测量平差抗差化和效率分析

本文从充分利用可能得到的信息的观点出发,讨论了测量平差的抗差性和优效性及其他们之间的关系。阐述了抗差估计理论和经典估计理论的本质区别。提出了测量平差抗差化的基本原则,并对几种估计的抗差能力和效率作了比较。     

陀螺经纬仪时间观测数据函数模型的研究

本文运用最小二乘谱分析的原理,对陀螺经纬仪时间观测数据进行分析,提出了一种较以前采用的函数模型更为简便、实用的函数模型,并利用实测数据分析了测时定向方法的精度。     

用于生成DTM的改进的最小二乘匹配(LSM)算法

本文提供了一种用于生成DTM的LSM算法的数学模型。其特点是,引入了辐射线条件和共面条件的几何约束,以及辐射偏差和几何变形参数的虚拟观测方程。试验表明.改善了LSM算法的收敛性、稳定性和可靠性,因而提高了图像配准的质量。     

电测深曲线二维反演技术及应用

本文对应用有限元及阻尼最小二乘法进行电测深二维反演解释技术做了进一步的研究。在区域网格剖分方法、有限元系数矩阵的合成、雅可比矩阵的计算及法方程的解法等方面进行了改进。由此在微机和工作站上设计了程序软件，经过对理论电测深曲线及实测电测深曲线进行的二维反演解释，结果表明，在微机和工作站上实现的二维反演解释软件可以用于实际测深资料的解释。     

Fitting variogram models by weighted least squares

The method of weighted least squares is shown to be an appropriate way of fitting variogram models. The weighting scheme automatically gives most weight to early lags and down-weights those lags with a small number of pairs. Although weights are derived assuming the data are Gaussian (normal), they are shown to be still appropriate in the setting where data are a (smooth) transform of the Gaussian case. The method of (iterated) generalized least squares, which takes into account correlation between variogram estimators at different lags, offer more statistical efficiency at the price of more complexity. Weighted least squares for the robust estimator, based on square root differences, is less of a compromise.     

地震序列较强余震灰色及最小二乘拟合预测方法的应用研究

在收集、整理1966年～2002年8月我国(主要为大陆地区)183个5级以上地震序列资料的基础上，应用灰色预测和最小二乘拟合预测方法，对每一地震序列较强(显著)余震发震时间进行“硬性”检验预测。结果表明，2种预测方法对7级以上地震序列和6级地震序列的较强(显著)余震具有较好的预测效能，预测效能分别为76．2％和64．7％；而对5级地震序列预测效果则不甚明显。因此，灰色预测和最小二乘拟合预测方法主要着眼于对大(强)地震的较强(显著)余震预测，可望在地震现场大(强)震应急工作中发挥积极作用。     

砂土的抗剪强度与干密度的曲线拟合

利用曲线拟合的最小二乘法原理，确定砂土在某垂直压力下的抗剪强度与干密度的关系，并绘制曲线图，可避免人工处理数据的随意性，减小了误差。     

Hybrid Estimation of Semivariogram Parameters

Two widely used methods of semivariogram estimation are weighted least squares estimation and maximum likelihood estimation. The former have certain computational advantages, whereas the latter are more statistically efficient. We introduce and study a “hybrid” semivariogram estimation procedure that combines weighted least squares estimation of the range parameter with maximum likelihood estimation of the sill (and nugget) assuming known range, in such a way that the sill-to-range ratio in an exponential semivariogram is estimated consistently under an infill asymptotic regime. We show empirically that such a procedure is nearly as efficient computationally, and more efficient statistically for some parameters, than weighted least squares estimation of all of the semivariogram’s parameters. Furthermore, we demonstrate that standard plug-in (or empirical) spatial predictors and prediction error variances, obtained by replacing the unknown semivariogram parameters with estimates in expressions for the ordinary kriging predictor and kriging variance, respectively, perform better when hybrid estimates are plugged in than when weighted least squares estimates are plugged in. In view of these results and the simplicity of computing the hybrid estimates from weighted least squares estimates, we suggest that software that currently estimates the semivariogram by weighted least squares methods be amended to include hybrid estimation as an option.     

Robust H∞ control for aseismic structures with uncertainties in model parameters

This paper presents a robust H∞ output feedback control approach for structural systems with uncertainties in model parameters by using available acceleration measurements and proposes conditions for the existence of such a robust output feedback controller. The uncertainties of structural stiffness, damping and mass parameters are assumed to be norm-bounded. The proposed control approach is formulated within the framework of linear matrix inequalities, for which existing convex optimization techniques, such as the LMI toolbox in MATLAB, can be used effectively and conveniently. To illustrate the effectiveness of the proposed robust H∞ strategy, a six-story building was subjected both to the 1940 El Centro earthquake record and to a suddenly applied Kanai-Tajimi filtered white noise random excitation. The results show that the proposed robust H∞ controller provides satisfactory results with or without variation of the structural stiffness, damping and mass parameters.     

A Least-squares Window Curves Method for Interpretation of Magnetic Anomalies Caused by Dipping Dikes

We have developed a least-squares method to determine simultaneously the depth and the width of a buried thick dipping dike from residualized magnetic data using filters of successive window lengths. The method involves using a relationship between the depth and the half-width of the source and a combination of windowed observations. The relationship represents a family of curves (window curves). For a fixed window length, the depth is determined for each half-width value by solving one nonlinear equation of the form f (z) = 0 using the least-squares method. The computed depths are plotted against the width values representing a continuous curve. The solution for the depth and the width of the buried dike is read at the common intersection of the window curves. The method involves using a dike model convolved with the same moving average filter as applied to the observed data. As a result, this method can be applied to residuals as well as to measured magnetic data. Procedures are also formulated to estimate the amplitude coefficient and the index parameter. The method is applied to theoretical data with and without random errors. The validity of the method is tested on airborne magnetic data from Canada and on a vertical component magnetic anomaly from Turkey. In all cases examined, the model parameters obtained are in good agreement with the actual ones and with those given in the published literature.