似鱼形断层角砾形成机制及意义

在湖南衡东县铅锌矿观察到一种似鱼形的断层角砾。利用单个角砾即可指示断层两盘位移方向和断层面产状。作者认为它将为变质岩区矿田构造研究、为钻孔岩心研究和矿山坑道地质研究中确定断层位移方向带来方便。     

江西赣县区牛角龙钨多金属矿床的发现及其特征

江西赣县区牛角龙钨多金属矿床是根据中国地质调查局在赣南老区实施精准扶贫工作总体部署,由南京地质调查中心"南岭东段重要矿种成矿要素调查"项目组通过1∶5万矿产地质调查、大比例尺填图、地表槽探揭露和深部钻探验证等技术手段发现的具有大型成矿远景的钨多金属矿床.最新研究成果显示,该矿床为石英细脉-网脉带型黑钨矿多金属矿床,地表已圈定南、北2个矿脉带,其中呈近SN向展布的北脉带( Ⅰ)是矿区的主矿脉,以石英细脉-网脉型黑钨多金属矿为主,宽10~30 m,南北延伸1 500 m以上,验证实施的ZK001钻孔和ZK002钻孔中分别发现含矿石英脉157条和249条,已圈定了4个钨矿体、2个银矿体和8个含矿细脉带.该矿床的发现不仅实现了赣南找矿空白区的新突破,为该区脱贫致富提供新的钨多金属矿资源基地,也为赣南及相邻地区钨多金属矿找矿勘查提供了理论依据和成功示范.     

简谐荷载下单层球面网壳动力失效机理研究

以具有实际工程意义的40m跨度K8型单层球面网壳为研究对象,采用ANSYS程序的Pipe20单元,考察不同矢跨比网壳在不同频率的简谐荷载作用下,随着荷载幅值的逐渐增大,其宏观和微观响应的变化,详细阐述了2类破坏行为的规律,并通过一定规模的参数分析,给出单层球面网壳动力强度破坏判别准则.     

2020年5月山东强对流天气特点及成因

2020年5月山东共出现13次强对流天气过程,其中8次出现冰雹,共15市（93.8%）81站（65.9%）出现降雹,单站最大降雹次数为4次。10次出现10级以上雷暴大风,5次出现短时强降水,强对流次数之多、范围之广、强度之大、灾害之重为近10年少见。其中,“5 ·17”强对流天气过程最为剧烈,其冰雹范围之广非常罕见,最大冰雹直径为4.5 cm,最大风速达36.6 m ·s^-1（12级）,最大雨强达56.9 mm ·h^-1。利用欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts,ECMWF）第五代大气再分析数据集（ECMWF Reanalysis v5,ERA5）和加密自动气象观测站、多普勒天气雷达、闪电定位等资料,对2020年5月山东强对流天气特点及强对流多发的原因进行分析,并以“5 ·17”强对流天气过程为例,对雷达回波特征和风暴内的垂直运动进行剖析。结果表明：（1）副热带高压强度偏强,一方面有利于其外围的西南暖湿气流到达山东,另一方面阻挡西风带系统,导致前倾槽强度偏强,长时间维持在山东上空;500 hPa异常偏强的暖高压脊前西北气流携带的冷空气叠加在850 hPa偏强的暖温度脊上空,造成山东上空长时间为位势不稳定大气层结。（2）在上述有利的天气背景下,山东上空水汽充沛,对流有效位能偏大,冀鲁豫3省交界处气旋式辐合偏强,鲁中地区稳定存在一条辐合线,容易触发产生强对流天气,造成山东5月强对流天气频发。（3）对流风暴高度组织化、区域性的超级单体群以及一条长度超过500 km的强飑线是造成“5 ·17”强对流的直接原因,对流风暴内部的上升速度高达28 m ·s^-1。     

北京稳定天气条件下城市边界层环流特征数值研究

采用科罗拉多大学和MRC／ASTER共同开发的区域大气模拟系统（RAMS）对北京地区稳定天气条件下的个例进行数值模拟,通过对数值模拟结果与观测事实的比较以及敏感性试验,分析了北京城市边界层环流特征和环流影响因子在环流发展过程中的作用。结果表明：①在山谷风环流和热岛环流相互作用下形成了北京城市边界层流场特有的局地环流。②热岛中心在决定边界层环流的辐合区位置上起相对较大的作用,边界层环流的强度和发展高度由山谷风强度和热岛强度共同决定。     

海藻糖类的气相色谱分析 Ⅱ.红藻及红藻多糖中D-半乳糖和3, 6-内醚半乳糖的测定

红藻中的糖类主要以多糖形式存在(琼胶、卡拉胶等)，其中主要的单糖组分是D-半乳糖和3, 6-内醚半乳糖，除此之外还含有少晕的木糖、葡萄糖及甘露糖等成分。由于各单糖的相互干扰，难以用比色等着通化学分析方法对每一种单糖准确地定性定量。作者曾研究过褐藻中单糖的气相色谱测定法, 但该法不能测定3, 6-内醚半乳糖，囚为这种内醚糖对酸水解不稳定。据Dutton和Nozawa等人报道, 将多糖经甲醇分解后进行气相色谱分析，结果令人满意。本文用琼胶(Difco bacto-agar)作代表，研究探讨了红藻胶甲醇-HCI分解的条件，确定了红藻胶中D-半乳糖和3, 6-内醚半乳糖的气相色谱分析方法，并将其应用于红藻中D-半乳糖和3, 6-内醚半乳糖的分析。     

GeoCube： 面向大规模分析的多源对地观测时空立方体

随着对地立体观测体系的建立,遥感大数据不断累积。传统基于文件、景/幅式的影像组织方式,时空基准不够统一,集中式存储不利于大规模并行分析。对地观测大数据分析仍缺乏一套统一的数据模型与基础设施理论。近年来,数据立方体的研究为对地观测领域大数据分析基础设施提供了前景。基于统一的分析就绪型多维数据模型和集成对地观测数据分析功能,可构建一个基于数据立方的对地观测大数据分析基础设施。因此,本文提出了一个面向大规模分析的多源对地观测时空立方体,相较于现有的数据立方体方法,强调多源数据的统一组织、基于云计算的立方体处理模式以及基于人工智能优化的立方体计算。研究有助于构建时空大数据分析的新框架,同时建立与商业智能领域的数据立方体关联,为时空大数据建立统一的时空组织模型,支持大范围、长时序的快速大规模对地观测数据分析。本文在性能上与开源数据立方做了对比,结果证明提出的多源对地观测时空立方体在处理性能上具有明显优势。     

西南印度洋中脊表层沉积物非碳酸盐组分的粒度特征及其地质意义

The carbonate-free fraction of 20 surface sediments collected from the ultraslow-spreading Southwest Indian Ridge(SWIR) was studied by grain size analysis and mineralogical analysis with X-ray powder diffraction(XRD),stereo microscopy and scanning electron microscopy(SEM). The characteristics of the carbonate-free fraction of the sediments were obtained, and related influential factors were discussed. The results show that the mean grain size of this fraction is in 1.96Φ–8.19Φ, with poorly sorting and unimodal, bimodal or irregular bimodal distribution patterns. Four grain size end members of the fraction are derived with the End Member Model method. The finest end member EM1 shows a significant contribution of terrigenous materials of the aeolian input and sediment carried by the bottom current. End member EM2 with medium size mainly reflects sediment of a siliceous bioclast origin. EM3 and EM4 are interpreted as representing the coarser volcanic materials related to bedrock weathering or volcanic activities. Multi-provenance is the dominant factor controlling the grain size pattern of the carbonate-free fraction of the sediments in that area. In addition, sediment transport processes such as the bottom current and wind are the minor factors that influence the grain size distribution of the carbonate-free fraction sediments.     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

西安市地震应急指挥技术系统设计与实现

本文从系统总体架构设计、系统功能设计与应用成果、系统特色4个方面简要介绍了西安市地震应急指挥技术系统设计与实现情况.本系统是首个应用于省会城市地震应急工作,并基于国产Server GIS软件平台研制开发出的地震应急指挥技术系统,可为大城市地震应急指挥技术系统的建设与发展提供可借鉴的经验.