The Zengmu and Beikang basins,separated by the West Baram Line(WBL)in the southwestern South China Sea margin,display distinct geological and geophysical features.However,the nature of the basins and the WBL are debat...The Zengmu and Beikang basins,separated by the West Baram Line(WBL)in the southwestern South China Sea margin,display distinct geological and geophysical features.However,the nature of the basins and the WBL are debated.Here we explore this issue by conducting the stratigraphic and structural interpretation,faults and subsidence analysis,and lithospheric finite extension modelling using seismic data.Results show that the WBL is a trans-extensional fault zone comprising normal faults and flower structures mainly active in the Late Eocene to Early Miocene.The Zengmu Basin,to the southwest of the WBL,shows an overall synformal geometry,thick folded strata in the Late Eocene to Late Miocene(40.4-5.2 Ma),and pretty small normal faults at the basin edge,which imply that the Zengmu Basin is a foreland basin under the Luconia and Borneo collision in the Sarawak since the Eocene.Furthermore,the basin exhibits two stages of subsidence(fast in 40.4-30 Ma and slow in 30-0 Ma);but the amount of observed subsidence and heat flow are both greater than that predicted by crustal thinning.The Beikang Basin,to the NE of the WBL,consists of the syn-rift faulted sub-basins(45-16.4 Ma)and the post-rift less deformed sequences(16.4-0 Ma).The heat flow(~60 mW/m2)is also consistent with that predicted based on crustal thinning,inferring that it is a rifted basin.However,the basin shows three stages of subsidence(fast in 45-30 Ma,uplift in 30-16.4 Ma,and fast in 16.4-0 Ma).In the uplift stage,the strata were partly folded in the Late Oligocene and partly eroded in the Early Miocene,which is probably caused by the flexural bulging in response to the paleo-South China Sea subduction and the subsequent Dangerous Grounds and Borneo collision in the Sabah to the east of the WBL.展开更多
High entropy alloys(HEAs)and compositionally complex alloys(CCAs)have recently attracted great research interest because of their remarkable mechanical and physical properties.Although many useful HEAs or CCAs were re...High entropy alloys(HEAs)and compositionally complex alloys(CCAs)have recently attracted great research interest because of their remarkable mechanical and physical properties.Although many useful HEAs or CCAs were reported,the rules of phase design,if there are any,which could guide alloy screening are still an open issue.In this work,we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning(ML)algorithms.Based on the artificial neural network algorithm,we were able to derive and extract a sensitivity matrix from the ML modeling,which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase,such as solid solution,intermetallic,or amorphous phase.Furthermore,we explored the use of an extended set of new design parameters,which had not been considered before,for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule,we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system.The outcomes of our experiments agree reasonably well with our predictions,which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs.展开更多
基金Supported by the Youth Innovation Promotion Association CASthe National Key Research and Development Program of China(No.2021YFC3100604)+5 种基金the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(No.GML2019ZD0205)the Guangzhou Municipal Science and Technology Program(No.201904010285)the K.C.Wong Education Foundation(No.GJTD-2018-13)the Hainan Key Laboratory of Marine Geological Resources and Environment(No.HNHYDZZYHJKF003)the China Geological Survey(No.DD20190378)the National Natural Science Foundation of China(No.42076077)。
文摘The Zengmu and Beikang basins,separated by the West Baram Line(WBL)in the southwestern South China Sea margin,display distinct geological and geophysical features.However,the nature of the basins and the WBL are debated.Here we explore this issue by conducting the stratigraphic and structural interpretation,faults and subsidence analysis,and lithospheric finite extension modelling using seismic data.Results show that the WBL is a trans-extensional fault zone comprising normal faults and flower structures mainly active in the Late Eocene to Early Miocene.The Zengmu Basin,to the southwest of the WBL,shows an overall synformal geometry,thick folded strata in the Late Eocene to Late Miocene(40.4-5.2 Ma),and pretty small normal faults at the basin edge,which imply that the Zengmu Basin is a foreland basin under the Luconia and Borneo collision in the Sarawak since the Eocene.Furthermore,the basin exhibits two stages of subsidence(fast in 40.4-30 Ma and slow in 30-0 Ma);but the amount of observed subsidence and heat flow are both greater than that predicted by crustal thinning.The Beikang Basin,to the NE of the WBL,consists of the syn-rift faulted sub-basins(45-16.4 Ma)and the post-rift less deformed sequences(16.4-0 Ma).The heat flow(~60 mW/m2)is also consistent with that predicted based on crustal thinning,inferring that it is a rifted basin.However,the basin shows three stages of subsidence(fast in 45-30 Ma,uplift in 30-16.4 Ma,and fast in 16.4-0 Ma).In the uplift stage,the strata were partly folded in the Late Oligocene and partly eroded in the Early Miocene,which is probably caused by the flexural bulging in response to the paleo-South China Sea subduction and the subsequent Dangerous Grounds and Borneo collision in the Sabah to the east of the WBL.
基金The research of Y.Y.is supported by City University of Hong Kong with the grant number 9610391by the Research Grants Council(RGC),the Hong Kong government,through the General Research Fund(GRF)with the project number CityU11213118 and CityU11209317.
文摘High entropy alloys(HEAs)and compositionally complex alloys(CCAs)have recently attracted great research interest because of their remarkable mechanical and physical properties.Although many useful HEAs or CCAs were reported,the rules of phase design,if there are any,which could guide alloy screening are still an open issue.In this work,we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning(ML)algorithms.Based on the artificial neural network algorithm,we were able to derive and extract a sensitivity matrix from the ML modeling,which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase,such as solid solution,intermetallic,or amorphous phase.Furthermore,we explored the use of an extended set of new design parameters,which had not been considered before,for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule,we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system.The outcomes of our experiments agree reasonably well with our predictions,which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs.
