设计模式在地质统计学建模软件的应用

针对构建具有自主知识产权的地质统计学建模软件的需要,设计该软件的基本结构.通过定义对象管理树、被管理时象和管理对象等概念,将设计模式的方法引入到该软件的设计和实现中,并给出了工厂方法的具体实现.该研究可以提高建模软件的代码复用、可靠性和可扩展性.     

A Copula based observation network design approach

In this paper, a method for environmental observation network design using the framework of spatial modeling with copulas is proposed. The methodology is developed to enlarge or redesign an existing monitoring network by taking the configuration which would increase the expected gain defined in a utility function. The utility function takes the estimation uncertainty, critical threshold value and gain-loss of a certain decision into account. In this approach, the studied spatial variable is considered as a random field in where variations in time is neglected and the variable of interest is static in nature. The uniqueness of this approach lies in the fact that the uncertainty estimation at the unsampled location is based on the full conditional distribution calculated as conditional copula in this study. Unlike the traditional Kriging variance which is a function of mere measurements density and spatial configuration of data points, the conditional copula account for the influence from data values. This is important specially if we are interested in purpose oriented network design (pond) as for example the detection of noncompliance with water quality standards, the detection of higher quantiles in the marginal probability distributions at ungauged locations, the presence or absence of a geophysical variable as soil contaminants, hydrocarbons, golds and so on. An application of the methodology to the groundwater quality parameters in the South-West region of Germany shows its potential.     

INTAMAP: The design and implementation of an interoperable automated interpolation web service

INTAMAP is a Web Processing Service for the automatic spatial interpolation of measured point data. Requirements were (i) using open standards for spatial data such as developed in the context of the Open Geospatial Consortium (OGC), (ii) using a suitable environment for statistical modelling and computation, and (iii) producing an integrated, open source solution. The system couples an open-source Web Processing Service (developed by 52°North), accepting data in the form of standardised XML documents (conforming to the OGC Observations and Measurements standard) with a computing back-end realised in the R statistical environment. The probability distribution of interpolation errors is encoded with UncertML, a markup language designed to encode uncertain data. Automatic interpolation needs to be useful for a wide range of applications and the algorithms have been designed to cope with anisotropy, extreme values, and data with known error distributions. Besides a fully automatic mode, the system can be used with different levels of user control over the interpolation process.     

潮褐土土壤养分的空间分布特征

应用主成分分析方法和地统计学方法,对河北省栾城县一15hm2试区里潮褐土全氮、全磷、有机质、速效磷、速效钾、碱解氮、NH 3-N等土壤养分含量的空间变异规律进行了定量分析。为实现土壤养分的精准管理和4-N和NO-变量平衡施肥提供了科学依据。     

菜地土壤养分的空间变异特征

通过地统计学、空间分析方法及结合农户施肥调查,对菜地土壤养分的空间变异特征进行研究。结果表明,农户施肥水平差别较大,整体上施磷相对过多,施钾相对较少,土壤OM、NH4 -N、P、K、Ca、Mg和S的变异系数在28.4%～69.2%之间。土壤各养分的空间变异结构有较大区别,土壤OM及NH4 -N的变异具有强烈空间相关性,土壤P、K、Ca、Mg和S养分含量的变异为中等空间相关性。土壤养分的等值线图显示,OM在空间的分布与土壤质地渐变规律相关,NH4 -N呈现南北向的带状分布,P、K、Ca、Mg和S趋于小块状分布。总的来说,施肥等人为因素加剧了土壤矿质养分的空间变异。     

Nonstationary Gaussian Process Models Using Spatial Hierarchical Clustering from Finite Differences

Modern digital data production methods, such as computer simulation and remote sensing, have vastly increased the size and complexity of data collected over spatial domains. Analysis of these large spatial datasets for scientific inquiry is typically carried out using the Gaussian process. However, nonstationary behavior and computational requirements for large spatial datasets can prohibit efficient implementation of Gaussian process models. To perform computationally feasible inference for large spatial data, we consider partitioning a spatial region into disjoint sets using hierarchical clustering of observations and finite differences as a measure of dissimilarity. Intuitively, directions with large finite differences indicate directions of rapid increase or decrease and are, therefore, appropriate for partitioning the spatial region. Spatial contiguity of the resulting clusters is enforced by only clustering Voronoi neighbors. Following spatial clustering, we propose a nonstationary Gaussian process model across the clusters, which allows the computational burden of model fitting to be distributed across multiple cores and nodes. The methodology is primarily motivated and illustrated by an application to the validation of digital temperature data over the city of Houston as well as simulated datasets. Supplementary materials for this article are available online.     

Spatial interpolation methods applied in the environmental sciences: A review

Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided.     

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research,a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation.The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs.In this procedure,the Artificial Neural Network was trained,tested and validated using assay values obtained from exploratory drillholes.Next,the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively.Finally,the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model.The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades.The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement.It could also be an effective mineral reserve evaluation method that could produce optimum block model for mine design.     

Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey

Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river metric into the space/time analysis of dissolved oxygen data for two impaired river basins. An efficient algorithm is developed to calculate river distances within the BMElib statistical package for space/time geostatistics. We find that using a river distance in a space/time context leads to an appreciable 10% reduction in the overall estimation error, and results in maps of DO that are more realistic than those obtained using a Euclidean distance. As a result river distance is used in the subsequent non-attainment assessment of DO for two impaired river basins in New Jersey.     

地质统计学及其在某铜矿山储量计算中的应用

首先论述了地质统计学的基本理论及应用其计算地质储量的方法步骤；然后以计算机为工具，运用地质统计学对某铜矿山铜金属储量进行了分析计算，并将计算结果与真实储量进行了对比分析。通过对比分析，认为运用地质统计学计算储量具有先进性及实用性。