A new method for indentifying the model error of adjustment system

Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given. Project supported by the Open Research Fund Program of the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (No. 905276031-04-10).     

Investigating the propagation mechanism of unmodelled systematic errors on coordinate time series estimated using least squares

The propagation of unmodelled systematic errors into coordinate time series computed using least squares is investigated, to improve the understanding of unexplained signals and apparent noise in geodetic (especially GPS) coordinate time series. Such coordinate time series are invariably based on a functional model linearised using only zero and first-order terms of a (Taylor) series expansion about the approximate coordinates of the unknown point. The effect of such truncation errors is investigated through the derivation of a generalised systematic error model for the simple case of range observations from a single known reference point to a point which is assumed to be at rest by the least squares model but is in fact in motion. The systematic error function for a one pseudo-satellite two-dimensional case, designed to be as simple but as analogous to GPS positioning as possible, is quantified. It is shown that the combination of a moving reference point and unmodelled periodic displacement at the unknown point of interest, due to ocean tide loading, for example, results in an output coordinate time series containing many periodic terms when only zero and first-order expansion terms are used in the linearisation of the functional model. The amplitude, phase and period of these terms is dependent on the input amplitude, the locations of the unknown point and reference point, and the period of the reference point's motion. The dominant output signals that arise due to truncation errors match those found in coordinate time series obtained from both simulated data and real three-dimensional GPS data.     

Application of Dangerous Probability Function in Risk Estimation of Surface Rupture Earthquake：Taking the Lingwu Fault in Ningxi1 Region as on Example

Based on the fitting on paleoearthquake data of intra-plate regions in the northern part of Chi-na and giving a statistical model of time interdependence,the potential damage earthquakes in a definite future period and characteristics of present shocks along the Lingwu fault have been analyzed by using dangerous probability function and some new data concerned.We have in-ferred that the fault has entered a period that earthquakes will probably occur.There exists a potential danger that a strong earthquake with Ms7.0-7.5 will occur in 10-100a.     

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.     

Imposing Age and Spatial Structures on Inadequate Migration‐Flow Datasets*

With the elimination of the long‐form questionnaire from future decennial censuses and its replacement by a much smaller continuous monthly sampling survey (the American Community Survey), students of territorial mobility may find it necessary to deal with inadequate, missing, or inaccurate sample data on migration by adopting an approach that “improves” such data using information from different geographical areas, time periods, and data sources. We develop such an approach in this article and illustrate it with interregional migration flow data reported by the U.S. decennial censuses of 1980 and 1990 and by the 1985 Current Population Survey.     

扁铲侧胀试验判别液化的研究和实践

通过对扁铲侧胀试验判别液化的研究和实践，提出了在上海地区采用扁铲侧胀试验判别液化的公式，扁铲侧胀试验判别液化与标准贯入和静力触探试验判别液化的方法对比，具有相对较好的结果，是一种有前途的液化判别方法。     

两种方法在地下水位估值中的应用

对于许多区域水资源问题,用数值方法进行潜水水流模拟时,需要给出每个节点上地下水位值。本文首先简单介绍了趋势面方法,然后着重阐述了泛克里格方法的基本原理及它们在地下水位估值中的应用,通过比较两种方法的计算结果可以得出泛克里格方法是进行地下水位估值的空间最优估计方法。     

软土大变形固结计算中参数识别的遗传算法

含水量很高的饱和软土在外荷载作用下沉降很大,小变形分析误差太大,必须通过大变形非线性固结计算来模拟。遗传算法是一种全局优化和搜索的仿生算法。近年来随着工程领域中复杂的大规模非线性系统的出现,遗传算法日益得到青睐,目前已经广泛应用到各个领域中。本文对遗传算法做了改进,主要体现在杂交算子的选取和轮盘赌模型的模拟退火拉伸,并将其用于饱和软土的大变形固结分析,解决其中的关键参数识别问题。研究表明该方法是行之有效的,值得进一步研究探索。     

观测有效性的度量方法

基于Hilbert空间理论 ,通过对观测方程的系数阵实施格拉姆_施密特 (Gram_Schmidt,G_S)正交化过程 ,分析观测的两个特性 ,从一个新的角度论证了参数估计的必要观测问题 ,以及多余观测与必要观测的关系 ;用欧氏范数‖·‖2 和Frobenius‖·‖ F 范数作为基本度量工具对观测提供参数估计的信息量进行度量分析 ,研究观测的有效性和参数估计的可靠性。采用观测空间分析方法研究了测绘学科中必要观测、多余观测等基本概念 ;提出了观测信息量、观测有效性的概念和相应的度量方法 ;从观测的空间结构出发分析参数估计系统的可靠性 ,从一个侧面扩展和丰富了测绘学中的可靠性理论 ;丰富了研究测绘学中一些基本问题的分析方法 ,也为信息学中的数据挖掘问题提供了一种分析思路     

用几何光学模型对方向变化和直方图建模(英文)

森林和林地的图象二向性反射函数是一个统计函数，较之小尺度的树冠，它更多地用于大尺度的均匀覆盖的地块。用航空象片和高空间分辨率扫描仪数据作的图象热点影响研究在小尺度下显示出很大方差，而且，太阳和观测角度的交互变化进一步增加了这种反射各向异性变化的方差并有规律地继续呈现在分辨率低的图象中，这被称作ＢＲＶＦ或二向性反射方差函数。近年来，作为一种解释结构的手段，高分辨率图象的方向性方差和直方图结构越来越受到重视，这方面的数据也越来越多。这项工作是利用图象方差来解释结构问题（Ｓｔｒａｈｌｅｒ和李小文倡导）的一种延伸，并在过去１５年中由众多人员作了大量工作。在树冠尺度下，森林的直方图和二向反射方差函数可以计算出来，这里利用了近似迭代函数来处理这些数据并和数值积分模拟进行了对比，结果显示可对二向性反射方差函数的测量和引入直方图的各向异性进行准确建模。