One of the major innovations awaiting in electron microscopy is full three-dimensional imaging at atomic resolution.Despite the success of aberration correction to deep sub-angstrom lateral resolution,spatial resoluti...One of the major innovations awaiting in electron microscopy is full three-dimensional imaging at atomic resolution.Despite the success of aberration correction to deep sub-angstrom lateral resolution,spatial resolution in depth is still far from atomic resolution.In scanning transmission electron microscopy(STEM),this poor depth resolution is due to the limitation of the illumination angle.To overcome this physical limitation,it is essential to implement a next-generation aberration corrector in STEM that can significantly improve the depth resolution.This review discusses the capability of depth sectioning for three-dimensional imaging combined with large-angle illumination STEM.Furthermore,the statistical analysis approach remarkably improves the depth resolution,making it possible to achieve three-dimensional atomic resolution imaging at oxide surfaces.We will also discuss the future prospects of three-dimensional imaging at atomic resolution by STEM depth sectioning.展开更多
双侧向测井是测井分析中的重要电法测井技术,为判断油水层提供有力的支持,随着勘探开发的不断深入,对薄层以及薄互层已经成为一种被开发的油气储集层,传统的双侧向仪器纵向分辨率已不能满足正确评价薄层以及薄互层的要求.本文在二维轴...双侧向测井是测井分析中的重要电法测井技术,为判断油水层提供有力的支持,随着勘探开发的不断深入,对薄层以及薄互层已经成为一种被开发的油气储集层,传统的双侧向仪器纵向分辨率已不能满足正确评价薄层以及薄互层的要求.本文在二维轴对称地层下,利用有限元方法编制了计算高分辨率双侧向测井仪器响应的数值模拟程序;利用有限元方法对高分辨率双侧向测井仪进行理论优化设计,提出了一种高分辨率双侧向电极环结构优化方案.经过优化后高分辨率双侧向在纵向分辨率达到0.2 m、探测深度达到1.03 m;对优化后高分辨率双侧向测井仪以及Cyber Service Unit(CSU)双侧向测井仪的井眼、探测深度、分层能力、以及薄互层影响进行对比分析,高分辨率双侧向受井眼影响较大,必须进行井眼校正;高分辨率双侧向的浅侧向CSU双侧向探测深度相同,但是深侧向略低于CSU深侧向;高分辨率双侧向在薄互层与薄层的响应要远优于CSU双侧向.展开更多
Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products ha...Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.展开更多
基金Project supported by JST-PRESTO (Grant No.JPMJPR1871)JST-FOREST (Grant No.JPMJFR2033)+2 种基金JST-ERATO (Grant No.JPMJER2202)KAKENHI JSPS (Grant Nos.JP19H05788,JP21H01614,and JP24H00373)“Next Generation Electron Microscopy”social cooperation program at the University of Tokyo。
文摘One of the major innovations awaiting in electron microscopy is full three-dimensional imaging at atomic resolution.Despite the success of aberration correction to deep sub-angstrom lateral resolution,spatial resolution in depth is still far from atomic resolution.In scanning transmission electron microscopy(STEM),this poor depth resolution is due to the limitation of the illumination angle.To overcome this physical limitation,it is essential to implement a next-generation aberration corrector in STEM that can significantly improve the depth resolution.This review discusses the capability of depth sectioning for three-dimensional imaging combined with large-angle illumination STEM.Furthermore,the statistical analysis approach remarkably improves the depth resolution,making it possible to achieve three-dimensional atomic resolution imaging at oxide surfaces.We will also discuss the future prospects of three-dimensional imaging at atomic resolution by STEM depth sectioning.
文摘双侧向测井是测井分析中的重要电法测井技术,为判断油水层提供有力的支持,随着勘探开发的不断深入,对薄层以及薄互层已经成为一种被开发的油气储集层,传统的双侧向仪器纵向分辨率已不能满足正确评价薄层以及薄互层的要求.本文在二维轴对称地层下,利用有限元方法编制了计算高分辨率双侧向测井仪器响应的数值模拟程序;利用有限元方法对高分辨率双侧向测井仪进行理论优化设计,提出了一种高分辨率双侧向电极环结构优化方案.经过优化后高分辨率双侧向在纵向分辨率达到0.2 m、探测深度达到1.03 m;对优化后高分辨率双侧向测井仪以及Cyber Service Unit(CSU)双侧向测井仪的井眼、探测深度、分层能力、以及薄互层影响进行对比分析,高分辨率双侧向受井眼影响较大,必须进行井眼校正;高分辨率双侧向的浅侧向CSU双侧向探测深度相同,但是深侧向略低于CSU深侧向;高分辨率双侧向在薄互层与薄层的响应要远优于CSU双侧向.
基金supported by Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA28110502]National Natural Science Foundation of China[grant number 41871248]+1 种基金Changchun Science and Technology Development Plan Project[grant number 21ZY12]Innovation and Entrepreneurship Talent Project of Jilin Province[grant number 2023QN15].
文摘Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.
