Geoscience frontiers最新文献

{"title":"Detailed in-depth mapping of the world largest anorthositic complex: Magnetic anomalies, 2.5-3D modelling and emplacement constraints of the Kunene Complex (KC), SW Angola","authors":"T. Mochales ,&nbsp;E. Merino-Martínez ,&nbsp;C. Rey-Moral ,&nbsp;A. Machadinho ,&nbsp;J. Carvalho ,&nbsp;P. Represas ,&nbsp;J.L. García-Lobón ,&nbsp;M.C. Feria ,&nbsp;R. Martín-Banda ,&nbsp;M.T. López-Bahut ,&nbsp;D. Alves ,&nbsp;E. Ramalho ,&nbsp;J. Manuel ,&nbsp;D. Cordeiro","doi":"10.1016/j.gsf.2025.102030","DOIUrl":"10.1016/j.gsf.2025.102030","url":null,"abstract":"<div><div>The Kunene Complex (KC) represents a very large Mesoproterozoic igneous body, mainly composed of anorthosites and gabbroic rocks that extends from SW Angola to NW Namibia (outcropping 18,000 km², NE-SW trend, and ca. 350 km long and up to 50 km wide). Little is known about its structure at depth. Here, we use recently acquired aerogeophysical data to accurately determine its hidden extent and to unravel its morphology at depth. These data have been interpreted and modelled to investigate the unexposed KC boundaries, reconstructing the upper crustal structure (between 0 and 15 km depth) overlain by the thin sedimentary cover of the Kalahari Basin. The modelling reveals that the KC was emplaced in the upper crust and extends in depth up to ca. 5 km, showing a lobular geometry and following a large NE-SW to NNE-SSW linear trend, presumably inherited from older Paleoproterozoic structures. The lateral continuation of the KC to the east (between 50 and 125 km) beneath the Kalahari Cenozoic sediments suggests an overall size three times the outcropping dimension (about 53,500 km²). This affirmation clearly reinforces the economic potential of this massif, related to the prospecting of raw materials and certain types of economic mineralization (Fe-Ti oxides, metallic sulphides or platinum group minerals). Up to 11 lobes have been isolated with dimensions ranging from 135.5 to 37.3 km in length and 81.9 to 20.7 km in width according to remanent bodies revealed by TMI mapping. A total volume of 65,184 km³ was calculated only for the magnetically remanent bodies of the KC. A long-lasting complex contractional regime, where large strike-slip fault systems were involved, occurred in three kinematic pulses potentially related to a change of velocity or convergence angle acting on previous Paleoproterozoic inherited sutures. The coalescent magmatic pulses can be recognized by means of magnetic anomalies, age of the bodies as well as the lineations inferred in this work: (i) Emplacement of the eastern mafic bodies and granites in a stage of significant lateral extension in a transtensional context between 1500 Ma and 1420 Ma; (ii) Migration of the mantle derived magmas westwards with deformation in a complex contractional setting with shearing structures involving western KC bodies and basement from 1415 Ma to 1340 Ma; (iii) NNW-SSE extensional structures are relocated westwards, involving mantle magmas, negative flower structures and depression that led to the formation of late Mesoproterozoic basins from 1325 Ma to 1170 Ma. Additionally, we detect several first and second order structures to place the structuring of the KC in a craton-scale context in relation to the crustal structures detected in NW Namibia.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102030"},"PeriodicalIF":8.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial heterogeneity of groundwater depths in coastal cities and their responses to multiple factors interactions by interpretable machine learning models","authors":"Yuming Mo ,&nbsp;Jing Xu ,&nbsp;Senlin Zhu ,&nbsp;Beibei Xu ,&nbsp;Jinran Wu ,&nbsp;Guangqiu Jin ,&nbsp;You-Gan Wang ,&nbsp;Ling Li","doi":"10.1016/j.gsf.2025.102033","DOIUrl":"10.1016/j.gsf.2025.102033","url":null,"abstract":"<div><div>Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities. Daily groundwater depth (GWD) data from 43 wells (2018–2022) were collected in three coastal cities in Jiangsu Province, China. Seasonal and Trend decomposition using Loess (STL) together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis (HCA). Machine learning models were developed to predict GWD, then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations (SHAP) method. Results showed that eXtreme Gradient Boosting (XGB) was superior to other models in terms of prediction performance and computational efficiency (<em>R</em>² &gt; 0.95). GWD in Yancheng and southern Lianyungang were greater than those in Nantong, exhibiting larger fluctuations. Groundwater within 5 km of the coastline was affected by tides, with more pronounced effects in agricultural areas compared to urban areas. Shallow groundwater (3–7 m depth) responded immediately (0–1 day) to rainfall, primarily influenced by farmland and topography (slope and distance from rivers). Rainfall recharge to groundwater peaked at 50% farmland coverage, but this effect was suppressed by high temperatures (&gt;30 °C) which intensified as distance from rivers increased, especially in forest and grassland. Deep groundwater (&gt;10 m) showed delayed responses to rainfall (1–4 days) and temperature (10–15 days), with GDP as the primary influence, followed by agricultural irrigation and population density. Farmland helped to maintain stable GWD in low population density regions, while excessive farmland coverage (&gt;90%) led to overexploitation. In the early stages of GDP development, increased industrial and agricultural water demand led to GWD decline, but as GDP levels significantly improved, groundwater consumption pressure gradually eased. This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102033"},"PeriodicalIF":8.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mesoproterozoic missing link between eastern Australia and China during the transition from Nuna to Rodinia?","authors":"Alexander Edgar ,&nbsp;Ioan Sanislav ,&nbsp;Paul Dirks","doi":"10.1016/j.gsf.2025.102017","DOIUrl":"10.1016/j.gsf.2025.102017","url":null,"abstract":"<div><div>We document, for the first time, Mesoproterozoic-aged, continental arc magmatism in the Tasmanides. Granitoid samples intruding the Proterozoic Cape River Metamorphics in northeast Queensland contain abundant ∼ 1200 Ma igneous zircons, with early-Paleozoic metamorphic rim overgrowths. Analytical mixing between the igneous and metamorphic zircons produces cryptic discordant analyses, but the origin of said discordance is resolved with zircon Th/U ratios. Samples of the Fat Hen Creek Complex are peraluminous, calc-alkaline, S-type granitoids, that record high-grade metamorphism and trace element mobilization. The P3 and P42 intrusions are metaluminous, calc-alkaline, I-type granodiorite, which intruded the Cape River Metamorphics, and contain trace element signatures consistent with a continental-arc setting. We propose that a Mesoproterozoic continental terrane, herein referred to as the Oakvale Province, exists as basement to the Thomson Orogen. We propose several models for the formation of the Oakvale Province, with potential links to the Tarim Block, and the Yangtze Craton, during the late-Mesoproterozoic. We propose that the Oakvale Province supplied the Tasmanides with late-Mesoproterozoic detritus, and that such detritus was not solely sourced from the Musgrave Province as previously interpreted. Finally, we interpret the oroclinal bending of Paleozoic deformation and plutonic fabrics to reflect the buried extent of the Oakvale Province, and to potentially map out the Neoproterozoic rift margin associated with Rodinia break-up.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102017"},"PeriodicalIF":8.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ocean singularity analysis and global heat flow prediction reveal anomalous bathymetry and heat flow","authors":"Yang Zhang ,&nbsp;Qiuming Cheng ,&nbsp;Tao Hong ,&nbsp;Junjie Ji","doi":"10.1016/j.gsf.2025.102013","DOIUrl":"10.1016/j.gsf.2025.102013","url":null,"abstract":"<div><div>The investigations of physical attributes of oceans, including parameters such as heat flow and bathymetry, have garnered substantial attention and are particularly valuable for examining Earth’s thermal structures and dynamic processes. Nevertheless, classical plate cooling models exhibit disparities when predicting observed heat flow and seafloor depth for extremely young and old lithospheres. Furthermore, a comprehensive analysis of global heat flow predictions and regional ocean heat flow or bathymetry data with physical models has been lacking. In this study, we employed power-law models derived from the singularity theory of fractal density to meticulously fit the latest ocean heat flow and bathymetry. Notably, power-law models offer distinct advantages over traditional plate cooling models, showcasing robust self-similarity, scale invariance, or scaling properties, and providing a better fit to observed data. The outcomes of our singularity analysis concerning heat flow and bathymetry across diverse oceanic regions exhibit a degree of consistency with the global ocean spreading rate model. In addition, we applied the similarity method to predict a higher resolution (0.1° × 0.1°) global heat flow map based on the most recent heat flow data and geological/geophysical observables refined through linear correlation analysis. Regions displaying significant disparities between predicted and observed heat flow are closely linked to hydrothermal vent fields and active structures. Finally, combining the actual bathymetry and predicted heat flow with the power-law models allows for the quantitative and comprehensive detection of anomalous regions of ocean subsidence and heat flow, which deviate from traditional plate cooling models. The anomalous regions of subsidence and heat flow show different degrees of anisotropy, providing new ideas and clues for further analysis of ocean topography or hydrothermal circulation of mid-ocean ridges.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102013"},"PeriodicalIF":8.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting strong subsequent earthquakes in Japan using an improved version of NESTORE machine learning algorithm","authors":"S. Gentili ,&nbsp;G.D. Chiappetta ,&nbsp;G. Petrillo ,&nbsp;P. Brondi ,&nbsp;J. Zhuang","doi":"10.1016/j.gsf.2025.102016","DOIUrl":"10.1016/j.gsf.2025.102016","url":null,"abstract":"<div><div>In this study, the advanced machine learning algorithm NESTORE (Next STrOng Related Earthquake) was applied to the Japan Meteorological Agency catalog (1973–2024). It calculates the probability that the aftershocks will reach or exceed a magnitude equal to the magnitude of the mainshock minus one and classifies the clusters as type A or type B, depending on whether this condition is met or not. It has been shown useful in the tests in Italy, western Slovenia, Greece, and California. Due to Japan’s high and complex seismic activity, new algorithms were developed to complement NESTORE: a hybrid cluster identification method, which uses both ETAS-based stochastic declustering and deterministic graph-based selection, and REPENESE (RElevant features, class imbalance PErcentage, NEighbour detection, SElection), an algorithm for detecting outliers in skewed class distributions, which takes in account if one class has a larger number of samples with respect to the other (class imbalance).</div><div>Trained with data from 1973 to 2004 (7 type A and 43 type B clusters) and tested from 2005 to 2023 (4 type A and 27 type B clusters), the method correctly forecasted 75% of A clusters and 96% of B clusters, achieving a precision of 0.75 and an accuracy of 0.94 six hours after the mainshock. It accurately classified the 2011 Tōhoku event cluster. Near-real-time forecasting was applied to the sequence after the April 17, 2024 M6.6 earthquake in Shikoku, correctly classifying it as a “Type B cluster”. These results highlight the potential for the forecasting of strong aftershocks in regions with high seismicity and class imbalance, as evidenced by the high recall, precision and accuracy values achieved in the test phase.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102016"},"PeriodicalIF":8.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A critical review of hurricane risk assessment models and predictive frameworks","authors":"Sameera Maha Arachchige ,&nbsp;Biswajeet Pradhan ,&nbsp;Hyuck-Jin Park","doi":"10.1016/j.gsf.2025.102012","DOIUrl":"10.1016/j.gsf.2025.102012","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure. Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard, vulnerability, exposure, and mitigation capacity, crucial for understanding and managing the risks hurricanes pose to communities. These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios. A systematic review was conducted, encompassing 94 articles, to scrutinize the structure, data inputs, assumptions, methodologies, perils modelled, and key predictors of hurricane risk. This review identified key research gaps essential for enhancing future risk assessments. The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril, commonly storm surge and flood, resulting in inadequacies in disaster resilience planning. Most risk assessments were based on hurricane frequency rather than hurricane damage, which is more insightful for policymakers. Furthermore, considering secondary indirect impacts stemming from hurricanes, including real estate market and business interruption, could enrich economic impact assessments. Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5% of studies. The top six predictive factors for hurricane risk were land use, slope, precipitation, elevation, population density, and soil texture/drainage. Another notable research gap identified was the potential of machine learning techniques in risk assessments, offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions. Existing machine learning based risk assessments leverage random forest models (42% of studies) followed by neural network models (19% of studies), with further research required to investigate diverse machine learning algorithms such as ensemble models. A further research gap is model validation, in particular assessing transferability to a new study region. Additionally, harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments. By addressing these research gaps, hurricane risk assessments can furnish invaluable insights for national policymakers, facilitating the development of robust hurricane mitigation strategies and the construction of hurricane-resilient communities. To the authors’ knowledge, this represents the first literature review specifically dedicated to quantitative hurricane risk assessments, encompassing a comparison of Multi-criteria Decision Making (MCDM), numerical models, and machine learning models. Ultimately, advancements in hurricane risk assessmen","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102012"},"PeriodicalIF":8.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of ambient geochemical and microbiological variables on the bacterial diversity in a cold seep ecosystem in North Indian Ocean","authors":"Delcy R. Nazareth ,&nbsp;Maria Judith Gonsalves ,&nbsp;Nitisha Sangodkar","doi":"10.1016/j.gsf.2025.102015","DOIUrl":"10.1016/j.gsf.2025.102015","url":null,"abstract":"<div><div>Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways. Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at these sites. One such site in the cold seep ecosystem of Krishna-Godavari basin (K-G basin) along the east coast of India, discovered in Feb 2018 at a depth of 1800 m was assessed for its bacterial diversity. The seep bacterial communities were dominated by phylum Proteobacteria (57%), Firmicutes (16%) and unclassified species belonging to the family <em>Helicobacteriaceae</em>. The surface sediments of the seep had maximum OTUs (operational taxonomic units) (2.27 × 10³) with a Shannon alpha diversity index of 8.06. In general, environmental parameters like total organic carbon (p &lt; 0.01), sulfate (p &lt; 0.001), sulfide (p &lt; 0.05) and methane (p &lt; 0.01) were responsible for shaping the bacterial community of the cold seep ecosystem in the K-G Basin. Environmental parameters play a significant role in changing the bacterial diversity richness between different cold seep environments in the oceans.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102015"},"PeriodicalIF":8.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do energy intensity, resource abundance and inequality drive energy poverty? Evidence from developing countries","authors":"Ashar Awan ,&nbsp;Mustafa Kocoglu ,&nbsp;Mohammad Subhan ,&nbsp;Mohammed Shakib ,&nbsp;Nora Yusma bte Mohamed Yusoff","doi":"10.1016/j.gsf.2025.102014","DOIUrl":"10.1016/j.gsf.2025.102014","url":null,"abstract":"<div><div>Energy poverty in developing countries is a critical issue characterized by the lack of access to modern energy services, such as electricity and clean cooking facilities, as marked in SDG 7. This study explores the correlations between energy poverty, energy intensity, resource abundance, and income inequality, as these factors have been theorized to play important roles in influencing energy poverty in developing countries. By observing that the dataset is heterogeneous across the countries and over the time frame, we use the Method of Moments Quantile Regression (MMQR) to analyze our developing countries’ data from 2000 to 2019. Our findings indicate that energy intensity is a significant factor influencing energy poverty, suggesting that higher energy consumption relative to the sample countries can exacerbate this issue. Additionally, we observe that income inequality within the sample countries is a critical determinant of energy poverty levels, highlighting the dynamics between economic disparity and access to energy resources. Interestingly, our study reveals that resource abundance acts as a blessing rather than a curse in terms of energy poverty, implying that countries rich in natural resources may have better opportunities to combat energy deprivation. Finally, we emphasize the vital role of financial markets in addressing energy poverty on a global scale, suggesting that robust financial systems can facilitate investments and innovations aimed at improving energy access for vulnerable populations. The results from the robustness supports the empirical results obtained from the main estimation. The empirical findings of the present study advance important comprehensions for policymakers to adopt energy policies that address the complex challenges of energy poverty and promote inclusive energy access.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102014"},"PeriodicalIF":8.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning of pyrite geochemistry reconstructs the multi-stage history of mineral deposits","authors":"Pengpeng Yu ,&nbsp;Yuan Liu ,&nbsp;Hanyu Wang ,&nbsp;Xi Chen ,&nbsp;Yi Zheng ,&nbsp;Wei Cao ,&nbsp;Yiqu Xiong ,&nbsp;Hongxiang Shan","doi":"10.1016/j.gsf.2025.102011","DOIUrl":"10.1016/j.gsf.2025.102011","url":null,"abstract":"<div><div>The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits; however, published models face challenges related to limited, imbalanced datasets and oversampling. In this study, the dataset was expanded to approximately 500 samples for each type, including 508 sedimentary, 573 orogenic gold, 548 sedimentary exhalative (SEDEX) deposits, and 364 volcanogenic massive sulfides (VMS) pyrites, utilizing random forest (RF) and support vector machine (SVM) methodologies to enhance the reliability of the classifier models. The RF classifier achieved an overall accuracy of 99.8%, and the SVM classifier attained an overall accuracy of 100%. The model was evaluated by a five-fold cross-validation approach with 93.8% accuracy for the RF and 94.9% for the SVM classifier. These results demonstrate the strong feasibility of pyrite classification, supported by a relatively large, balanced dataset and high accuracy rates. The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China, which has been inconclusive among SEDEX, VMS, or a SEDEX-VMS transition. Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite (Py1) and late recrystallized pyrite (Py2). The majority voting classified Py1 as the VMS type, with an accuracy of RF and SVM being 72.2% and 75%, respectively, and confirmed Py2 as an orogenic type with 74.3% and 77.1% accuracy, respectively. The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system, followed by late orogenic-type overprinting of metamorphism and deformation, which is consistent with the geological and geochemical observations. This study further emphasizes the advantages of Machine learning (ML) methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102011"},"PeriodicalIF":8.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Warm continental subduction initiated by back-arc collapse: Evidence from remote south-west Tasmania","authors":"Dillon A. Brown ,&nbsp;Martin Hand ,&nbsp;Laura J. Morrissey ,&nbsp;Justin L. Payne ,&nbsp;Andrew W. McNeill","doi":"10.1016/j.gsf.2025.102009","DOIUrl":"10.1016/j.gsf.2025.102009","url":null,"abstract":"<div><div>The Tasmanian microcontinent, situated along the East Gondwana accretionary margin during the late Neoproterozoic and early Palaeozoic, contains an unequivocal high-pressure metamorphic record comprising key information pertaining to the geodynamics of subduction along the margin. Subduction of the Tasmanian microcontinent is interpreted by some as a response to back-arc basin inversion prior to ophiolite obduction and high-pressure metamorphism during the Cambrian Tyennan Orogeny. However, thermobarometric evidence in support of such a model from rocks once positioned on the subducting continental margin is lacking. Despite occurrences of eclogite-facies mineral assemblages in the strongly deformed Tyennan Region of western Tasmania, garnet-bearing quartzofeldspathic assemblages documented in metasedimentary lithologies from the remote south-west coast of Tasmania have been interpreted as an expression of low- to moderate-pressure metamorphism. We report a strongly overprinted chlorite-quartz-garnet-bearing assemblage from the southern Tyennan Region (Nye Bay) which shows evidence for high-pressure metamorphism. Coarse-grained garnet porphyroblasts contain inclusions of kyanite, muscovite, and rutile, and yield in-situ Lu–Hf dates of c. 520 Ma. The cm-scale garnet porphyroblasts are zoned in the major and trace elements, preserving core-rim compositional gradients reflecting garnet growth up-pressure. Aided by mineral equilibria forward modelling, the garnet rim compositions and the Zr content of Cambrian rutile constrain peak metamorphic conditions of ∼ 17.5–19 kbar and ∼ 780–820 °C, equivalent to warm subduction thermal gradients between 410–470 °C/GPa. Garnet core compositions and the Ti content of quartz inclusions in the garnet cores constrain the pressures and temperatures for garnet nucleation to ∼ 6–7 kbar and ∼ 560–580 °C, corresponding to relatively high prograde thermal gradients between 800–965 °C/GPa. The thermal gradients determined from the south-west Tasmanian metamorphic record provide a direct window into the progressive evolution of the thermal state of the Cambrian subduction system, with the physical conditions of garnet nucleation potentially reflecting those of subduction initiation. The corresponding warm thermal gradients provide evidence for subduction initiation driven by the collapse of a pre-orogenic back-arc. This interpretation is consistent with an existing tectonic model for the Tyennan Orogeny which proposes a back-arc basin origin for the protoliths to the western Tasmanian sub-ophiolitic metamorphic sole.</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 3","pages":"Article 102009"},"PeriodicalIF":8.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}