L1范数与IQR统计量组合的GNSS坐标序列粗差探测算法

GNSS坐标时间序列中不可避免地含有粗差,未剔除的粗差将会导致参数估计有偏。因此,粗差探测与剔除是GNSS坐标序列分析中一项重要的数据预处理工作。针对GNSS坐标时间序列特点,提出了一种将L1范数(L1-norm)估计与四分位距统计量IQR(interquartile range)组合的移动开窗粗差探测算法,称之为L1_Mod IQR。该方法的主要思想是,首先利用L1范数估计得到较"真实"的残差,然后再对残差采用IQR统计量进行粗差探测。将L1_Mod IQR法与"3σ"法、基于最小二乘的τ检验法等粗差探测算法进行了模拟计算与对比,验证了该算法的有效性。进一步采用L1_Mod IQR算法对中国区域10个IGS站的高程时间序列进行了分析,结果表明中国区域IGS站高程序列的粗差剔除率最小为0.1%,最大为2.6%。并且以WUHN站为例与SOPAC提供的结果进行了对比,结果表明SOPAC提供的"Clean"数据仍含有大量的粗差,而L1_Mod IQR算法能够有效地剔除粗差。     

Analysis of trends of annual and seasonal precipitation from 1956 to 2000 in Guangdong Province,China

Abstract

The trends of annual, seasonal and monthly precipitation in southern China (Guangdong Province) for the period 1956–2000 are investigated, based on the data from 186 high-quality gauging stations. Statistical tests, including Mann-Kendall rank test and wavelet analysis, are employed to determine whether the precipitation series exhibit any regular trend and periodicity. The results indicate that the annual precipitation has a slightly decreasing trend in central Guangdong and slight increasing trends in the eastern and western areas of the province. However, all the annual trends are not statistically significant at the 95% confidence level. The average precipitation increases in the dry season in central Guangdong, but decreases in the wet season, meaning that the precipitation becomes more evenly distributed within the year. Furthermore, the analysis of monthly precipitation suggests that the distribution of intra-annual precipitation changes over time. The results of wavelet analysis show prominent precipitation with periods ranging from 10 to 12 years in every sub-region in Guangdong Province. Comparing with the sunspot cycle (11-year), the annual precipitation in every sub-region in Guangdong province correlates with Sunspot Number with a 3-year lag. The findings in this paper will be useful for water resources management.

Editor Z.W. Kundzewicz; Associate editor Sheng Yue

Citation Dedi Liu, Shenglian Guo, Xiaohong Chen and Quanxi Shao, 2012. Analysis of trends of annual and seasonal precipitation from 1956 to 2000 in Guangdong Province, China. Hydrological Sciences Journal, 57 (2), 358–369.     

南堡凹陷沉积岩化学元素分布特征及多元统计方法研究

对南堡凹陷Ed1亚段、Ng下、Ng上、Nm底地层的75个沉积岩样品进行了主量、微量元素分析,结果表明粒度效应造成砂岩和泥岩元素丰度明显不同,砂岩除富集K、Na、Sr、Ba、Si外,其余元素均较泥岩亏损.根据地层垂向上元素丰度由高—低—高的变化规律,可将这4个亚段分为Ed1、Ng下和Ng上、Nm底3个阶段,与凹陷构造演化特征响应.因子分析、聚类分析等多元统计方法表明,表生环境中元素具有不同的迁移富集规律,Ca、Na、Sr等具有较强的活动性,Zr、Hf等少数元素则在粗粒级中含量较高,而其余元素则多在黏土粒级中富集.元素在表生环境中地球化学行为的不同主要受化学结构特征控制,沉积环境对其也有影响.     

中国西南低空急流活动的统计分析

参照北美研究低空急流气候特征的方法，对中国低空急流进行重新定义，实现了低空急流定义的统一，并在此基础上对低空急流活动特征进行了分析。结果表明：中国低空急流主要有两个活动中心，分别位于江南地区和东北地区；江南地区的低空急流活动主要有两个活跃期，分别为1-4月和6-7月；东北地区的低空急流活动主要表现为2月、8-9月两个活动较弱期；江南地区的低空急流主要在6-8月表现出独立的急流轴，东北地区则主要在8月份；汛期低空急流活动与长江流域区域性暴雨有伴随性，多雨年急流次数很多，少雨年则很少；长江中下游及两湖盆地附近急流活动有明显的日变化。     

基于专家经验值的滑坡易发性评价——以四川平武高坪铺库区为例

滑坡灾害的易发性评价方法和模型有很多,不同的学者又对其进行了不同的改进,形成了不同的方法。究竟哪种方法更好,还需要结合实际进行方法的比较分析。论文应用基于专家经验值的二元统计法分析了四川平武高坪铺库区(约32 km2)高程、岩性、与居民点距离、与河流距离、坡度和坡向6种因素对滑坡灾害影响,并在此基础上进行了滑坡易发性评价。     

面向目标的起伏地表组合震源延时参数计算方法

组合震源技术通过调节延迟激发时间和震源埋藏深度能够实现地震波场的定向传播,提高地震波的照明能量和地震数据信噪比.然而,目前国内外对基于特定目标的组合震源最佳聚焦方向的选取以及起伏地表组合震源定向理论的研究相对较少.在聚焦方向选取方面,目前基本是利用多种不同延时参数的组合震源分别进行试验,从而确定较好的组合震源延时参数.本文则利用爆炸反射面原理,在目的层界面上均匀布置震源同时激发地震波,能够产生垂直于地层界面传播的平面波,当初至波传播至地表时进行地表方向统计.根据互易性原理,以初至波传播方向的反方向为聚焦方向,激发产生的地震波主波束方向将垂直入射到目的层界面上,从而精确的计算出组合震源的最佳聚焦方向.在组合震源定向方面,目前主要是利用组合震源方向因子公式进行计算,但该方法要求震源布置在同一条直线上,并不能满足起伏地表的应用要求.本文以惠更斯-菲涅尔原理为理论基础,提出通过炮点向量在聚焦方向上的投影来确定组合震源传播至虚拟波前的走时,从而确定任意起伏地表组合震源的延迟激发时间.该方法不仅能够计算出沿给定地质模型的目的层界面垂直入射的组合震源聚焦方向,也能够确定任意起伏地表情况下形成沿该聚焦方向定向传播地震波场的组合震源延迟激发时间.因此,本文提出的面向目标的起伏地表组合震源延时参数计算方法对组合震源数据采集具有理论与实践意义.

     

呼和浩特市近50a气温变化特征分析

利用呼和浩特市1961—2010年气温资料,用线性回归、方差分析等统计方法分析呼市气温的年代际变化特征以及季节性变化规律,指出近50a来平均气温以每0.57℃/10a的幅度增高。     

基于遗传算法和统计学的电阻率测深二维反演研究

本文介绍了一种以遗传算法和统计学为基础的反演方法,该方法利用一维反演结果的统计学特征确定地层电性结构并设计初始模型,使用遗传算法修改模型,经过二维有限单元法正演达到拟合误差最小,从而完成电阻率测深数据的二维反演.这个方法的优点在于不需要解大型线性方程组,降低计算量,先判断地层电性结构,反演后给出用地电断面表示的直观的解释结果.理论模型试算和模型实验证明该方法效果较好.     

基于统计的小波包分析的输电塔结构损伤预警

本文针对输电塔结构的损伤特点,提出了基于统计过程控制理论和小波包分析实现输电塔结构损伤预警的方法。首先将振动测试信号进行小波包分解,然后以小波包系数节点能量作为识别损伤的特征向量建立相应的损伤因子,通过统计过程控制理论计算其单侧置信上限,实现输电塔结构的损伤预警。为了证明该方法,对一个三层输电塔结构模型进行了试验研究,结果表明该方法能够有效地识别损伤的发生。     

Clustering of temporal event processes

A temporal point process is a sequence of points, each representing the occurrence time of an event. Each temporal point process is related to the behavior of an entity. As a result, clustering of temporal point processes can help differentiate between entities, thereby revealing patterns of behaviors. This study proposes a hierarchical cluster method for clustering temporal point processes based on the discrete Fréchet (DF) distance. The DF cluster method is divided into four steps: (1) constructing a DF similarity matrix between temporal point processes; (2) constructing a complete linkage hierarchical tree based on the DF similarity matrix; (3) clustering the point processes with a threshold determined by locating the local maxima on the curve of the pseudo-F statistic (an index which measures the separability between clusters and the compactness in clusters); and (4) identifying inner patterns for each cluster formed by a series of dense intervals, each of which contains at least one event of all processes of the cluster. The contributions of the article are: (1) the proposed DF cluster method can cluster temporal point processes into different groups and (2) more importantly, it can identify the inner pattern of each cluster. Two synthetic data sets were created to illustrate the DF distance between temporal point process clusters (the first data set) and validate the proposed DF cluster method (the second data set), respectively. An experiment and a comparison with a method based on dynamic time warping show that DF cluster successfully identifies the preconfigured patterns in the second synthetic data set. The cluster method was then applied to a population migration history data set for the Northern Plains of the United States, revealing some interesting population migration patterns.