数据同化在海洋生态模型中的应用和研究进展

将数据同化方法引入海洋生态系统动力学模型研究,利用现有的观测数据,获得最佳的模式参数、初始场或提高状态模拟,是当前多学科交叉研究的热门领域。本文依据国内外研究进展,主要就海洋生态模型研究中所采用的变分伴随、卡尔曼滤波、模拟退火法方法进行了介绍,总结了数据同化在我国的海洋生态系统研究中的现状和发展趋势。     

Stress‐dependent hardening and failure surfaces of dry sand

During several triaxial compression experiments on plastic hardening, softening, and failure properties of dense sand specimens, it was found on various stress paths that the size of the failure surface was not constant. Instead, it changed depending on the current state of hydrostatic pressure. This finding is in contrast to the standard opinion consisting of the fact that the failure surface remains constant, once it has been reached during an experiment or in situ. In general, the behaviour of cohesionless granular‐material‐like sand is somehow characterised in between fluid and solid, where the solid behaviour results from the angle of internal friction and the confining pressure. Although the friction angle is an intrinsic material property, the confining pressure varies with the boundary conditions, thus defining different solid properties like plastic hardening, softening, and also failure. Based on our findings, it was the goal of the present contribution to introduce an improved setting for the plastic strain hardening and softening behaviour including the newly found yield properties at the limit state. For the identification of the material parameters, a complete triaxial experimental analysis of the tested sand is given. The overall elasto‐plasticity concept is validated by numerical computations of several laboratory foundation‐ and slope‐failure experiments. The performance of the proposed approach is compared with the standard concept of a constant failure surface, where the corresponding yield surfaces are understood as contours of equivalent plastic work or plastic strain. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction by regression and intrarange data scatter in surface-process studies

Modeling is a major component of contemporary earth science, and regression analysis occupies a central position in the parameterization, calibration, and validation of geomorphic and hydrologic models. Although this methodology can be used in many ways, we are primarily concerned with the prediction of values for one variable from another variable. Examination of the literature reveals considerable inconsistency in the presentation of the results of regression analysis and the occurrence of patterns in the scatter of data points about the regression line. Both circumstances confound utilization and evaluation of the models. Statisticians are well aware of various problems associated with the use of regression analysis and offer improved practices; often, however, their guidelines are not followed. After a review of the aforementioned circumstances and until standard criteria for model evaluation become established, we recommend, as a minimum, inclusion of scatter diagrams, the standard error of the estimate, and sample size in reporting the results of regression analyses for most surface-process studies.     

矿山地理信息系统中的空间和时间四维数据模型

现有的许多地理信息系统（GISs）一般都没有考虑时间这一维数据。本文依据矿山的特点和需求，提出了能满足空间和时间要求的GIS的概念。针对传统数据模型所存在的缺点，以及根据处理历史数据所具有的特点，提出了一种用于矿业开发的空间和时间综合四维数据模型。这种模型通过采用四维空间线性编码技术，可大大减少数据存贮所需的辅助操作。为了适应综合四维数据处理的需要，笔者研究出了一种新的拓扑结构——四维空间和时间因素的拓扑关系。     

微气象自动观测网

本文介绍利用计算机和通讯技术,建立局地网络的微气象观测系统,实现气象数据的自动采集、无线传输和集中实时处理。整个系统由一个中心站和五个(或更多)观测子站组成,是一个点到点的通讯网络。该系统为研究不同下垫面地气相互作用的观测实验提供了手段和方便。整个系统分两部份介绍,本文作一总体概述,第二部份说明数据采集和无线传输的具体实施。     

自记水位仪的数字化改造

本文详细叙述了采用光电编码和微机技术对自记水位仪进行数字化改造的方案选择、理论计算和改造实践。     

地下流体前兆模糊识别

在地震的孕育、发生到发展的过程中，可能会出现多项地下流体观测量不同程度的前兆异常变化，这些异常变化是复杂多样、模糊不清的。引入模糊识别法对一定区域和一定时间段的地下流体观测数据进行了分析和处理，表明模糊识别法可较好地识别和分辨出地下流体前兆的地震异常。     

实时波形数据断记的解决办法

应用Windows操作系统的网络技术和自行开发的报警软件，解决了牡丹江地震台连续波形数据断记问题。本文详细介绍了解决方案和报警软件的安装及使用方法。     

苏北地区大地电磁测深采集方法技术研究

根据苏北地区大地电磁勘探环境特点进行了电极距长度、磁场位置和采集时长的试验工作，确定了大地电磁测深勘探技术参数设置下限，电极距长度不能小于50m，采集时间要大于8h，磁距应控制在2km范围内。     

Assessing spatial and attribute errors in large national datasets for population distribution models: a case study of Philadelphia county schools

Geospatial technologies and digital data have developed and disseminated rapidly in conjunction with increasing computing efficiency and Internet availability. The ability to store and transmit large datasets has encouraged the development of national infrastructure datasets in geospatial formats. National datasets are used by numerous agencies for analysis and modeling purposes because these datasets are standardized and considered to be of acceptable accuracy for national scale applications. At Oak Ridge National Laboratory a population model has been developed that incorporates national schools data as one of the model inputs. This paper evaluates spatial and attribute inaccuracies present within two national school datasets, Tele Atlas North America and National Center of Education Statistics (NCES). Schools are an important component of the population model, because they are spatially dense clusters of vulnerable populations. It is therefore essential to validate the quality of school input data. Schools were also chosen since a validated schools dataset was produced in geospatial format for Philadelphia County; thereby enabling a comparison between a local dataset and the national datasets. Analyses found the national datasets are not standardized and incomplete, containing 76 to 90 percent of existing schools. The temporal accuracy of updating annual enrollment values resulted in 89 percent inaccuracy for 2003. Spatial rectification was required for 87 percent of NCES points, of which 58 percent of the errors were attributed to the geocoding process. Lastly, it was found that by combining the two national datasets, the resultant dataset provided a more useful and accurate solution.