变形监测网的可区分性分析

本文首先借助于 Gauss-Markov 模型下两个多维备选假设检验的理论,将变形监测网单个备选假设下的灵敏度,在顾及模型误差的情况下,扩展成监测网的可区分性理论,提出了监测网的可区分性和区分可靠性概念;其次,对于监测网在三种典型变形模型(单点移动,错动和均匀应变)下变形与粗差的可区分性进行了讨论,获得了一些有益的结论。     

以双可区分度为准则的变形监测网非线性动态优化设计

在监测网设计阶段，不仅要考虑不同变形模型之间的可区分度，而且要研究变形与粗差的可区分度，即双可区分度，以利以后进行变形分析。文中导出了变形与粗差的可区分度，并以双可区分度为准则进行监测网非线性动态优化设计。同时给出了一个非线性直接优化算法，该算法避开了常规的台劳级数线性展开的方法，易于在计算机上实现。     

动态平差模型下形变与粗差的可区分性研究

从形变监测数据处理中常用的动态平差模型出发，推导出了研究形变与粗差可区分性的公式，给出在形变模型统计检验的同时统计检验粗差的方法与步骤，对垂直形变监测网的可区分性作了详细讨论和实例分析，得出几点结论．     

Ability to detect and locate gross errors on DEM matching algorithm

Abstract

Digital elevation model (DEM) matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator, in which terrain changes are treated as gross errors. However, all existing methods only emphasise their deformation detecting ability, and neglect another important aspect: only when the gross error can be detected and located, can this system be useful. This paper employs the gross error judgement matrix as a tool to make an in-depth analysis of this problem. The theoretical analyses and experimental results show that observations in the DEM matching algorithm in real applications have the ability to detect and locate gross errors. Therefore, treating the terrain changes as gross errors is theoretically feasible, allowing real DEM deformations to be detected by employing a surface matching technique.     

单个备选假设的可区分

根据单个备选假设的统计检验,详细导出了模型误差的可区分度公式,并论述了可区分度与可发现度及与Forstner法的关系。作为应用,可导出变形模型的可区分度及网的可区分标准,在监测网的设计中应用。     

动态平差概括模型与假设检验

建立了包括各种静态平差模型和线性动态平差模型的动态平差概括模型，推导了其主要求解公式。对动态平差概括模型下的形变检验和形变与粗差的可区分性检验作了公式推导和理论分析，丰富了形变监测数据处理理论。     

异常形变检测方法探讨

在讨讼以往方法的基础上,将拟准检定法拓展用于发现异常形变。该方法直接以真误差为研究对象,避免了其他方法以最二乘残差及其函数为研究对象的缺点。最后通过一个算例,简单介绍了拟准检定法检测异常形变的实施过程。     

高分辨率三维激光扫描数据的微小变形统计分析

地基三维激光扫描数据具有高精度、高空间分辨率的特点,能够改变传统的单点变形观测模式,使传统的点测量向“形测量”转化,因而大量的变形监测数据使得通过数学统计的方法探测变形量小于测量精度的微小变形成为可能。利用高分辨率三维激光扫描数据的微小变形统计分析方法,提出了基于反射值影像和BaySAC的拼接方法提高拼接精度;通过拟合方式提高整体分析精度,而影响拟合精度的主要因素为粗差,因此采用RanSAC算法剔除粗差,提高拟合的精度;然后利用格网化方法消弱偶然误差、修正拼接误差,最终得到变形量。采用建筑墙面及地铁隧道的点云数据进行了微小变形检测的实验,证明了该方法的有效性。     

强污染单历元GPS形变信号的提取和粗差识别

阐述了小波分析技术用于强污染GPS单历元形变信号处理的基本原理及其实现方法。以含少量粗差的低信噪比形变信号为例 ,研究了基于Mallat分解和合成算法 ,分离信号趋势项并进行粗差识别的技术。采用小波软阈值降噪的方法去除强污染数据中的随机噪声 ,降噪效果远好于中值滤波。最后对识别的粗差信息进行线性修复 ,获得了令人满意的形变信号     

Single epoch GPS deformation signals extraction and gross error detection technique based on wavelet transform

IntroductionGPS technique is widely used for deformationmonitoring thanks for the high precision. Usual-ly , there are three working modes associatedwith GPS deformation observation: periodicalGPS deformation monitoring net , GPS monito-ring array and rea…     

LZD模型多维粗差发现和定位能力分析

不借助地面控制点的DEM匹配差异探测算法是多时相DEM分析的最新发展方向。现有的算法大多把DEM表面差异作为粗差进行处理,算法性能得到了较大的提高,但忽略了对DEM匹配模型多维粗差的可发现和可定位能力进行分析,而这是DEM差异探测算法的理论基础。本文采用测量误差和可靠性理论中的判断矩阵法,通过理论分析和模拟试验验证了对于实际DEM而言,LZD模型具备多维粗差发现和定位能力。基于LZD模型的DEM差异探测算法能够正确地探测表面变形,这就完善了DEM差异探测研究的理论基础。     

利用基于抗差垂直向方差分量估计的GPS-InSAR数据融合方法反演三维形变场

GPS-InSAR数据融合解算三维形变场模型易受观测值粗差影响,且基于方差分量估计的定权方法不具备抵御粗差能力,计算效率低下。鉴于此,本文提出了一种基于抗差垂直向方差分量估计的GPS-InSAR融合解算模型,利用方差分量估计方法及抗差估计理论,通过对观测值最优化分类并进行选权迭代,精确分配权重,进而有效计算三维形变场。试验结果表明,该方法能有效抵御观测值粗差不利影响,提高三维形变场反演精度,提升逐点式计算的三维形变场效率。     

Method for determining weight matrix for resolving three-dimensional surface deformation using Multi-LOS D-InSAR technology

Three-dimensional surface deformation is resolved using deformations obtained from multiple line of sight differential interferometry synthetic aperture radar (multi-LOS D-InSAR). However, the random model is susceptible to both accidental errors (such as errors due to noises) and system errors (such as orbital errors and topographic errors). To solve this problem, we present a weight-determining method based on high-precision global positioning system (GPS) observations. Specifically, the precision of multi-LOS D-InSAR deformation observations is evaluated based on high-precision GPS deformation observations from the same period. Then, by considering the influence of system errors and accidental errors on the deformation of a pixel observed by multi-LOS D-InSAR, a combined fitting model associated with the positions and elevations of a pixel is built. Finally, the precision of deformations from multi-LOS D-InSAR is evaluated pixel-by-pixel by using the built model. As a result, the weights of observations in the model for resolving three-dimensional surface deformation using multi-LOS D-InSAR can be determined. We verify the feasibility and precision of the weight-determining method through a simulation experiment and a real data experiment, respectively. The verification results show that the proposed weight-determining method based on high-precision GPS observations provides higher reliability for the resolved three-dimensional surface deformation.     

知识引导的滑坡监测数据粗差定位与剔除方法

为了避免灾情误判和误报,准确探测和剔除滑坡形变监测数据中的粗差已经成为提高监测数据质量亟待解决的问题。已有方法主要针对单一传感器数据独立处理,且过度依赖数据变化本身的突变-平滑关系,难以有效区分粗差和外界因素突变引起的奇异值。介绍了一种知识引导的滑坡监测数据粗差剔除方法,通过粗糙集属性约简筛选具有相关关系的多源滑坡观测数据,并结合多元统计理论挖掘粗差影响因素间的时空约束关系,利用不同类型滑坡监测数据变化间的相关性规律,将多因素影响下的滑坡形变抽象为多模式的组合,根据不同模式自适应选择多因子模型以此引导卡尔曼滤波模型更新,从而实现滑坡形变监测粗差的定位与剔除。实验证明,该方法不仅能够有效甄别因环境变化引起的突变,并且能显著提高滑坡形变监测数据粗差自适应剔除的准确性、可靠性与智能化水平。     

Improvement of the separability of a survey scheme for monitoring crustal deformations in the area of an active fault

 Monitoring of the crustal movements along a tectonic fault is of particular importance in the study of the mechanism of an earthquake. There are several techniques to gauge crustal deformations, including terrestrial survey methods, space-positioning techniques and permanently installed geotechnical instruments. Each technique or method has its own advantages and limitations. Integration of the various techniques into a monitoring scheme is recommended. It is discussed how a proper integrated system can significantly improve the separability of a monitoring scheme at little additional expense. Separability is the ability of a monitoring scheme to distinguish among potential deformation models, and can be used for the optimum design of monitoring schemes. Discussion concentrates on the separability between a dislocation model and a rigid movement model in the area of an active fault. The addition of a few strain observations to a conventional terrestrial survey scheme can better distinguish between the above-mentioned models. A simulated example is presented to demonstrate the idea. Received: 4 November 1997 / Accepted: 9 July 2001     

小波分析在GPS变形监测数据处理中的应用

阐述了小波变换在变形监测数据处理中的应用方法,将变形监测的数据序列视为不同频率成分组成的数字信号,用MATLAB编程实现小波分析对监测数据的粗差识别、消噪、发展趋势的提取,实例表明,小波分析可以较好地适用于大坝变形监测的数据处理。     

GPS单历元形变信号的小波降噪

阐述了小波变换、多分辨分析及中值滤波的基本原理。研究了对小波变换的高频系数采用中值滤波,然后重构信号达到降噪目的的形变数据处理新方法。并与强制降噪及软门限阈值化小波降噪方法比较。模拟实验表明,基于高频中值滤波的小波降噪方法不仅能有效降噪,保持信号的光滑性,还能在降噪的过程中剔除少量粗差。     

小波阈值去噪法在矿区地表变形监测数据处理中的应用

为了解决矿区地表变形观测数据中含有的粗差对地表沉陷规律研究带来的不利影响,提出了采用小波阈值去噪法对原始观测数据进行处理。分析了小波阈值去噪的基本过程,讨论了小波阈值函数,并对两种阈值函数进行了对比;使用Matlab小波工具箱对观测数据进行分析并建立回归曲线,与地表实测沉陷值进行了验证。结果表明:相比常用的数据处理方法,小波阈值去噪法能有效消除粗差对变形结果的影响,实现了矿区高精度、动态的地表变形监测,具有较高的推广价值。     

Outlier separability analysis with a multiple alternative hypotheses test

Outlier separability analysis is a fundamental component of modern geodetic measurement analysis, positioning, navigation, and many other applications. The current theory of outlier separability is based on using two alternative hypotheses—an assumption that may not necessarily be valid. In this paper, the current theory of outlier separability is statistically analysed and then extended to the general case, where there are multiple alternative hypotheses. Taking into consideration the complexity of the critical region and the probability density function of the outlier test, the bounds of the associated statistical decision probabilities are then developed. With this theory, the probabilities of committing type I, II, and III errors can be controlled so that the probability of successful identification of an outlier can be guaranteed when performing data snooping. The theoretical findings are then demonstrated using a simulated GPS point positioning example. Detailed analysis shows that the larger the correlation coefficient, between the outlier statistics, the smaller the probability of committing a type II error and the greater the probability of committing a type III error. When the correlation coefficient is greater than 0.8, there is a far greater chance of committing a type III error than committing a type II error. In addition, to guarantee successful identification of an outlier with a set probability, the minimal detectable size of the outlier (often called the Minimal Detectable Bias or MDB) should dramatically increase with the correlation coefficient.