng","doi":"10.1016/j.gsf.2026.102254","DOIUrl":"10.1016/j.gsf.2026.102254","url":null,"abstract":"<div><div>The lunar surface element distribution obtained from Chang’e-2 gamma-ray spectrometer has provided new insights into the thermal activity and element migration of the Moon. To further investigate lunar thermal evolution and geological activities, the heat production rate (HPR) distribution was selected as a breakthrough. An optimized inversion method for Chang’e-2 gamma-ray spectrum data, based on multivariate statistical analysis, was developed to effectively reduce the influence of time-varying factors by improving the background estimation and subtraction process. The results validated the utility of HPR for lunar research. The global HPR distribution maps not only provide a reference for assessing the thermal state of the lunar surface, demonstrating that radiogenic heat production can be reliably studied at a global scale, but also enable detailed investigations of regional geological processes. In the Imbrium Basin, HPR clearly reflects the effects of large-scale impact events and subsequent mare volcanic activity. High-HPR materials associated with impact ejecta can be distinguished from the lower-HPR mare basalts. Furthermore, by integrating HPR data with additional geological information, it is possible to assess and partially subdivide the structure of the Imbrium Basin, providing new quantitative insights into its evolution and compositional heterogeneity.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"17 2","pages":"Article 102254"},"PeriodicalIF":8.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034338","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}