散乱数据插值的HASM方法

本文运用高精度曲面建模(HASM)方法,研究了空间散乱数据插值算法,并以陕西咸阳彬县大佛寺煤矿区的实测数据为案例,使用交叉统计检验方法比较分析HASM方法与地理信息系统(GIS)领域中常用传统插值方法的空间插值结果。结果表明,HASM方法具有较高的精度。本文还分析了传统常用插值方法的一些统计结果,得出一些结论,这些结论对于使用常用传统GIS空间插值方法的研究者有很好的参考作用。     

Generalized cross-validation for covariance model selection

A weighted cross-validation technique known in the spline literature as generalized cross-validation (GCV), is proposed for covariance model selection and parameter estimation. Weights for prediction errors are selected to give more importance to a cluster of points than isolated points. Clustered points are estimated better by their neighbors and are more sensitive to model parameters. This rational weighting scheme also provides a simplifying significantly the computation of the cross-validation mean square error of prediction. With small- to medium-size datasets, GCV is performed in a global neighborhood. Optimization of usual isotropic models requires only a small number of matrix inversions. A small dataset and a simulation are used to compare performances of GCV to ordinary cross-validation (OCV) and least-squares filling (LS).     

A comparison between cokriging and ordinary kriging: Case study with a polymetallic deposit

Cokriging is applied to the estimation of mineral resources in a polymetallic deposit. Several major steps, which should be taken in using cokriging, are highlighted as necessary practical considerations. The case study is related to an ultramafic copper-nickel deposit. Six elements, Cu, Ni, Au, Ag, Pt, and Pd, occurring in the deposit, are partitioned into three subgroups and the elements within each group are simultaneously estimated on the basis of over 4000 drill assays. A comparison was made between ordinary kriging and cokriging methods through cross-validation. The results show that cokriging has significantly improved the estimates of resources by reducing the overall estimation error by over 15% and the variance of error by over 20%.     

确定大地水准面的Tikhonov最小二乘配置法

LSC法（最小二乘配置法）因能融合不同种类重力观测数据确定大地水准面的特性而受到广泛关注,但由于协方差矩阵存在病态性,微小的观测误差将被协方差矩阵的小奇异值放大,导致计算的配置结果不稳定且精度偏低。本文提出Tikhonov_LSC法,即在LSC法中引入Tikhonov正则化算法,基于GCV法选择协方差矩阵的正则化参数,利用正则化参数修正协方差矩阵的小奇异值,以抑制其对观测误差的放大影响。基于Tikhonov_LSC法计算大地水准面,能有效提高其稳定性和精度。通过以EGM2008重力场模型分别计算山区、丘陵和海域重力异常作为基础数据确定相应区域大地水准面的实验,验证了该方法的有效性。     

CVVF方法用于GPS多路径效应的研究

将交叉证认法与Vondrak数字滤波器相组合,提出一种分离测量资料中信号与噪声的新方法,即CVVF方法,并将该方法用于GPS多路径效应的研究中.对数字模拟试验和实际观测资料的分析表明,该方法能最大限度地削弱测量的随机误差,使资料序列中的信号和噪声合理分离.同时,利用GPS多路径效应周期性重复的特性,可有效地削弱多路径效应对观测结果的影响,从而提高GPS定位精度.     

几种建立DEM模型插值方法精度的交叉验证

建立DEM模型时需要对离散的高程点进行空间插值,实现这一目的的插值方法有很多种。文章主要选择了6种常用的插值方法,分别在平原、丘陵和高山几种不同复杂程度的环境下对其插值的精度使用交叉验证方法进行评估。结果表明,同样的插值方法对不同复杂程度的地形效果是不一致的,多种插值方法中以克里金插值方法的插值适用性最强,精度最高。     

一种基于Cross-Validation的盲图像恢复方法

图像盲复原所面临的主要问题是可利用的信息不足，目前已有的图像盲复原算法一般都是有先验知识，如非负和有限支持域的限制。但在实际中目标的支持域是未知的。文中介绍了CV(cross-validation)的基本原理，给出了一种基于CV原理的支持域确定方法的详细步骤；并针对计算大的问题提出了一定的改进。最后给出了实验结果，证明CV确定支持域的方法对于图像盲复原是有一定价值的。     

Diatoms as indicators of climatic and limnological change in Swedish Lapland: a 100-lake calibration set and its validation for paleoecological reconstructions

This study investigated the distribution of subfossil diatom assemblages in surficial sediments of 100 lakes along steep ecological and climatic gradients in northernmost Sweden (Abisko region, 67.07° N to 68.48° N latitude, 17.67° E to 23.52° E longitude) to develop and cross-validate transfer functions for paleoenvironmental reconstruction. Of 19 environmental variables determined for each site, 15 were included in the statistical analysis. Lake-water pH (8.0%), sedimentary loss-on-ignition (LOI, 5.9% and estimated mean July air temperature (July T, 4.8%) explained the greatest amounts of variation in the distribution of diatom taxa among the 100 lakes. Temperature and pH optima and tolerances were calculated for abundant taxa. Transfer functions, based on WA-PLS (weighted averaging partial least squares), were developed for pH (r² = 0.77, root-mean-square-error of prediction (RMSEP) = 0.19 pH units, maximum bias = 0.31, as assessed by leave-one-out cross-validation) based on 99 lakes and for July T (r² = 0.75, RMSEP = 0.96 °C, max. bias = 1.37 °C) based on the full 100 lake set. We subsequently assessed the ability of the diatom transfer functions to estimate lake-water pH and July T using a form of independent cross-validation. To do this, the 100-lake set was divided in two subsets. An 85-lake training-set (based on single limnological measurements) was used to develop transfer functions with similar performance as those based on the full 100 lakes, and a 15-lake test-set (with 2 years of monthly limnological measurements throughout the ice-free seasons) was used to test the transfer functions developed from the 85-lake training-set. Results from the intra-set cross-validation exercise demonstrated that lake-specific prediction errors (RMSEP) for the 15-lake test-set corresponded closely with the median measured values (pH) and the estimations based on spatial interpolations of data from weather stations (July T). The prediction errors associated with diatom inferences were usually within the range of seasonal and interannual variability. Overall, our results confirm that diatoms can provide reliable and robust estimates of lake-water pH and July T, that WA-PLS is a robust calibration method and that long-term environmental data are needed for further improvement of paleolimnological transfer functions.