Cumulative semivariogram models of regionalized variables

The cumulative semivariogram approach is proposed for modeling regionalized variables in the geological sciences. This semivariogram is defined as the successive summation of half-squared differences which are ranked according to the ascending order of distances extracted from all possible pairs of sample locations within a region. This procedure is useful especially when sampling points are irregularly distributed within the study area. Cumulative semivariograms possess all of the objective properties of classical semivariograms. Classical semivariogram models are evaluated on the basis of the cumulative semivariogram methodology. Model parameter estimation procedures are simplified with the use of arithmetic, semilogarithmic, or double-logarithmic papers. Plots of cumulative semivariogram values vs. corresponding distances may scatter along a straight line on one of these papers, which facilitates model identification as well as parameter estimation. Straight lines are fitted to the cumulative semivariogram scatter diagram by classical linear regression analysis. Finally, applications of the methodology are presented for some groundwater data recorded in the sedimentary basins of the Kingdom of Saudi Arabia.     

Comparing some approximate methods for building local confidence intervals for predicting regionalized variables

Approximate local confidence intervals can be produced by nonlinear methods designed to estimate indicator variables. The most precise of these methods, the conditional expectation, can only be used in practice in the multi-Gaussian context. Theoretically, less efficient methods have to be used in more general cases. The methods considered here are indicator kriging, probability kriging (indicator-rank co-kriging), and disjunctive kriging (indicator co-kriging). The properties of these estimators are studied in this paper in the multi-Gaussian context, for this allows a more detailed study than under more general models. Conditional distribution approximation is first studied. Exact results are given for mean squared errors and conditional bias. Then conditional quantile estimators are compared empirically. Finally, confidence intervals are compared from the points of view of bias and precision.     

Minimum-variance unbiased quadratic estimation of covariances of regionalized variables

The parameters of covariance functions (or variograms) of regionalized variables must be determined before linear unbiased estimation can be applied. This work examines the problem of minimum-variance unbiased quadratic estimation of the parameters of ordinary or generalized covariance functions of regionalized variables. Attention is limited to covariance functions that are linear in the parameters and the normality assumption is invoked when fourth moments of the data need to be calculated. The main contributions of this work are (1) it shows when and in what sense minimum-variance unbiased quadratic estimation can be achieved, and (2) it yields a well-founded, practicable, and easy-to-automate methodology for the estimation of parameters of covariance functions. Results of simulation studies are very encouraging.     

Conditional LAS stochastic simulation of regionalized variables in random fields

Ore reserves forecasts are required to aid in investment decisions, mine design and valuation, short and long term production plans and proper and efficient mill design. In random multivariable fields with limited data and high levels of uncertainty, the kriged block estimates produce a smoothing effect resulting in underestimating high values and overestimating low values. The modified conditional simulation (MCS) methodology solves these problems by simulating the random field to preserve its mean and the variance structure. The simulation model is conditioned to reproduce the data at known sample points to minimize the variability between the simulated data and the true field data. In this study, the authors develop the MCS methodology to simulate ore reserve grades using the best linear unbiased estimation (BLUE) and the local average subdivision (LAS) techniques. The MCS methodology is applied to simulate block grades in a section of the Sabi Gold Project in Zimbabwe. The results are compared with the kriged estimates for this section. Analysis of the results shows that the MCS methodology reproduces the known sample grades with minimum estimation error of 0.001 while the estimation error associated with the kriged estimates is 1.104, a 100% efficiency of the MCS method over the kriging technique.     

Multiobjective optimization-based design of stabilizing piles in earth slopes

Though the technology of using stabilizing piles to prevent landsliding is not new, the design of such piles with a meaningful optimization framework has been rarely reported. In this paper, a multiobjective optimization-based framework for design of stabilizing piles is presented, in which both reinforcement effectiveness and cost efficiency could be explicitly considered. The design parameters considered in the proposed design framework are the pile parameters, including pile diameter, spacing, length, and position, and the design objectives considered are the reinforcement effectiveness and cost efficiency. The design of stabilizing piles is then implemented as a multiobjective optimization problem. In that the desire to maximize the reinforcement effectiveness and that to maximize the cost efficiency are two conflicting objectives, the output of this multiobjective optimization will be a Pareto front that depicts a trade-off between these two design objectives. With the obtained Pareto front, an informed decision regarding the design of stabilizing piles is reached. The effectiveness and significance of the proposed multiobjective optimization-based design framework for stabilizing piles are demonstrated through two illustrative examples: one is the design of stabilizing piles in a one-layer earth slope and the other the design of stabilizing piles in a two-layer earth slope. Further, parametric analyses are conducted to investigate the influences of the pile design parameters on the stability of reinforced slopes.     

地下水污染监测网多目标优化设计模型及进化求解

采用模拟-优化方法建立了一个用于地下水污染监测网设计的多目标优化模型,该模型包括最小化监测费用、污染物质量评估误差、污染羽一阶矩评估误差和二阶矩评估误差等4个目标函数,以充分揭示减少地下水污染监测费用与提高污染监测精度之间的权衡关系.将改进小生境Pareto遗传算法与地下水流模拟程序和污染物运移模拟程序相耦合用于求解地下水污染监测网多目标设计模型.算例研究表明,采用进化算法求解监测网的多目标模型,能真实地反映各个目标函数间的权衡关系,并且不用考虑传统方法中惩罚因子的影响.与单目标优化模型相比,多目标优化模型可在较短的时间内得到优化问题的一系列Pareto权衡解,以利于相应条件下决策者选择最为经济有效的地下水污染监测方案.     

Approximate up-scaling of geo-spatial variables applied to deep foundation design

We present a series of simple approximate methods for up-scaling the cumulative distribution function of spatially correlated variables by using an effective number n _e of independent variables. Methods are based on the property of distribution permanence of the gamma and inverse Gaussian distributions under averaging, bootstrap sampling and expansions about the normal and gamma distributions. A stochastic simulation study is used to validate each method, and simple parameters are defined to identify respective ranges of applicability. A practical example is presented where core sample rock strength data are up-scaled to shaft size for probabilistic (risk-based) deep foundation design. Supplemental material is available online.     

综合利用水库群多目标优化调度

研究了五个水库系统的多目标优化调度问题.根据系统中各水库及用户特点,建立了一个多目标分层序列优化的数学模型.第一层目标是对两个城市、三个灌区的供水优化;第二层目标是对水电站A₁的发电优化.计算表明,这个模型能获得较好的多目标效益.     

孔位离散游移式单纯形地下水位观测网优化设计方法

本文介绍一种新的地下水位观测多优化设计方法－－孔位离散游移式单纯形优化设计法。该方法不仅优化速度快，自动化程度高，而且衡量观测网质量的标准全面客观，是一种适合广泛使用的观测网优化设计法。文章还对与观测网优化设计有关的最优观测网使用时限及观测精度问题进行了讨论。     

槽波地震法特殊观测系统设计的探讨

系统地总结了该观测系统设计的原则、类型、方法及其独特的应用。

     

地下水观测井网优化设计

本文比较系统地论述克立金方法的基本思想,结合数学规划方法,推导出克立金方程组,并以现有的神头电厂贮灰场地下水观测井网的质量评价为基础,考虑观测井网优化设计的目的和经费投入量,拟定备选方案,分别计算出估计误差方差分布,经比较分析,选取一个最佳方案.分析结果表明,应用克立金方法可以解决地下水观测井网的优化问题.     

Spatial distribution of regionalized variables on reservoirs and groundwater resources based on geostatistical analysis using GIS: case of Rmel-Oulad Ogbane aquifers (Larache,NW Morocco)

In many countries of the world, groundwater is the main source of water in arid and semiarid regions. The scarcity of water is one of the main issues in Morocco. The coastal aquifer system of Rmel-Oulad Ogbane is recognized as one of the most important aquifers in Morocco and is very well known for their role in industrial, economic, and social development. However, this role is confronted to climate change impacts and heavy abstraction rates leading to a major decline in the groundwater levels and may eventually cause a deficit water balance of the aquifer as well as a degradation of the freshwater quality by seawater intrusion. The objective of this research is to identify and evaluate the distribution and spatial changes of regionalized variables on reservoirs and groundwater resources using geostatistical analysis in Geographic Information System (GIS) software. The prediction of these variables was performed using an interpolation method: ordinary kriging in a GIS. The normality test and trend analysis were applied to each variable to select the appropriate semivariogram model (SVM) and check the results using cross-validation (CV). Hence, several kriged maps of reservoirs and water resources have been produced to be exploited by the decision maker. The studied variables related to reservoirs and hydrodynamic data have a strong spatial dependence, which show correlations in specific direction, while the hydrochemical data are mainly related to groundwater mechanisms, such as advective-diffusive transport, without any autocorrelation between data.     

“十五”福建省地壳形变数字化监测网简介

该文主要对福建省地形变监测数字化监测网的特色 ,监测网的内容 ,布网目的 ,主要技术指标以及数据处理中心建设作了简介 ,最后对地壳形变数字化监测网在“数字福建”工程中的作用作了阐述     

A stochastic optimization approach for paper recycling reverse logistics network design under uncertainty

One of the most important objectives of a manufacturing firm is the efficient design and operation of its supply chain to maximize profit. Paper is an example of a valuable material that can be recycled and recovered. Uncertainty is one of the characteristics of the real world. The methods that cope with uncertainty help researchers get realistic results. In this study, a two-stage stochastic programing model is proposed to determine a long term strategy including optimal facility locations and optimal flow amounts for large scale reverse supply chain network design problem under uncertainty. This network design problem includes optimal recycling and collection center locations and optimal flow amounts between the nodes in the multi-facility environment. Proposed model is suitable for recycling/ manufacturing type of systems in reverse supply chain. All deterministic, stochastic models are mixed-integer programing models and are solved by commercial software GAMS 21.6/CPLEX 9.0.     

Geodetic network design for crustal deformation studies in the Caldera of Teide area

We present the previous work done in order to establish a geodetic control network at the Caldera of Teide (Canary Islands) for the purpose of detecting crustal movements in volcanic areas, as well as a procedure for solving the configuration problem, taking into account the features of the terrain.     

Nonlinear regression for dependent variables

Nonlinear regression methods can be used to fit functions for two related variables where both variables are subject to error. A computer program for nonlinear estimation described previously has been modified to fit such functions for a given set of data. A numerical example is provided for a second-degree equation in xand y.A closer fit to an observed set of data is possible if the error structure for the variables is specified.     

The influence of design and operating variables on the capacities of hydrocyclone classifiers

An extensive test programme has been conducted on 10.2-, 25.4- and 38.1-cm “Krebs” hydrocyclones in order to study the effects of some design and operating variables on their volume capacities.For a given pressure the capacity of a cyclone was found to rise sharply with the addition of solid particles to water until the solids content was about 15% by weight. Further increase in solids caused a decrease in throughput. The effect of vortex finder diameter on the throughput was found to be non-linear. A general equation which relates inlet and outlet diameters, operating pressure and feed solids content to the volume capacity is presented. This equation is independent of hydrocyclone diameter.     

神经网络在岩土工程观测数据优化中的应用

利用人工神经网络独特的结构和处理信息的方法，以实际工程为背景，编制了岩土工程观测数据优化程度，结合德兴铜矿2号尾矿坝B-B测线浸润线观测实例进行分析研究，使得优化的结果与实际相吻合。