Stokes波的谱特性分析

应用理论推导及数值计算方法，对Stokes随机波的谱特性进行了分析。首先将波面方程，海水质点水平速度用一阶波面分量的非线性组合表示，应用平稳随机高阶短的降阶计算法则，得到了波面方程及海水质点水平速度与一阶波面分量的自相关函数之间的关系，从而确定了Stokes随机波浪的波浪谱密度及海水质点水平速度和加速度谱密度，进而求得有关波浪要素的均方根值。文章还应有数值计算方法，分析了波浪基本参数对均方根值的影响。     

A New Retracking Technique for Brown Peaky Altimetric Waveforms

A new Brown-Peaky (BP) retracker has been developed for peaky waveforms that usually appear within ～10 km to the coastline. The main feature of the BP is that it fits peaky waveforms using the Brown model without introducing a peak function. The retracking strategy first detects the peak location and width of a waveform using an adaptive peak detection method, and then estimates retracking parameters using a weighted least squares (WLS) estimator. The WLS assigns a downsized weight to corrupted waveform gates, but an equal weight to other normal waveform gates. The BP retracker has been applied to 4-year Jason-1 waveform (2002–2006) in two Australian coastal zones. The results retracked by BP, MLE4 and ALES retrackers have been validated against tide-gauge observations located at Burnie, Lorne and Broome. The comparison results show that three retrackers have similar performance over open oceans with the correlation coefficient (～0.7) and RMSE (～13 cm) between altimetric and tide-gauge sea levels for distance >7 km offshore. The main improvement of BP retracker occurs for distance ≤7 km to the coastline, where validation results indicate that data retracked by BP are more accurate (15–21 cm) than those by ALES (16–24 cm) and MLE4 (19–37 cm).     

灰色稳健总体最小二乘估计及高铁路基变形预测

最小二乘估计和部分变量误差模型的总体最小二乘估计不具备抵御粗差的能力。鉴于粗差可能同时出现在灰色白化微分方程的观测值和系数矩阵中,本文提出基于IGGⅢ抗差方案的部分变量总体最小二乘稳健估计。结合仿真数据和高铁路基观测数据,系统地比较稳健最小二乘、部分变量总体最小二乘、本文算法参数估计结果和算法稳定性。结果表明,本文算法预测精度高,可以应用到高铁路基沉降预测中。     

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。     

晶体点阵参数最小二乘法计算程序

作者以最小二乘法理法导出了晶体点阵参数计算的公式,并顾及了X射线粉晶衍射图测定所引起的系统误差修正,从而得到点阵参数的最佳估值。以导出的公式为数学模型编制了PC-1500微机的扩展BASIC语言程序,可适用于各晶系点阵参数的计算,使用方便。     

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。     

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.     

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.     

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.