监测网的精度测度与三维应变参数的准则矩阵

本文基于监测网的特点，用变形参数的精度来测度同的质量，讨论了精度标准的不变性，重点根据位移向量与应变参数的关系，构造了三维应变参数的准则矩阵，以供参考。     

地图数字化数据的平差处理

地图数字化是获取 GIS空间数据的主要手段之一。针对地图数字化过程中对直角型和直线型地物的数据采集误差的平差处理 ,建立了条件方程 ,并讨论了衡量地图数字化质量的精度标准     

用手扶跟踪数字化器进行地图量算的试验

许多地物的数量特征都是通过地图量算获得的。但是过去在地图上量算物体的长度和面积，是用机械仪器和手工操作计算。随着电子技术的广泛应用，发展了地图的数字量算方法，大大提高了量算精度和速度。下面我们结合工作实践，着重介绍使用手扶跟踪数字化器进行地图量算的方法。     

地图扫描数字化的点位精度分析

地图扫描数字化为建立ＧＩＳ提供了一种较好的原始地图数据获取方法，本文讨论了影响地图扫描数字化精度的诸种因素，分析了定向点和采样点误差对地图数字化结果的影响，发展了定向点和采样点的自动对中算法，从而保证地图扫描数字化的精度能满足建立ＧＩＳ的要求。     

地图数字化的模糊性分析及质量评定

由于数字化过程的复杂性以及人们认识客观事物的有限性，地图数字化会产生模糊不确定性。从地图数字化所涉及的数据来源、硬件、软件以及人员等因素出发，详细讨论了由它们带来的模糊不确定性。运用模糊综合评判方法，在已有的基础上，加入数据来源、软、硬件和人员的模糊性评判因子以及权重，对数字化产品进行合理的质量评析。     

基于MAPGIS的地图数字化与地图接边

地图是GIS主要数据源，地图数字化是GIS数据输入的主要手段。分幅数字化常导致数据在接边处不匹配，地图接边是实现数字匹配的重要手段。文中详细了MAPGIS地图数字化输入方法，通过MAPGIS图形变换功能，总结了9幅分幅数字化地图拼接过程，通过MAPGIS图幅接边功能介绍了交互式，全自动，半自动图幅接边操作及如何实现数字接边。     

基于扫描矢量化地图数据生产的数据质量控制

地图数字化作为GIS数据输入的主要手段,处于GIS应用流程的最前端,数字化的数据质量对整个GIS的数据质量起着决定的作用。不同的数字化方法对数据质量的控制策略不同,但都应从软件的开发和使用两方面来保证数字化数据的质量。本文概述了基于扫描矢量化GIS数据的生产过程,并且主要分两个部分介绍了扫描矢量化质量控制的策略和方法,在地图扫描阶段可以采用栅格图纠正的方法对地图的变形进行纠正,以提高矢量化原图的质量,在数字化采集阶段,可以在算法和软件使用工艺两个方面采取措施来提高数据的质量。     

浅谈数字化测图的质量控制

本文依据ＩＳＯ９００１质量体系标准和铁一院航测大队四年多来进行数字化测图的生产实践所采取的质量控制方法与措施，结合保证在数字化地图规范化、标准化、规模化生产中不出现不合格品的经验与体会，对数字化测图质量控制的有关问题进行了一些论述。     

直角与直线元素数字化的数据处理

结合地图数字化为ＧＩＳ采集空间数据的工作实际，对直角、直线数字化的数据处理的平差模型以及相应的精度评定方法进行了研究。作者认为，通过平差和评定精度，可以提高数字化的精度，并有助于实现对ＧＩＳ数据采集的质量控制     

地图数据几何纠正时仿射变换与相似变换的对比分析

分析了地图数字化过程及其坐标变换，利用仿射变换和相似变换对聚酯薄膜和纸质地形图与航摄数字影像进行了实验研究。结果表明，对于地图数字化坐标换算，仿射变换优于相似变换，特别是对于纸质地图，仿射变化具有明显的优越性。     

位移-应变模型的稳健均匀分析

运用稳健估计法确定应变模型的特殊位移,识别影响均匀应变的特殊位移参数,用实例分析验证了作者提出了稳健的均匀分析法的有效性。     

稳健基准应变分析法用于鲜水河断裂带活动特征研究

本文运用稳健估计法确定位移基准,以此为基础,识别影响均匀应变场的特殊位移信息,并在估计均匀应变参数的同时,也对特殊位移信息作出估计。用所提的分析方法对覆盖鲜水河断裂带的甘孜一塔公寺三角锁网三期成果进行了分析处理,研究了该断裂带的活动特征。     

应变参数的抗差解及误差影响

应用弹性力学的应变分析理论和抗差估计原理，推导了应变参数的抗差解及其误差影响函数。实际算例表明，应变参数的抗差解可以有效地抵制粗差的异常影响，得到应变参数的可靠解，这对于利用高精度GPS复测资料研究大尺度的地壳运动和变形具有实际意义。     

小区域地壳变形监测与分析研究

介绍了小区域GPS地壳形变监测网建立、观测和数据处理方法,提出了一种平面直角坐标下位移—应变关系计算模型,得到监测区的位移场、应变场及断层活动方式,进一步得到主应变及方向、最大剪应变、面膨胀等应变参量及应力。     

Probability distribution of eigenspectra and eigendirections of a twodimensional,symmetric rank two random tensor

Let there be given a twodimensional symmetric rank two tensor of random type (examples:strain, stress) which is either directly observed or indirectly estimated from observations by an adjustment procedure. Under the assumption of normalityof tensor components we compute the joint probability density functionas well as the marginal probability density functionsof its eigenspectra (eigenvalues) and eigendirections (orientation parameters). Due to the nonlinearity of the relation between eigenspectra-eigendirections and the random tensor components, via the inverse nonlinear error propagationbiases and aliases of their first and centralized second moments (mean value, variance-covariance) are expressed in terms of Jacobianand Hessianmatrices. The joint probability density function and the first and second moments thus form the fundamental of hypothesis testing and qualify control of eigenspectra (eigenvalues, principal components) and eigendirections (orientation parameters, eigenvectors, principial direction) of a twodimensional, symmetric rank two random tensor.     

边长变化反演应变参数的总体最小二乘方法

根据总体最小二乘原则,推导了同时顾及边长变化及测线方位角量测误差的应变参数反演的总体最小二乘方法,并给出了精度评定公式。实际算例计算结果分析表明,应用总体最小二乘方法可以得到更合理的应变参数反演结果,特别是参数精度评定方面;对于系数矩阵中含有常数列时,必须将该常数列取出,否则得到的解是错误的。     

广西区域现今地壳运动与应变的GPS监测分析

利用GAMIT/GLOBK软件分析了1998-2010年间多期GPS观测数据,获得了ITRF2005框架下广西区域现今地壳运动三维速度场和应变场参数,并对区域形变场和现今构造运动动力学机制进行了初步讨论。GPS监测结果显示:广西区域现今地壳运动随华南块体作南东东方向的逆时针构造运动,ITRF2005框架下水平方向平均速率为33.49 mm/a,优势方向为N 109.3° E;垂直方向平均速率为+1.30 mm/a,整体趋势是自桂东向桂西方向垂向速率逐步增加。现今应变状态以N 127.3°±4.8° E挤压为主,最大主压应变率-2.7±0.5×10^-9/a;兼有N 37.3°±4.8° E方向的拉张作用,最大主张应变率1.8±0.2×10^-9/a。本文研究结果为描述广西区域现今地壳运动提供了一个更加精细的三维速度场。     

磁梯度张量系统的非线性集成矢量校正

磁梯度张量系统测量精度受到单磁传感器系统误差与传感器阵列间非对准误差的严重影响。为了获得精确的张量测量输出，建立了单磁传感器零漂、标度因子与非正交角等系统误差和多传感器轴系间非对准误差的集成数学模型，提出了基于十字磁梯度张量系统最小二乘非线性集成校正方法。相比两步标量校正，利用建立的集成数学模型能够一次性估计出十字形张量系统的48个误差参数，以"人造"平台输出为参考实现低成本矢量校正，极大提高了校正效率和参数估计准确率。仿真和实测表明，张量系统误差参数仿真估计准确率高于99.75%，实验校正后总场输出均方根误差（root mean square error，RMSE）小于2 nT，张量分量RMSE小于50 nT/m，参数估计具有较高的鲁棒性。     

Statistical inference of the eigenspace components of a two-dimensional, symmetric rank-two random tensor

The eigenspace components (i.e. principal components and principal directions) play a key role in the validation of a symmetric rank-two random tensor, e.g. for strain and stress. They classify deformation and stress patterns in earthquake regions, plate tectonics and glacial isostatic adjustment (postglacial rebound). It is assumed that the strain or stress tensor has been directly observed or indirectly determined by other measurements. According to the Measurement Axiom, such a symmetric rank-two tensor is considered random. For its statistical inference, the random tensor is assumed to be tensor-valued Gauss–Laplace normally distributed. The eigenspace synthesis relates the eigenspace elements to the observations by means of a nonlinear vector-valued function, thus establishing a special nonlinear multivariate Gauss–Markov model. For its linearized form, the best linear uniformly unbiased estimation (BLUUE) of the eigenspace elements and the best invariant quadratic uniformly unbiased estimate (BIQUUE) of its variance–covariance matrix have been successfully constructed. The related linear hypothesis test has documented large confidence regions for both eigenvalues and eigendirections based upon real measurement configurations. They lead to a statement of caution when dealing with data concerning extension and contraction, as well as the orientation of principal stresses.     

Surface deformation analysis of dense GPS networks based on intrinsic geometry: deterministic and stochastic aspects

In this contribution, the regularized Earth’s surface is considered as a graded 2D surface, namely a curved surface, embedded in a Euclidean space . Thus, the deformation of the surface could be completely specified by the change of the metric and curvature tensors, namely strain tensor and tensor of change of curvature (TCC). The curvature tensor, however, is responsible for the detection of vertical displacements on the surface. Dealing with eigenspace components, e.g., principal components and principal directions of 2D symmetric random tensors of second order is of central importance in this study. Namely, we introduce an eigenspace analysis or a principal component analysis of strain tensor and TCC. However, due to the intricate relations between elements of tensors on one side and eigenspace components on other side, we will convert these relations to simple equations, by simultaneous diagonalization. This will provide simple synthesis equations of eigenspace components (e.g., applicable in stochastic aspects). The last part of this research is devoted to stochastic aspects of deformation analysis. In the presence of errors in measuring a random displacement field (under the normal distribution assumption of displacement field), the stochastic behaviors of eigenspace components of strain tensor and TCC are discussed. It is applied by a numerical example with the crustal deformation field, through the Pacific Northwest Geodetic Array permanent solutions in period January 1999 to January 2004, in Cascadia Subduction Zone. Due to the earthquake which occurred on 28 February 2001 in Puget Sound (M _w > 6.8), we performed computations in two steps: the coseismic effect and the postseismic effect of this event. A comparison of patterns of eigenspace components of deformation tensors (corresponding the seismic events) reflects that: among the estimated eigenspace components, near the earthquake region, the eigenvalues have significant variations, but eigendirections have insignificant variations.