面向稀疏散布数据集的时空Kriging优化

时空Kriging法通过将变异函数向时空域进行扩展得到时空变异函数,有效地利用时空邻近的采样点综合进行插值,由于时空稀疏散布数据集具有单一时刻下样本点数量少以及时空分布不规律的特点,难以满足使用时空Kriging插值法的基本条件,导致插值精度不高,据此本文提出了优化方法：通过多时段叠置拟合空间变异函数的方法,综合利用时空邻域内的采样点以解决单一时刻下空间邻域内数量不足情况;控制时间变异对空间变异函数拟合的误差影响;采用积合式模型构建时空变异函数进行插值。最后使用Argo海温数据进行插值实验,在相同条件下与时空Kriging法以及时空权重法的交叉验证结果对比得出,该方法在保证拟合所需采样点数量要求的同时,有效削减了一般时空Kriging法中时间变异对空间变异函数拟合结果的干扰,插值结果的绝对误差均值从0.5降低至0.2以内,稳定性进一步增强,改善了时空Kriging法在稀疏散布数据条件下精度上的不足。     

Geostatistical estimation of chemical contamination in stream sediments: The case study of Vale das Gatas mine (northern Portugal)

Based on an environmental geochemistry case study carried out in the neighbourhood of a W–Sn abandoned mine, the pollution in stream sediments was modelled through a Global Contamination Index. Such an index permits one to summarize the combination of deleterious elements in a single variable, obtained by the projection of samples onto the first axis of a PCASD (Principal Components Analysis of Standardized Data) applied to the entire n × p matrix containing the available concentrations of p = 16 elements in the set of n = 220 collected samples.In order to provide a sound basis for a coherent planning of the remediation process which will be put in operation in the affected area, it is necessary to balance the costs of reclaiming with the probabilities of exceeding the upper limits accepted for concentrations of environmentally harmful elements in sediments. Given these limits, they are back-transformed in the index values, providing a practical threshold between ‘clean’ and ‘contaminated’ samples. On the other hand, the minimum dimension of the cell to be reclaimed is restrained by the selected remediation process to be applied in the affected area. Hence, to meet the constraints of such a remediation process, it is required to estimate the probabilities of exceeding the index threshold in technologically meaningful sub-areas. For this end, the Indicator Block Kriging technique was applied, producing a series of maps where sub-areas to be reclaimed can be spotted for different probability levels. These maps, on which the decision making remediation agency can rely for its cost-benefit analysis, take into account both the spatial structure of ‘clean’ vs. ‘contaminated’ samples and the constraints of the reclaiming process.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

Triple diagram method for the prediction of wave height and period

Many formulations have been developed so far to predict the wave height and period from fetch length and wind blowing duration for a constant wind speed. This study aimed to predict wave parameters from fetch length and meteorological factors by using triple diagram methodology based on Kriging principles. Proposed model results were compared with Joint North Sea Wave Project (JONSWAP) model which is used so commonly in the ocean and coastal engineering studies. For the implementation of the methodology hourly wave and wind data were obtained from a buoy located in Lake Ontario. Numerical and graphical comparisons demonstrated that the proposed method outperforms the classical formulation.     

实验变差函数计算方法的研究与运用

本文系统、详尽地分析讨论了不同样品数据条件下实验变差函数的计算方法。在实验变差函数计算流程的设计和有效样品搜索域的确定方面提出了自己独特的见解。最后用一个矿山的控矿工程教据对文中介绍的方法进行了测试。结果表明,利用本文的方法,容易得到稳健、准确、信息量高的实验变差函数。     

线性地质统计学在地质工作计算机化中的作用及意义

线性地质统计学的发展与地质工作逐渐由定性向定量、由人工劳动逐渐被计算机取代的过程密切相关的。对样品品位赋予一定的权值进行滑动加权平均，来估计中心块段平均品位的方法，使地质问题的计算能在计算机上实现开辟了新的途径。     

应用Kriging方法研究格尔木河流域地下水位动态观测网的优化配置

地下水动态是水文地质研究的一个重要方面，如何获满足一定精度要求全面而合理的地下水位动态资料具有现实经济效益。本文研究格尔木河流域地下水位动态测网，通过选取理想的变差函，运有Ｋｒｉｇｉｎｇ方法进行地下水位的线性无偏最优估计和计算估计误差的标准差，结合给定的允许误差限评判地下水位动态观测网的配置是否合适，并提出调整现有观测网的方案     

正态化克立格法在路腊汞矿储量计算中的应用

正态化克立格法是对传统储量计算方法的改革和创新。其计算过程的科学性和计算结果的正确性在路腊汞矿作了探采对比试算，取得了良好的结果。     

Space-time mapping of soil salinity using probabilistic bayesian maximum entropy

The mapping of saline soils is the first task before any reclamation effort. Reclamation is based on the knowledge of soil salinity in space and how it evolves with time. Soil salinity is traditionally determined by soil sampling and laboratory analysis. Recently, it became possible to complement these hard data with soft secondary data made available using field sensors like electrode probes. In this study, we had two data sets. The first includes measurements of field salinity (EC_a) at 413 locations and 19 time instants. The second, which is a subset of the first (13 to 20 locations), contains, in addition to EC_a, salinity determined in the laboratory (EC_2.5). Based on a procedure of cross-validation, we compared the prediction performance in the space-time domain of 3 methods: kriging using either only hard data (HK) or hard and mid interval soft data (HMIK), and Bayesian maximum entropy (BME) using probabilistic soft data. We found that BME was less biased, more accurate and giving estimates, which were better correlated with the observed values than the two kriging techniques. In addition, BME allowed one to delineate with better detail saline from non-saline areas.     

应用空间统计学理论解译遥感影像信息“缺失”区

借助图像处理软件ERDAS、地理信息系统软件ArcInfo以及空间统计分析软件ILWIS ,在对TM遥感影像进行分类的基础上 ,运用空间统计学理论以及Kriging插值技术 ,内插了影像真实信息“缺失”斑块的信息 ,插值结果通过了精度检验。为解译影像信息“缺失”区 ,提供了一种手段和方法。