抗差最小二乘配置在数字化地图几何纠正中的应用讨论

抗差估计具有较好的抗拒异常观测值及粗差的能力,而最小二乘配置又能较好地处理系统误差,本文结合两者的优点,利用抗差最小二乘配置对数字化地图进行几何纠正,其中对协方差函数采用抗差拟合,得到了较好的结果。实验证明在GIS数据处理的扫描数字化地图几何纠正中,抗差最小二乘配置在抗拒异常值和处理系统误差方面优于单纯的最小二乘估计和单纯的最小二乘配置方法。     

Estimation of covariance matrix from geochemical data with observations below detection limits

Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances, , , , and forY andZ: (ii) defining new variables by and and lettingA=C+D andB=C–D, and obtaining MLE for variances, and forA andB; (iii) estimating the correlation coefficient _YZ by and the covariance _YZ by . The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada.     

利用主成分分析改善土地利用变化的遥感监测精度——以珠江三角洲城市用地扩张为例

近年来,珠江三角洲由于经济的快速发展,城市用地急剧增加,利用多时相的遥感图,可以定量地监测这种城市化的现象。但理,由一般的遥感动态监测方法所得的结果往往夸大变化的程度,以及获得一些不合理的结论．该文提出主成分分析的方法来改善遥感动态监测的精度。将该方法应用应用于珠江三角洲发展最快的东莞市,获得了较满意的结果。     

成都城市发展对热岛强度影响的主成份回归分析

本文对成都市总人口、建成区面积等11个因子、作了主成份回归L-S估计和M-估计,讨论了成都城市发展对“热岛”强度的主要影响因子。结果表明,城区房屋建筑面积及总人口数是影响城市气候(气温)的主要因子,其次为城市人口总户数、建成面积等。 文中,对回归方程进行了拟合计算,回归效果比较满意(尤其是稳健回归)。     

Spatial and temporal variability of Holocene temperature in the North Atlantic region

The early Holocene climate of the North Atlantic region was influenced by two boundary conditions that were fundamentally different from the present: the presence of the decaying Laurentide Ice Sheet (LIS) and higher than present summer solar insolation. In order to assess spatial and temporal patterns of Holocene climate evolution across this region, we collated quantitative paleotemperature records at sub-millennial resolution and synthesized their temporal variability using principal components analysis (PCA). The analysis reveals considerable spatial variability, most notably in the time-transgressive expression of the Holocene thermal maximum (HTM). Most of the region, but especially areas peripheral to the Labrador Sea and hence closest to the locus of LIS disintegration, experienced maximum Holocene temperatures that lagged peak summer insolation by 1000-3000 years. Many sites from the northeastern North Atlantic sector, including the Nordic Seas and Scandinavia, either warmed in phase with maximum summer insolation (11,000-9000 years ago) or were less strongly lagged than the Baffin Bay-Labrador Sea region. These spatially complex patterns of Holocene climate development, which are defined by the PCA, resulted from the interplay between final decay of the LIS and solar insolation forcing.     

Hydrogeochemical evolution of groundwater in a Mediterranean coastal aquifer, Mersin-Erdemli basin (Turkey)

In this study, hydrogeologic and hydrochemical information from the Mersin-Erdemli groundwater system were integrated and used to determine the main factors and mechanisms controlling the chemistry of groundwaters in the area and anthropogenic factors presently affecting them. The PHREEQC geochemical modeling demonstrated that relatively few phases are required to derive water chemistry in the area. In a broad sense, the reactions responsible for the hydrochemical evolution in the area fall into four categories: (1) silicate weathering reactions; (2) dissolution of salts; (3) precipitation of calcite, amorphous silica and kaolinite; (4) ion exchange. As determined by multivariate statistical analysis, anthropogenic factors show seasonality in the area where most contaminated waters related to fertilizer and fungicide applications that occur during early summer season.     

保持主分量变换结果信息的量化方法

从主分量变换后图像信息保持的角度出发，充分分析了变换结果存储导致信息丢失的原因；同时以直接线性变换为基础，提出了另外两种新的对主分量变换结果进行量化的方法；并对这3种方法进行了充分的比较，文中最后以信息熵为指标评价了3种方法的优劣。     

MAPGIS组件技术在平硐空间数据管理中的应用

利用MAPGIS组件技术,实现了将隧道施工中采集的多源种类数据信息直接生成MAPGIS支持的平硐图形文件,并且能将平硐的图形信息和隧道采集数据进行连接与同时管理的功能。这充分发挥了GIS管理空间数据和属性数据的优势,合理管理平硐基础空间信息资料,也解决了由手工矢量化和数字化采集等常规方式获取平硐图形信息工作量较大的缺点,以及平硐图形信息(空间数据)与采集的数据信息(属性数据)不能在一般非GIS系统中实现对应连接管理的问题。     

GIS叠置图层方差分量的极大似然估计

针对GIS叠置中的同名点,以维希特分布密度为似然函数,提出了各图层方差分量的极大似然估计方法。该方法不依赖残差,不需要迭代就能估计未知参数和方差分量。     

Size Scaling of Cross Correlation Between Multiple Variables

Reservoir models have large uncertainty because of spatial variability and limited sample data. The ultimate aim is to use simultaneously all available data sources to reduce uncertainty and provide reliable reservoir models for resource assessment and flow simulation. Seismic impedance or some other attribute provides a key source of data for reservoir modeling. These seismic data are at a coarser scale than the hard well data and it not an exact measurement of facies proportions or porosity. A requirement for data integration is the cross-covariance between the well and seismic data.The size-scaling behavior of the cross correlation for different measurement scales was nvestigated. The size-scaling relationship is derived theoretically and validated by numerical studies (including an example with real data). The limit properties of the cross-correlation coefficient when the averaging volume becomes large is shown. After some averaging volume, the volume-dependent cross-correlation coefficient reaches a limit value. This plateau value is controlled mainly by the large-scale behavior of the cross and direct variograms.The cross correlation can increase or decrease with volume support depending on the relative importance of long- and short-scale covariance structures. If the direct and cross variograms are proportional, there is no change in the cross correlation as the averaging volume changes. Our study shows that the volume-dependent cross-correlation coefficient is sensitive to the shape of the cross variogram and differences between the direct variograms of the well data and seismic data.