复测水准网拟稳抗差解算模型

变形监测网平差的主要方法是秩亏平差法或拟稳平差法。本文基于抗差估计原理，首先推导了秩亏平差模型的抗差解式和拟稳抗差解，并推导了普通秩亏抗差解与拟稳抗差解之间的转换关系。使得拟稳抗差估计的解算简便直观。     

Spatial continuity measures for probabilistic and deterministic geostatistics

Geostatistics has traditionally used a probabilistic framework, one in which expected values or ensemble averages are of primary importance. The less familiar deterministic framework views geostatistical problems in terms of spatial integrals. This paper outlines the two frameworks and examines the issue of which spatial continuity measure, the covarianceC (h) or the variogram (h), is appropriate for each framework. AlthoughC (h) and (h) were defined originally in terms of spatial integrals, the convenience of probabilistic notation made the expected value definitions more common. These now classical expected value definitions entail a linear relationship betweenC (h) and (h); the spatial integral definitions do not. In a probabilistic framework, where available sample information is extrapolated to domains other than the one which was sampled, the expected value definitions are appropriate; furthermore, within a probabilistic framework, reasons exist for preferring the variogram to the covariance function. In a deterministic framework, where available sample information is interpolated within the same domain, the spatial integral definitions are appropriate and no reasons are known for preferring the variogram. A case study on a Wiener-Levy process demonstrates differences between the two frameworks and shows that, for most estimation problems, the deterministic viewpoint is more appropriate. Several case studies on real data sets reveal that the sample covariance function reflects the character of spatial continuity better than the sample variogram. From both theoretical and practical considerations, clearly for most geostatistical problems, direct estimation of the covariance is better than the traditional variogram approach.This paper was presented at MGUS 87 Conference, Redwood City, California, 14 April 1987.     

有偏估计与LS估计的比较与选择

从假设检验的角度研究测量平差Gauss-Markov模型中有偏估计与LS估计的选择问题。首先在均方误差准则下对目前应用最广泛的2种有偏估计－－岭估计和主成分估计与LS估计进行了比较研究，得到了岭估计、主成分估计优于LS估计的条件；然后运用统计方法对这些条件的成立进行了假设检验；最后通过数值实验说明，在一定显著性水平下当原假设被接受时，说明没有理由不相信采用有偏估计来代替LS估计的合理性，可认为采用有偏估计将对LS估计做出比较有效的改进，当原假设被拒绝时，说明对采用有偏估计的优越性产生了怀疑，此时建议仍采用LS估计。     

GPS基线非线性解算的精度评定方法

在GPS基线向量非线性平差解算的基础上，分析了单位权中误差估值，平差值的精度估计和基线长度的精度估计，并给出了简明的直接估计公式，为GPS基线向量非线性平差解算的精度评定提供了直接的评定公式。     

通道特性相关时带乘性噪声系统观测噪声的最优估计

研究在较弱的乘性噪声条件下系统观测噪声的最优估计问题,就乘性噪声为一般随机矩阵且各观测通道乘性噪声在同时刻相关的情形,给出了在线性最小方差意义下的观测噪声最优滤波算法和最优平滑算法。针对这些算法进行了仿真研究,仿真结果表明算法有较好的估计效果。     

广义岭估计的谱分解式和Liu型广义岭估计

首先从多参数岭估计,即广义岭参数人手,给出其谱分解形式。然后就岭估计会使估值偏差增大的问题,在Liu估计的基础上,推导出一种新的有偏估计方法——Liu型广义岭估计,并给出该方法的模型、解式。Liu型广义岭估计可以在尽量不增大偏差的同时改进病态性,获得更好的估值。最后通过算例进行了比较分析。     

两种激光粒度仪测量湖泊沉积物粒度结果的对比

湖泊沉积物粒度参数是湖泊沉积与环境演变研究中常用的环境指标,近年来激光粒度仪的发展和广泛使用更是促进了粒度指标的应用并在很大程度上提高了精度和效率.然而不同的激光粒度仪对于同一样品的测量会得到不同的结果,本文利用两种常见的激光粒度仪测量了西藏纳木错的两支岩芯,并对结果进行了对比分析.从仪器的重现性来看,Mastersizer2000型激光粒度仪要优于LS13320型激光粒度仪,而LS13320型激光粒度仪在对含量较少的细颗粒和粗颗粒的检测效果上优于Mastersizer2000型激光粒度仪;两种仪器得到的粒度参数d(0.1)和d(0.5)无论在变化趋势还是绝对数值上都较为相似,而d(0.9)的差异则较大.对比研究结果为利用湖泊沉积物粒度参数提取古环境信息研究提供了基础依据.     

An application of the deterministic variogram to design-based variance estimation

When estimating the mean value of a variable, or the total amount of a resource, within a specified region it is desirable to report an estimated standard error for the resulting estimate. If the sample sites are selected according to a probability sampling design, it usually is possible to construct an appropriate design-based standard error estimate. One exception is systematic sampling for which no such standard error estimator exists. However, a slight modification of systematic sampling, termed 2-step tessellation stratified (2TS) sampling, does permit the estimation of design-based standard errors. This paper develops a design-based standard error estimator for 2TS sampling. It is shown that the Taylor series approximation to the variance of the sample mean under 2TS sampling may be expressed in terms of either a deterministic variogram or a deterministic covariance function. Variance estimation then can be approached through the estimation of a variogram or a covariance function. The resulting standard error estimators are compared to some more traditional variance estimators through a simulation study. The simulation results show that estimators based on the new approach may perform better than traditional variance estimators.     

A statistical model for foraminiferal paleobathymetry, with application to the middle Miocene of the Hokuroku district, Japan

A paleobathymetric Bayes estimator, based on a uniform prior distribution of foraminiferal species over the depth range from 0 to 5000 m, is developed and calibrated from the occurrence of 35 species of foraminifera from present day sites on the Pacific and Indian Ocean seafloors. The species are grouped into 12 clusters in order to more nearly satisfy the independence assumptions of the model. Objective depth-independent criteria are developed for ruling certain samples inadmissible for estimation. The accuracy of the estimator is assessed by comparing estimated and measured depths for the admissible calibration samples, yielding a mean absolute error of estimation of between 300 and 400 m. The depth estimator is applied to foraminifera-bearing Miocene samples from the Hokuroku district of Japan, where paleobathymetric depths of between 3000 and 4000 m are indicated at the time of massive sulfide ore formation.