变差函数在地下水位估值中的应用研究

用数值方法进行地下水水资源评价时 ,需要给出每个节点上的初始地下水位值。本文着重阐述了区域化变量和变差函数及其计算步骤 ,指出变差函数在分析地下水位的空间变异的重要性     

结合软硬数据的连续型多点地质统计渗透率图像模拟方法

渗透率图像的预测模拟对油田的开发具有重要意义。充分利用条件数据可以提高渗透率模拟的精度,因此提出一种基于连续型多点地质统计法和软硬数据的渗透率图像模拟方法。首先,利用过滤器得分操作对渗透率图像降维,所有的过滤器得分形成了一个过滤器得分空间;其次,通过两步划分法将得分空间划分,得到每个非空得分类的特征,形成一个“特征库”;最后,通过自定义的距离函数从“特征库”提取已知模式的特征,以完成对节点的模拟。比较各情况下渗透率模拟图像可以看出,将软硬数据同时作为条件数据的模拟图像由于采用了较为丰富的条件数据信息,因此与真实情况下的渗透率分布在结构特征上最为接近。     

An optimal sampling approach to modelling whole-body vibration exposure in all-terrain vehicle driving

Abstract

Exposure to whole-body vibration (WBV) presents an occupational health risk and several safety standards obligate to measure WBV. The high cost of direct measurements in large epidemiological studies raises the question of the optimal sampling for estimating WBV exposures given by a large variation in exposure levels in real worksites. This paper presents a new approach to addressing this problem. A daily exposure to WBV was recorded for 9–24 days among 48 all-terrain vehicle drivers. Four data-sets based on root mean squared recordings were obtained from the measurement. The data were modelled using semi-variogram with spectrum analysis and the optimal sampling scheme was derived. The optimum sampling period was 140 min apart. The result was verified and validated in terms of its accuracy and statistical power. Recordings of two to three hours are probably needed to get a sufficiently unbiased daily WBV exposure estimate in real worksites. The developed model is general enough that is applicable to other cumulative exposures or biosignals.

Practitioner Summary: Exposure to whole-body vibration (WBV) presents an occupational health risk and safety standards obligate to measure WBV. However, direct measurements can be expensive. This paper presents a new approach to addressing this problem. The developed model is general enough that is applicable to other cumulative exposures or biosignals.     

基于地质统计学纹理的遥感影像分类研究进展

传统的基于像素与像素基础上的遥感影像光谱分类方法忽视了邻近像素值之间潜在有用的空间信息,三十多年来,人们一直都在谋求利用遥感影像本身所固有的空间信息以加强光谱分类,尽管从事该方面研究的人一直都很少,其实现的手段主要依靠对原始影像的滤波,滤波的一般方式是生成纹理波段以指导接下来的分类。近年来,变异函数被用来表达空间依赖性,并取代简单的方差滤波成为了纹理分类的主要手段,在这篇综述性的论文中,笔者主要讨论了两类将基于地质统计学的纹理信息集成到遥感影像分类中的应用,它们代表了当前遥感影像纹理分类的主流。     

地质统计学法在露天矿勘探网度优化中的应用

论述了应用地质统计学法进行露天矿勘探网度优化的基本原理和方法，以某露天矿为例，建立了该露天矿的矿床模型，得出了该露天矿主采煤层的煤层厚度、灰分和硫分等地质变量的理论变差函数模型，并对其变异规律进行了分析和评价。根据估计方差是勘探网度和网型的函数这一原理，模拟得出了最佳勘探网度，并对该露天矿首采区和后续区的勘探网度进行了优化和评价，为采矿设计提供矿床地质条件的可靠性论证。     

基于Kriging方法的空间散乱点插值

给出的一种距离加权插值方法借鉴地质统计学中的Ｋｒｉｇｉｎｇ方法,在空间数据场满足给定的统计分布特征的前提假设下进行插值。通过对帽状曲面函数的插值结果的观察,可以看出该方法可以获得良好的插值效果。     

基于变差函数和方向小波的噪声图像边缘检测方法

为了检测受噪声污染图像的边缘,提出了一种基于变差函数和方向小波的多尺度边缘检测新方法.首先,在我们前期研究成果的基础上,基于变差函数理论,提出了图像中45度和135度方向边缘的判别准则.然后根据图像中边缘的方向性,选择合适的方向小波旋转参数,再利用方向小波对各个子区域进行不同尺度小波变换.仿真实验表明,对于受高斯白噪声污染的图像,所提出的边缘检测方法无论在边缘定位的准确性还是在去除伪边缘点方面,均优于传统的小波边缘检测方法.     

Kriging插值中条件数据点个数的选择

Kriging插值时使用的数据量越大,插值结果越可靠,但数据量对耗时的影响则呈指数增加。为了保证尽量多的数据参与Kriging插值,又能维持合理的计算耗时,必须正确地选择条件数据点个数。通过对地质统计学中Kriging插值算法的深入解析,利用Kriging插值中产生的平均权系数分析插值过程中的条件数据点个数的选择,当平均权系数最小时,找到这个平衡点,让插值过程中的计算在保证计算精度的同时,尽量减少计算耗时。并改变Kriging插值的维数、网格大小和变差函数的变程,分析所选择的条件数据点个数对此3种因素的敏感度,得到条件数据点个数选择40～60最为合适,主要的影响因素为计算维数。     

矿业地质统计学在我国的应用现状及对影响克立格估计的若干因素的研究

地质统计学已用于我国多个矿床的储量计算。变程Ａ是一个很稳健的变异参数，块金Ｃ０、基台Ｃ以及估计邻域的大小较大地影响着克立格估计。     

A flexible lag definition for experimental variogram calculation

Inferring the experimental variogram used in geostatistics commonly relies on the method-of-moments approach. Ideally, the available data-set used for calculating the experimental variogram should be drawn from a regular pattern. However, in practice the available data-set is typically sampled over a sparse pattern at irregularly spaced locations. Hence, some binning of the variogram cloud is required to obtain fair estimates of the experimental variogram. Grouping of the variogram data pairs as a result of conventional binning depends on parameters such as the main anisotropic directions and a regular definition of the lag vectors. These parameters are not based on the configuration of the variogram data pairs in the variogram cloud but on a segment of it that is arbitrarily predefined. Therefore, the conventional experimental variogram estimation approach is biased because of the strict configuration of the bins over the variogram cloud. In this paper, a new method of estimating experimental variograms is proposed. Lag vectors and their tolerances are decided in the proposed method from information in the variogram cloud: they are not influenced by any predefined directions. The proposed methodology is a well-founded, practicable and easy-to-automate approach for experimental variogram calculation using an irregularly sampled data-set. Comparison of results from the new method to those from the traditional approach is very encouraging.