基于几何坐标法的多维数据可视化技术在地质数据处理中的应用

为简单且快速地查看地质研究过程中所获得地质数据属性信息的分布特征,用VC++6.0实现了常用的基于几何坐标法的多维数据可视化方法,即平行坐标法和圆形平行坐标法。这两个方法融合了直方图和等值线图各自的优点,可以同时查看数据的频数分布及其对应的坐标,还可以通过多个坐标轴显示数据,拓宽了一个图所能承载的数据信息,因此可以用这样的一个图形总体把握数据的分布特征。该方法在澳大利亚新南威尔士州Mandamah地区铜金矿床钻孔数据的应用中,通过折线密度可以看出Au、Cu、Pb和Zn等元素都有高值突出,即高值附近折线密度稀疏,而大量数据聚集在低值附近,即低值附近折线分布密度高。     

渗流监测数据缺失处理与渗透稳定判断

由于地下岩体条件复杂,渗流监测数据缺失现象普遍存在。为了在时间和空间上对地下渗流场进行全面分析,需要对缺失数据进行补充。提出了三类缺失数据的插补方法,对时间序列、空间断面以及区域整体的缺失数据分别用监测统计模型、物理因子修正和反演进行了有效的补充,反映了缺失数据的规律和时间、空间因素对监测数据的影响,最终可得完整、全面的渗流场,并通过反演渗流场对渗透稳定进行判断,为工程评价提供了依据。将该方法用于天荒坪电站缺失数据的插补,结果有效地插补了缺失数据,最终的反演渗流场能够判断渗透破坏可能发生的部位,对工程加固措施具有指导意义。     

测绘地理信息类专业双语课程评价体系构建

中国高校"双一流"建设要求培养兼备专业能力与国际竞争力人才,而双语教学则是一条有效途径.以CIPP(context,input,process,product)课程评价模型为理论依据,根据测绘地理信息类专业双语教学的特点,从背景评估、输入评估、过程评估、成果评估4个方面构建了测绘地理信息类专业双语课程评价指标体系,为提...     

基于虚拟SAN的网格GIS数据存储技术研究

针对空间数据存储的特点,结合网格和虚拟SAN技术的优势,提出了基于虚拟SAN的网格GIS数据存储解决方案,旨在解决网格GIS环境下的数据存储问题。     

周期浅析

本文分析了滑动平均的数据处理方法对周期分析结果产生的影响。提出了过程变量同号积分的数据处理方法,并对亚洲西风指数序列进行了试验分析。分析结果表明,周期的长短与振幅的大小有密切关系,亚洲逐日西风指数的周期振荡具有明显的季节特征。     

东海陆架水文浊度要素自动采集系统设计

提出了一种利用四脚架体原位观测东海陆架区域水文、浊度要素的方法,通过数据采集单元,实现对测量要素的采集和存储。通过对获取数据的分析,表明该方法安全可靠,可为揭示东海内陆架沉积过程研究提供数据来源。     

A statistical analysis of mesoscale eddies in the Bay of Bengal from 22–year altimetry data

本文使用一种基于SLA数据的涡旋识别方法,通过22年的AVISO高度计测高数据对孟加拉湾的中尺度涡特征进行了研究。本文主要分析了孟加拉湾涡旋的地理分布、涡旋极性、涡旋生命周期和传播距离、涡旋产生和消失位置、涡旋传播方向和移动轨迹、涡旋运动特征、涡旋属性的演化以及涡旋活动的季节和年际变化等特性。涡旋主要分布在孟加拉湾西部海域,并且大部分涡旋向西移动。涡旋极性分布显示气旋涡更经常出现在湾的西北部和南部,而反气旋涡主要出现在湾的东部。在22年间,共追踪探测到生命周期超过30天的气旋涡565个、反气旋涡389个;对所有生命周期和传播距离而言都是气旋涡数量居多。所有观测到的涡旋的运动属性分析显示气旋涡的涡旋平均振幅大于反气旋涡;对平均半径和平均移动速度而言,气旋涡和反气旋涡相差不大。而且,涡旋属性演化显示生命周期超过90天的涡旋具有明显的双阶段演化特征,包括一个前50天的涡旋成长阶段和一个50天之后的涡旋消亡阶段。针对涡旋活动的季节变化,气旋涡在春季居多而反气旋涡在夏季较多;长生命周期的涡旋季节分布显示在孟加拉湾涡旋活动具有明显的季节分布特征。涡旋数量的年际变化与EKE变化有一个明显的负相关。     

Phylogeography of the pelagic fish Seriola lalandi at different scales: confirmation of inter-ocean population structure and evaluation of southern African genetic diversity

The study investigated the global and regional phylogeography of the yellowtail kingfish Seriola lalandi by examining genetic diversity and population genetic structure of this species at inter-and intra-ocean level and on a regional scale. DNA fragments of two mitochondrial genes, cytochrome b (Cytb) and cytochrome c oxidase subunit I (COI) and one nuclear gene, recombination activating gene 1 (RAG1), were sequenced to investigate the global-scale phylogeography of this species. The population genetic structure within the South Pacific, as well as along the South African coastline, was examined further using six microsatellite markers. Three distinct clades were identified for S. lalandi, which correspond with previously described subspecies of the North-East Pacific, North-West Pacific and the Southern Hemisphere. Within the latter, additional divergence was observed between the South Pacific and the South-East Atlantic regions. Divergence estimates were indicative of a Pacific origin for S. lalandi populations, because of Pleistocene vicariant events. Microsatellite analyses revealed overall significant genetic differentiation between South African and South Pacific samples. This corroborates recent findings on the global phylogeography of the species. No population differentiation was observed within South Africa, indicating high levels of gene flow.     

大洋涌浪耗散研究及模式应用的进展及主要问题

大洋中涌浪普遍存在且对大气-海洋之间的物理过程有较大影响,但目前对涌浪的耗散过程研究尚不充分。总结了关于涌浪传播和耗散的观测事实,着重指出利用遥感数据推进相关研究的可能性;同时分析可能的物理机制,并论述其在海浪模式中的应用及不足。通过梳理大洋涌浪耗散的研究进展,为今后开展涌浪相关研究提供依据。     

Prediction of Seaward Slope Recession in Berm Breakwaters Using M5' Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim’s and Shekari’s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5'' model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5'' approach.