Variance–Covariance Matrix of the Experimental Variogram: Assessing Variogram Uncertainty

Assessment of the sampling variance of the experimental variogram is an important topic in geostatistics as it gives the uncertainty of the variogram estimates. This assessment, however, is repeatedly overlooked in most applications mainly, perhaps, because a general approach has not been implemented in the most commonly used software packages for variogram analysis. In this paper the authors propose a solution that can be implemented easily in a computer program, and which, subject to certain assumptions, is exact. These assumptions are not very restrictive: second-order stationarity (the process has a finite variance and the variogram has a sill) and, solely for the purpose of evaluating fourth-order moments, a Gaussian distribution for the random function. The approach described here gives the variance–covariance matrix of the experimental variogram, which takes into account not only the correlation among the experiemental values but also the multiple use of data in the variogram computation. Among other applications, standard errors may be attached to the variogram estimates and the variance–covariance matrix may be used for fitting a theoretical model by weighted, or by generalized, least squares. Confidence regions that hold a given confidence level for all the variogram lag estimates simultaneously have been calculated using the Bonferroni method for rectangular intervals, and using the multivariate Gaussian assumption for K-dimensional elliptical intervals (where K is the number of experimental variogram estimates). A general approach for incorporating the uncertainty of the experimental variogram into the uncertainty of the variogram model parameters is also shown. A case study with rainfall data is used to illustrate the proposed approach.     

Implications of Habitat Fragmentation on Climate Change-Induced Forest Migration

A rapid warming caused by the release of greenhouse gases could result in the displacement of climatic controls that limit the current ranges of many species. Projected northward displacement for loblolly pine is over 400 km, with only a narrow region of overlap between the current and projected future range limits. A model of dispersal developed for loblolly pine is presented. The model utilizes a GIS to assess the critical influence of land use patterns on climate change-induced migration through modern landscapes. Results from two relatively large (150 × 150 km) study areas in the eastern United States suggest that potential migration rates may fall short by at least an order of magnitude of that necessary to track projected range shifts. Management options of species transplanting and the establishment of greenways are explored with the model. Species that are unable to keep pace with changing range limits may experience a reduction in population size and exist in climatic disequilibrium.     

人造金刚石孕镶钻头井底破岩功率的研究及其应用

笔者把井底破岩过程看作一个叫做“黑箱”的控制系统,它的输入(P、n、Q等)和输出(扭矩M,功率N等)之间的规律性,可借助计算机拟合出来。笔者通过大量实验建立的功率公式可用于预测和控制。多次试验表明,若井底破岩功率超过了预测区间的上限(95%可信度),则烧钻很快发生。     

海相碳酸盐岩烃源岩评价的有机碳下限问题

随着勘探程度的提高,深部石油天然气勘探在我国将越来越重要.我国碳酸盐岩地层分布广,埋藏深,其生烃评价是深部石油天然气勘探不可回避的问题.针对我国科学家在这一问题上的争论,提出了海相碳酸盐岩烃源岩评价的三个下限标准,即最低烃源岩的有机碳下限、有效烃源岩有机碳下限和形成大规模油气田的烃源岩有机碳下限.认为以往的争论是最低烃源岩、有效烃源岩下限的争论.通过文献整理、实验研究和计算,认为海相碳酸盐岩普遍存在的Ⅱ型有机质,其最低、有效油源岩的下限值可能在0.1%和0.4%左右,最低、有效气源岩下限可能在0.1%和0.3%左右,大规模油气田形成的TOC应在1%以上.     

基于MATLAB的关联规则空间数据挖掘探讨——以济南市平阴县安城乡为例

本文根据关联规则挖掘方法,在Matlab中编写程序,时土地利用现状数据库进行数据挖掘。计算了不同用地类型与坡度之间的支持度和置信度,得出关联规则。通过平阴县安城乡的实证,取得了较好的结果。     

Data Assimilation Method Based on the Constraints of Confidence Region

The ensemble Kalman filter(En KF) is a distinguished data assimilation method that is widely used and studied in various fields including methodology and oceanography. However, due to the limited sample size or imprecise dynamics model, it is usually easy for the forecast error variance to be underestimated, which further leads to the phenomenon of filter divergence.Additionally, the assimilation results of the initial stage are poor if the initial condition settings differ greatly from the true initial state. To address these problems, the variance inflation procedure is usually adopted. In this paper, we propose a new method based on the constraints of a confidence region constructed by the observations, called En CR, to estimate the inflation parameter of the forecast error variance of the En KF method. In the new method, the state estimate is more robust to both the inaccurate forecast models and initial condition settings. The new method is compared with other adaptive data assimilation methods in the Lorenz-63 and Lorenz-96 models under various model parameter settings. The simulation results show that the new method performs better than the competing methods.     

渤海和南海海域极值风速的置信区间

目前我国海洋台站积累的实测日最大风速资料长度还较短，本文提出采用实测日4次（或3次）平均风速产生日最大风速样本的方法。认为极值风速是随机变量，采用本文方法产生的日最大风速样本，统计推算了渤海和南海海域15个台站的若干年一遇极值风速的统计参数，得到了不同重现期的不同置信水平的极值风速区间，为更合理地确定海洋平台结构设计风荷载提供了依据。