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冀鲁豫地区重力场变化与封丘ML4.8地震关系的探讨

利用冀鲁豫测网的流动重力5期观测成果,探讨了重力场变化与封丘地震的关系。研究发现：冀鲁豫交界地区的重力点值在封丘地震前后出现了下降-上升-下降的变化过程,较好地对应了封丘ML4.8地震孕育 发震 震后调整的过程。该重力变化可能是封丘地震的异常反映。     

基于条件平差的卫星双向时间传递链路性能优化方法

卫星双向时间传递(Two-Way Satellite Time and Frequency Transfer, TWSTFT)是目前精度最高的时间传递方法之一,同时也是参与国际原子时计算的守时实验室之间比较原子时尺度的一种主要方法.提高TWSTFT链路的短期稳定度,降低周日效应对链路时间传递结果的影响,对优化TAI (International Atomic Time)的性能具有现实意义.提出了一种基于条件平差的TWSTFT链路性能优化方法,先依据TWSTFT链路测量噪声水平与谱分析结果建立TWSTFT链路性能优化网络(简称优化网络),再根据优化网络中各链路测量噪声分析结果设置权系数阵,建立条件平差模型.选取亚太地区的中国计量科学研究院(National Institute of Metrology, NIM)-中国科学院国家授时中心(National Time Service Center, NTSC)卫星双向时间传递链路作为待优化链路,以NTSC、NIM以及德国联邦物理技术研究所(Physikalisch-Technische Bundesanstalt,PTB)之间的TWSTFT链路组成优化网络,对优化网络的组网方法和条件平差模型进行实验验证.结果表明,平差后待优化链路短期稳定度得到了改善,同时其受周日效应的影响降低了约24.6%.使用该方法,能够有效提高待优化链路的时间传递性能.     

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海陆气候事件关联规则挖掘方法

近年来,异常气候事件的频发对人类的生活环境和经济发展带来严重负影响。气象学家研究表明：海洋气候异常对陆地气候异常事件的发生具有重要的诱发作用,因此,对海陆气候间的内在关联机制进行深入挖掘具有重要研究价值。本文提出了一种关联规则挖掘方法,以探索单一海洋气候指数与陆地异常气候事件间存在的关联。首先,针对陆地气候要素,采用顾及空间邻近关系的层次聚类方法进行有效气候分区,通过对各层分区结果进行相关统计分析得到有效的各区域气候序列;然后,进行顾及多重约束进行时序关联规则挖掘,以探索海陆气候要素间的关联机制;最后,通过实际算例分析得到的各气候指数与我国陆地区域异常降水事件间的关联机制结果,与实际情况高度吻合。     

附有限制条件的间接平差秩亏时解法初探

在附有限制条件的间接平差的应用中,若所列观测方程中实际用到的未知参数的个数少于所选参数的总数u时,就会出现法方程系数阵秩亏的问题。为了解决此问题,在理论公式推导的基础上,给出了一种新的解决方法,并结合算例进行了验证。     

A strip adjustment procedure to mitigate the impact of inaccurate mounting parameters in parallel lidar strips

Lidar (laser scanning) technology has been proven as a prominent technique for the acquisition of high-density and accurate topographic information. Because of systematic errors in the lidar measurements (drifts in the position and orientation information and biases in the mirror angles and ranges) and/or in the parameters relating the system components (mounting parameters), adjacent lidar strips may exhibit discrepancies. Although position and orientation drifts can have a more significant impact, these errors and their impact do not come as a surprise if the quality of the GPS/INS integration process is carefully examined. Therefore, the mounting errors are singled out in this work. The ideal solution for improving the compatibility of neighbouring strips in the presence of errors in the mounting parameters is the implementation of a rigorous calibration procedure. However, such a calibration requires the original observations, which may not be usually available. In this paper, a strip adjustment procedure to improve the compatibility between parallel lidar strips with moderate flight dynamics (for example, acquired by a fixed-wing aircraft) over an area with moderately varying elevation is proposed. The proposed method is similar to the photogrammetric block adjustment of independent models. Instead of point features, planar patches and linear features, which are represented by sets of non-conjugate points, are used for the strip adjustment. The feasibility and the performance of the proposed procedure together with its impact on subsequent activities are illustrated using experimental results from real data.     

Estimation of parameters in a split functional model of geodetic observations (<Emphasis Type="Italic">M</Emphasis><Subscript>split</Subscript> estimation)

The paper presents a method of estimating parameters in two competitive functional models. The models considered here are concerned with the same observation set and are based on the assumption that an observation may result from a realization of either of two different random variables. These variables differ from one another at least in the main characteristic (for example, outliers can be realizations of one variable). A quantity that describes the opportunity of identifying a single observation with one random variable is assumed to be known. That quantity, called the elementary split potential, is strictly referred to the amount of information that an observation can provide about two competitive assumptions concerning the observation distribution. Parameter assessments that maximize the global elementary split potential (concerning all observations), are called M _split estimators. A generalization of M _split estimation presented in the paper refers to the theoretical foundation of M-estimation. An erratum to this article can be found at     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

基于全站仪工程控制测量的三维间接平差法

目前,利用高精度全站仪三维坐标测量功能建立三维工程控制网已成为一种趋势。针对这一问题的观测数据处理,提出了一种考虑平面观测数据和高程观测数据二者间整体性的三维间接平差方法,详细推导了水平角误差方程,天项距误差方程,斜距误差方程等关键步骤,较之已有的方法,严密性和实用性更强,具有一定的参考价值。