暴雨内涝危险性情景模拟方法研究——以上海中心城区为例

本文采用地理信息系统技术（GIS）与水文/水动力学相结合的方法,使用修正的SCS模型进行产流模拟,利用局部等体积法和水动力模型进行汇流模拟,建立了基于情景的城市暴雨内涝危险性模拟工具,并对相同雨强情景下不同汇流模型的模拟结果与精度进行了对比分析。结果表明：（1）局部等体积法计算简单,对降雨的时程分配较为敏感,峰前历时和雨强对结果影响显著,较适合于雨型确定的城市暴雨内涝危险性快速模拟;水动力模型计算复杂,与整个降雨历时存在明显关系,模拟精度较高。（2）利用台风“麦莎”带来强降雨导致的积水实测数据,对模拟结果进行对比和验证,表明本文建立的水动力模型法模拟结果与实测降雨积水更为接近,更适合上海中心城区暴雨内涝危险性情景模拟。     

基于Bayes理论的星间链路自主定轨误差评估方法

针对导航星座自主运行期间因缺乏外部基准而导致自主定轨结果不能得到有效评估的问题,提出一种基于Bayes理论的自主定轨结果评估方法。一方面充分考虑定轨误差的先验统计特性,以提供先验位置基准;另一方面以卡尔曼滤波中定轨协方差的迹作为定轨URE的表征量,解决了自主定轨中对定轨误差没有观测信息的问题。利用自主定轨的仿真数据对该方法进行验证,结果表明,对于单颗卫星,在24 d内,由该方法得到的URE估计误差不超过±0.5 m,验证了该方法的可行性。     

高频单历元GPS周期性误差特性分析

分析GPS高频单历元时间序列中的误差特性,采用中值滤波法消去高频噪声。经计算,偶然中误差在水平方向为±4～4.5 mm,高程方向为±9 mm。通过周期性重复解算、时间序列比对及相关系数最大化发现,GPS高频单历元时间序列中包含的周期性系统误差的重复周期为86 153～86 165 s。通过对系统误差建模或差分,可消除时间序列中的系统性成分,提高GPS高频单历元数据处理的精度。     

宽频带静电反馈倾斜仪正交耦合误差分析

分析和计算宽频带静电反馈倾斜仪测量中正交方向上的倾斜对测量结果的影响。结果表明,正交方向的倾斜使测量结果产生误差,需对仪器加以改进,以减小正交耦合误差。     

川中地区上三叠统须四段砂体成因及对油气运移影响分析

川中地区上三叠统须四段砂体沉积过程中,由于构造活动强弱的变化,导致物源供应速率与可容纳空间增长之间关系的变化,须四段厚层砂体自下而上由水退式、加积式和水进式3种不同成因类型的砂组纵向叠合而成.水退式砂组主要由高能削截式河道、削截式河口坝和远砂坝组成,加积式砂组主要包括冲蚀下切式河道和高能削截式河道,水进式砂组主要包括低能河道、低能削截式河道、完整式河口坝和下缺式河口坝.不同砂组中砂体类型不同,颗粒大小分异不同,水动力强弱的差异,不同成因砂体间形成了不同的成岩相和储集相,加积式砂组以绿泥石衬边和溶蚀成岩相为主,物性好,为好的储集体;水进式砂组由压实和胶结相为主,物性较差;水退式砂组中溶蚀相和胶结相发育,物性较好,为中等的储集体.须四段三种成因砂组中,加积式砂组中发育高能削截式河道,形成了在平面上广泛分布的高能水道,砂体厚度大,延伸长,发育有利的成岩相,物性较好,为油气的富集提供了足够的空间;同时加积式砂组与水退式砂组呈沉积不整合相接触,呈现相嵌关系,对天然气的运移起到通道作用,加积砂组向上的水进式砂组容易发生储层致密化,形成有利的盖层,使加积式砂组发育区成为川中天然气富集的有利部位.     

鄂尔多斯盆地延长组长6期湖盆中部砂体发育特征

通过对鄂尔多斯盆地延长组长6期湖盆中部地区物源分析、沉积相特征等研究,分析了厚层砂体的形成机理和砂体类型。认为长6期物源主要来自东北、西南方向,发育三角洲前缘水下分流河道、河口坝、远端砂坝和浊流等砂体类型,湖盆演化从鼎盛转向萎缩的长6早期厚层砂体比较发育。白豹、合水地区砂层厚度大,储层条件良好,是石油勘探的有利地区。     

基于判断矩阵的观测量粗差发现和定位相关性分析

用布尔矩阵分析、研究判断矩阵,得到观测量粗差发现和定位的相关数以及测量系统的最大可发现粗差和定位粗差数的计算公式.试验证明,当粗差发现和定位相互影响的观测量同时含有粗差时,现行的迭代数据探测法和选权迭代法不可能完全正确定位粗差.通过算例验证了使用布尔矩阵和判断矩阵分析多维粗差发现和定位相关性的有效性和优越性.     

GIS中3维空间圆曲线的不确定性εσ模型

随着GIS向3维领域的不断发展,对3维空间元素的不确定性研究日趋重要.基于不确定性理论及概率统计理论,研究GIS中3维空间圆曲线的不确定性模型.首先建立3维空间圆曲线上任意点坐标的微分关系式,然后得到圆曲线上任意点坐标的方差, 进而获得3维空间圆曲线的εσ模型.实际算例表明,3维空间圆曲线误差分布呈现出"两端大,中间小"的趋势.     

WATER-QUALITY MONITORING AND SPECIOUS DATA: THE EXAMPLE OF GEORGES CREEK,MARYLAND∗

Concurrent water data were collected by the U.S. Geological Survey, the U.S. Corps of Army Engineers, and the Maryland Department of Health and Mental Hygiene at the same site in order to describe present conditions and predict future environmental change in the Georges Creek basin in western Maryland. Evaluation of the data sets reveals measurement errors and weaknesses in sample design so that published complications contain significant errors. Unless these errors can be identified, policy based on such information may have unfortunate results.     

Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam,India

Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GAR_err) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GAR_err model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.