SOFTWARE REVIEWS

ATLAS*GIS , Version 1.0. ATLAS*MapMaker , Version 4.0. FIVFIV-SINSIN , Release 9.0. Geo-EAS , Version 1.2.1. Evan Englund Sim City , Version 1.0 Statistix , Version 3.1.     

基于地球化学组合求异的津巴布韦奇马尼马尼地区成矿预测

如何有效地解释评价异常是化探找矿取得成功的关键,传统的地球化学异常处理方法一般是以单一元素的含量值与背景值的差异来圈定异常,因子-泛克立格求异模型则建立在矿床主成矿期元素共生组合与地球化学异常之间的对应关系。采用因子-泛克立格模型对津巴布韦东部奇马尼马尼地区进行成矿预测,结果表明:奇马尼马尼地区存在7种元素组合,分别对应于不同的岩性组合,F4和F6元素组合异常可作为区内热液成矿指示标志,与东部塔卡金矿套合程度高。利用该模型圈定了5处找矿远景区,Ⅰ号远景区为铜镍金钛铁矿成矿有利区,Ⅱ号远景区具有与塔卡金矿相似的地球化学组合背景,Ⅲ号、Ⅳ号和Ⅴ号远景区则具寻找热液型金铜矿及多金属矿的潜力。     

滑动窗口的普通克立格方法在降水量插值中的应用

利用2009年全国2200个观测站降水量资料,使用滑动窗口的普通克立格方法对降水量资料进行格点化估计。针对滑动窗口的普通克立格方法在降水量格点化应用中存在的问题,设计了3种技术处理的试验方案。比较了全局搜索与方位邻近方法的误差,讨论了最大影响半径及屏蔽效应对插值效果的作用。提出了方位邻近法的样本点选择策略,结果表明,相对传统滑动窗口的普通克立格方法较常使用的全局搜索法而言,方位邻近法显著降低了计算资源的耗用,同时又具有较高的插值精度,特别是在站点密集地区有突出的优势;试验结果也表明:变程为4°～5°的经(纬)线弧长时,在方位邻近法下,我国大部分区域有较好的插值效果;屏蔽效应弱,ε取值为0.1时,降水量插值准确率较高,随着ε增大,插值误差也逐渐增大。     

Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan,Taiwan

This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers. Copyright © 2004 John Wiley & Sons, Ltd.     

Comparison between different kriging estimators

Six different geostatistical estimators (linear kriging, lognormal kriging, and disjunctive kriging, each with and without a nonbias, i.e., universality condition) were compared using data from a polymetallic deposit in Algeria. The differences between estimators with and without the nonbias condition were far more pronounced than between the different kriging methods. This highlights the importance of choosing an appropriate stationarity model for the data. The criterion concerning kriging weight of the mean in simple kriging, proposed by Remacre (1984, 1987) and Rivoirard (1984) was found to be helpful for determining blocks where the choice of the stationarity hypothesis was critical.     

Random vectors and spatial analysis by geostatistics for geotechnical applications

Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators: kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics to spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings.     

Comparison of two techniques for applying disjunctive kriging: The Gaussian anamorphosis model versus the direct statistical inference of the bivariate distributions

Indicator cokriging is an alternative to disjunctive kriging for estimation of spatial distributions. One way to determine which of these techniques is more accurate for estimation of spatial distributions is to apply each to a particular type of data. A procedure is developed for evaluation of disjunctive kriging and indicator cokriging for such an application. Application of this procedure to earthquake ground motion data found disjunctive kriging to be at least as accurate as indicator cokriging for estimation of spatial distributions of peak horizontal acceleration. Indicator cokriging was superior for all other types of earthquake ground motion data.     

Trend-surface analysis

This paper is largely an exposition of the work of the French geostatistian G. Matheron and his school in English and at a simpler mathematical level. The probability theory on which it is based is essentially all contained in the references cited, most of which will be unfamiliar to mathematical geologists. The important method of universal kriging is explained intuitively. The genuine statistical problems, which have yet to be overcome, are pointed out.Research supported by the Office of Naval Research under contract NONR 4010(09) awarded to the Department of Statistics, The Johns Hopkins University. This paper in whole or in part may be reproduced for any purpose of the United States Government.     

Geostatistical analysis of field hydraulic conductivity in compacted clay

Hydraulic conductivity(K) of fractured or porous materials is associated intimately with water flow and chemical transport. Basic concepts imply uniform flux through a homogeneous cross-sectional area. If flow were to occur only through part of the area, actual rates could be considerably different. Because laboratory values ofK in compacted clays seldom agree with field estimates, questions arise as to what the true values ofK are and how they should be estimated. Hydraulic conductivity values were measured on a 10×25 m elevated bridge-like platform. A constant water level was maintained for 1 yr over a 0.3-m thick layer of compacted clay, and inflow and outflow rates were monitored using 10×25 grids of 0.3-m diameter infiltration rings and outflow drains subtending approximately 1×1 m blocks of compacted clay. Variography of inflow and outflow data established relationships between cores and blocks of clay, respectively. Because distributions of outflow rates were much less and bore little resemblance to the distributions of break-through rates based on tracer studies, presence of macropores and preferential flow through the macropores was suspected. Subsequently, probability kriging was applied to reevaluate distribution of flux rates and possible location of macropores. Sites exceeding a threshold outflow of 100×10^–9 m/s were classified as outliers and were assumed to probably contain a significant population of macropores. Different sampling schemes were examined. Variogram analysis of outflows with and without outliers suggested adequacy of sampling the site at 50 randomly chosen locations. Because of the potential contribution of macropores to pollutant transport and the practical necessity of extrapolating small plot values to larger areas, conditional simulations with and without outliers were carried out. Simulated scenarios based on all available data compared well with conditional simulations based on randomly chosen locations.This paper was presented at MGUS 87 Conference, Redwood City, California, 14 April 1987.