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地学前缘(英文版)
地学前缘(英文版)

莫宣学

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1674-9871

geofrontier@cugb.edu.cn

010-82322283,82321855

100083

北京市海淀区学院路29号中国地质大学（北京）期刊中心

地学前缘(英文版)/Journal GEOSCIENCE FRONTIERSCSCDCSTPCD北大核心SCI
查看更多>>GEOSCIENCE FRONTIERS (GSF) is a quarterly journal that publishes in English significant original research articles and high quality reviews of recent advances in all fields of Earth Sciences — including stratigraphy and paleontology, mineralogy and petrology, economic geology and minerals and fuel exploration, structural geology, lithospheric tectonics, environmental and engineering geology, hydrogeology, astrogeology, marine geology, and geophysics and geochemistry. Technical papers, case histories, reviews, and discussions are welcomed.
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    Machine learning and tectonic setting determination:Bridging the gap between Earth scientists and data scientists

    Pratchaya TakaewJianhong Cecilia XiaLuc S.Doucet
    347-361页
    查看更多>>摘要：Technological progress and the rapid increase in geochemical data often create bottlenecks in many stud-ies,because current methods are designed using limited number of data and cannot handle large data-sets.In geoscience,tectonic discrimination illustrates this issue,using geochemical analyses to define tectonic settings when most of the geological record is missing,which is the case for most of the older portion of the Earth's crust.Basalts are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings,and their chemical compositions can be an effective way to understand tectonics-related mantle processes.However,the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique solutions.Also,weathering,erosion,and oro-genic processes can modify the chemical composition of basalts and eliminate or obscure other comple-mentary geotectonic records.To address those limitations,supervised machine learning techniques(a part of artificial intelligence)are being utilized more often as a tool to analyze multidimensional data-sets and statistically process data to tackle big data challenges.This contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine learning.Deep learning,espe-cially Convolutional Neural Network(CNN)is the most accurate approach.However,it requires a large dataset and considerable processing time,and the gain of accuracy can be at the expense of interpretabil-ity.Therefore,this study designed guidelines for data pre-processing,tectonic setting classification and objectively evaluating the model performance.We also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research,aimed at closing the divide between earth scientists and data scientists.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Munabulake ophiolitic mélange:Implication for the evolution history of the north branch of the Proto-Tethys ocean

    Changfeng LiuWencan LiuBaoying YeZixian Zhao...
    363-382页
    查看更多>>摘要：One of the ophiolites that record the Proto-Tethys Ocean's episodic closure is the Munabulake ophiolitic mélange,which is situated in the middle of the Kunlun-Qaidam and Altun-Qilian blocks.Detailed field mapping revealed that the Munabulake ophiolitic mélange comprises local(ultramafic rocks,basalts,andesites,gabbros,diorites,and plagiogranites)and exotic(marble,gneiss,schist,and amphibolite)blocks,many of which are in the schist matrix(Qimantage Group).Based on geochronological,geochem-ical,and petrological observations,the mafic rocks in the Munabulake ophiolitic mélange can be catego-rized into three categories:498-Ma OIB-like gabbros,468-Ma Hawaiian alkaline basalt-like dolerite and pillow basaltic slices,and 428 Ma massive SSZ-like ultramafic rocks.The 501-452 Ma I-type granites exhibit arc affinities due to the oceanic crust subduction.These findings,along with spatial relationships,suggest that the Early Paleozoic ophiolite complex,island arc rocks,and accretionary complex generated as an intra-oceanic arc system as a result of obduction of the south Altun Ocean's onto the Central Altun block within a north-directed subduction event.A dextral strike-slip was evident throughout the Early Paleozoic oceanic crust subduction based on the whole set of planar and linear structural data,and the subduction polarity was likely to the north.According to the ophiolitic mélange's youngest rocks and the existence of 413 Ma granite dykes that are widely exposed in the Munabulake ophiolitic mélange,the Munabulake ophiolitic mélange was most likely emplaced during the Middle Silurian.This Munabulake ophiolitic mélange is similar in age and petrochemical characteristics to the other ophiolites in the South Altun subduction-collision belt,indicating that the Manabulak ophiolite mélange is a west-ward extension of the Apa-Mangya subduction-collision belt,which formed during the northward sub-duction of the South Altun Ocean slab during the Early Paleozoic.Thus,the final closing time of the South Altun Ocean is between 413 and 428 Ma.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Factor analysis of recent major heatwaves in East Asia

    Arim YoonJeongwon KimJooyeop LeeHyun Min Sung...
    383-392页
    查看更多>>摘要：Heatwaves(HWs)present a major hazard to our society and more extreme heatwaves are expected with future climatic changes.Hence,it is important to improve our understanding of the underlying processes that drive HWs,in order to boost our socioeconomic-ecological resilience.In this study,we quantified the influences of key driving factors(large-scale atmospheric circulation,soil moisture,and sea surface tem-perature)and their synergies on recent heatwaves in East Asia.We conducted a factor separation analysis for three recent HW events by constraining the key factors in the regional Weather Research and Forecasting model with their climatologies or pseudo-observations in different combinations.Our study showed distinct spatial variations in the HW-controlling factors in East Asia.The synergistic interaction of large-scale circulation and soil moisture was the most important factors in the 2013 Chinese HW.During the 2018 HWs in Korea and Japan,the same stagnant large-scale atmospheric circulation played a dominant role in driving the HW events.The land-atmosphere coupling via soil moisture,its interaction with circulation,and SST exhibited stronger influences during the Korean HW than the Japanese HW.Our analysis also revealed temporal variations in the factors driving Korean and Chinese HWs due to typhoon passage and other multiple processes over heterogeneous surfaces(i.e.,topographically induced Foehn winds,large-scale warm advection from the warm ocean,spatial differences in soil moisture).Our find-ings suggest that future heatwave-related studies should consider interactive contributions of key fac-tors,their interplay with surface heterogeneities of complex terrain.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    From source to emplacement:The origin of leucogranites from the Sikkim-Darjeeling Himalayas,India

    Tanya SrivastavaNigel HarrisCatherine MottramKumar Batuk Joshi...
    393-410页
    查看更多>>摘要：Himalayan leucogranites are important for understanding the tectonic evolution of collision zones in gen-eral and the causes of crustal melting in the Himalayan orogen in particular.This paper aims to understand the melt source and emplacement age of the leucogranites from Sikkim in order to decipher the deep geodynamic processes of the eastern Himalayas.Zircon U-Pb analysis of the Higher Himalayan Sequence(HHS)metamorphic core reveals a prolonged period of crustal melting between＞33 Ma and ca.14 Ma.Major and trace element abundances are presented for 27 leucogranites from North Sikkim that are classified into two-mica and tourmaline leucogranite types.They are peraluminous in composition,characterized by high SiO2(70.91-74.9 wt.％),Al2O3(13.69-15.82 wt.％),and low MgO(0.13-0.74 wt.％).Elemental abundances suggest that Sikkim Himalayan leucogranites are derived from crustal melts.The two-mica leucogranites are derived from a metagreywacke source,whereas the tourmaline leucogranites are sourced from metapelitic sources,with inherited zircons indicating an HHS origin for both types.U-Pb zircon geochronology of the two mica leucogranites indicates ages of ca.19-15 Ma,consistent with crustal melting recorded in HHS gneisses from Darjeeling.Monazites from both the two-mica and tourmaline leucogranites yield a crystallization age of ca.15-14 Ma,coeval with movement on the Main Central Thrust and South Tibetan Detachment System which further provides constraints on the timing and mechanism of petrogenesis of leucogranites in the Sikkim Himalayas.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate

    Yaning XuWenxi LuZidong PanChengming Luoa...
    411-425页
    查看更多>>摘要：Groundwater contamination source identification(GCSI)is a prerequisite for contamination risk evalua-tion and efficient groundwater contamination remediation programs.The boundary condition generally is set as known variables in previous GCSI studies.However,in many practical cases,the boundary con-dition is complicated and cannot be estimated accurately in advance.Setting the boundary condition as known variables may seriously deviate from the actual situation and lead to distorted identification results.And the results of GCSI are affected by multiple factors,including contaminant source informa-tion,model parameters,boundary condition,etc.Therefore,if the boundary condition is not estimated accurately,other factors will also be estimated inaccurately.This study focuses on the unknown bound-ary condition and proposed to identify three types of unknown variables(contaminant source informa-tion,model parameters and boundary condition)innovatively.When simulation-optimization(S-O)method is applied to GCSI,the huge computational load is usually reduced by building surrogate models.However,when building surrogate models,the researchers need to select the models and optimize the hyperparameters to make the model powerful,which can be a lengthy process.The automated machine learning(AutoML)method was used to build surrogate model,which automates the model selection and hyperparameter optimization in machine learning engineering,largely reducing human operations and saving time.The accuracy of AutoML surrogate model is compared with the surrogate model used in eXtreme Gradient Boosting method(XGBoost),random forest method(RF),extra trees regressor method(ETR)and elasticnet method(EN)respectively,which are automatically selected in AutoML engineering.The results show that the surrogate model constructed by AutoML method has the best accuracy com-pared with the other four methods.This study provides reliable and strong support for GCSI.
      原文链接:
    • NETL
    • NSTL
    • 万方数据
    • 维普

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