SBAS-InSAR形变反演的L曲线谱修正迭代法

提出一种基于L曲线的谱修正迭代法,通过绘制谱修正充分迭代过程中的L曲线,以最大曲率点对应的迭代估值作为最终估计结果.通过SBAS InSAR形变反演实验进行验证分析,结果表明,L曲线谱修正迭代法能够确定残差较小且稳定的估值,提高了谱修正迭代估计结果的精度和稳定性.     

Gauss-Markov模型参数的部分根方估计

在分析根方估计，Stein均压缩估计，Sclove部分压缩估计各自特点的基础上，提出了一种新的有偏估计－部分根方估计，并讨论了它的优良性质。计算结果表明，在设计阵呈病态时，部分根方估计确能改善LS估计。     

粗差估值型抗差估计

在测量数据处理中,解差受到观测空间和设计空间的双重影响。本文导出了粗差估值与解差的关系,进而得出了以粗差估值表示的ＬＳ估计的影响函数。通过对粗差估值加以限制,根据等价权原理,实现ＬＳ估计对观测空间和设计空间的抗差性。算例表明,粗差估值型抗差估计对观测空间和设计空间均具有良好的抗差效果。     

Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization

 Spatial and temporal behavior of hydrochemical parameters in groundwater can be studied using tools provided by geostatistics. The cross-variogram can be used to measure the spatial increments between observations at two given times as a function of distance (spatial structure). Taking into account the existence of such a spatial structure, two different data sets (sampled at two different times), representing concentrations of the same hydrochemical parameter, can be analyzed by cokriging in order to reduce the uncertainty of the estimation. In particular, if one of the two data sets is a subset of the other (that is, an undersampled set), cokriging allows us to study the spatial distribution of the hydrochemical parameter at that time, while also considering the statistical characteristics of the full data set established at a different time. This paper presents an application of cokriging by using temporal subsets to study the spatial distribution of nitrate concentration in the aquifer of the Lucca Plain, central Italy. Three data sets of nitrate concentration in groundwater were collected during three different periods in 1991. The first set was from 47 wells, but the second and the third are undersampled and represent 28 and 27 wells, respectively. Comparing the result of cokriging with ordinary kriging showed an improvement of the uncertainty in terms of reducing the estimation variance. The application of cokriging to the undersampled data sets reduced the uncertainty in estimating nitrate concentration and at the same time decreased the cost of the field sampling and laboratory analysis. Received: 23 July 1997 · Accepted: 31 March 1998     

A Maximum-Entropy Method for Estimating the Spectrum

Based on the maximum-entropy(ME)principle,a new power spectral estimator for random waves is derived in the form of S～(ω)=a/8H~2(2π)~(d 1)ω~-~((d 2))exp[-b(2π/ω)~n],by solving a variational problem subject to some quite general constraints.This robust method is comprehensive enough to describe the wave spectra even in extreme wave conditions and is superior to periodogram method that is not suitable to process comparatively short or intensively unsteady signals for its tremendous boundary effect and some inherent defects of FFT.Fortunately,the newly derived method for spectral estimation works fairly well,even though the sample data sets are very short and unsteady,and the reliability and efficiency of this spectral estimator have been preliminarily proved.     

Assessment of climatic variability in the catchments of Himalayan Lake,Jammu & Kashmir

The present investigation was conducted to analyse the trends in hydrometeorological data to addresses how these trends impacted on the water extent of Wular Lake, Kashmir. Temporal changes in the lake surface area have been analysed, using the Landsat satellite data. The nonparametric Mann–Kendall and Sen’s methods were used to determine trends in hydrometeorological data. In addition, trends in extreme indices of frost days (FD), summer days, days with rainfall > 10 mm (R10), days with rainfall > 20 mm (R20) and dry spell for temperature and precipitation were also analysed. The results suggest an overall decrease in extreme rainfall events, R10 and R20, and annual precipitation pattern with increase in maximum and minimum annual average temperatures. Furthermore, the analysis of extreme temperature events suggests warming trend with increased number of warm days and decreased number of FD. With regard to water extent of the lake, an intense decreasing trend was observed.     

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响应变量随机缺失下部分线性单指标模型的非参数判别检验

研究了响应变量随机缺失情况下部分线性单指标模型的非参数部分检验问题,检验非参数部分预测变量同响应变量之间是否存在非线性关系.用参数和非参数函数的借补估计对缺失响应变量进行插值,并基于借补估计构造了广义似然比检验统计量,证明了其渐近分布性质.     

重复中位数估计Robust算法在长周期大地电磁中的应用

将基于重复中位数估计的Robust算法应用于长周期大地电磁阻抗张量估算中。与传统Robust估计(M估计)相比,重复中位数估计求解过程不需要重设参量,并且提高了算法对时间域原始资料中噪声干扰的容限,使算法的理论崩溃值从30%提高到50%,也能得到准确的估算结果。龙门山地区实测资料的处理结果表明,基于重复中位数估计的Robust算法应用于阻抗张量的求解具有较强的优越性。