克里金参数估值法及其在参数估计分析中的应用

为考虑岩土介质参数的空间分布的结构性和随机性等不确定因素,引入了克里金参数估值法。采用变异函数描述参数在空间结构上的变化,建立其空间变异规律的数学模型,从而实现对岩土参数的估值。通过实例分析揭示了克里金估值法具有反映“过滤效应”和“集团效应”的优点,对不同位置的数据赋予不同的权重系数,能够有效地反映参数空间变异结构,有利于对参数的合理化分析     

移动轨迹的尺度特征

移动轨迹是移动对象在地理空间环境中移动而留下的痕迹。移动轨迹数据是一类特殊的地理时空数据,它具有时间尺度、空间尺度和语义尺度特征。本文首先分析了轨迹数据的空间尺度特征与时间尺度特征,建立了轨迹空间尺度与时间尺度转换关系式;然后论述了轨迹的语义内涵和语义尺度特征,将轨迹语义分为移动对象语义、地理空间环境语义、采集设备语义、移动方式语义,并从地理空间环境语义的角度分析了轨迹的语义多尺度;最后探讨了轨迹语义尺度与时空尺度之间的一致性关系。     

A Case Study on the Effects of Heterogeneous Soil Moisture on Mesoscale Boundary-Layer Structure in the Southern Great Plains, U.S.A. Part II: Mesoscale Modelling

The importance of soil moisture inputs and improved model physics in the prediction of the daytime boundary-layer structure during the Southern Great Plains Hydrology Experiment 1997 (SGP97) is investigated using the non-hydrostatic fifth-generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model MM5. This is Part II of a two-part study examining the relationship of surface heterogeneity to observed boundary-layer structure. Part I focuses on observations and utilizes a simple model while Part II uses observations and MM5 modelling. Soil moisture inputs tested include a lookup table based on soil type and season, output from an offline land-surface model (LSM) forced by atmospheric observations, and high-resolution ( 800 m) airborne microwave remotely sensed data. Model physics improvements are investigated by comparing an LSM directly coupled with the MM5 to a simpler force-restore method at the surface. The scale of land surface heterogeneities is compared to the scale of their effects on boundary-layer structure.The use of more detailed soil moisture fields allowed the MM5 to better represent the large-scale (hundreds of km) and small-scale (tens of km) horizontal gradients in surface-layer weather and, to a lesser degree, the atmospheric boundary-layer (ABL) height, which was evaluated against observations measured by differential absorption lidar (DIAL). The benefits of coupling an LSM to the MM5 were not readily evident in this summertime case, with the model having particular difficulty simulating the timing of maximum surface fluxes while underestimating the depth of the mixed layer.     

Fundamental causes and spatial heterogeneity of deforestation in Legal Amazon

This study explores the main direct and underlying causes of deforestation in Brazil's Legal Amazon region by considering spatial differences. The computation of localized parameters is based on geographically weighted regression (GWR). The novelty of this paper lies in its incorporation of economic, rather than Euclidean, distances into the GWR. Economic distances are measured by travel time, sourced from Google Inc. A global approach revealed several important factors that affect deforestation, including: rural population, GDP (suggesting a U-shaped environmental Kuznets curve), forest stock, cattle ranching, timber value, and road networks (both official and unofficial). Local analysis uncovered patterns not seen under global models, especially in the state of Pará. Most notably, crop cultivation was found to accelerate deforestation in southeastern Pará and northeastern Mato Grosso, while in some regions (especially in the northeastern corner of Pará), the area covered by crop plantations was negatively associated with deforestation. For Pará, rural credit constraints, larger territories designated as sustainable use areas and indigenous lands, and higher levels of precipitation inhibit deforestation. Further, rural population has a very heterogeneous impact on deforestation across Legal Amazon: it is not a significant factor of deforestation in northern Pará and Amapá, but it has a relatively strong effect in the western parts of Mato Grosso and Rondônia. Also, official and illegal roads create significantly more pressure on forests in remote regions compared to developed areas. Finally, the use of economic distances, as opposed to Euclidean distances, leads to notably different GWR results.     

语义Web本体语言及OWL研究

面对Internet应用模式的变化和Web海量信息的冲击,Web信息个性化、智能化的研究不断深入.语义Web规划对当前的万维网进行扩展,期望用加入信息中的语义内容导航信息资源检索并提高网络服务的智能化,使机器自动与人协同工作.目前,虽然语义Web还是个理想模型,但将语义Web融入现有Web结构进行信息智能开发的初步努力已经展开.特别是随着对本体技术研究的日渐深入,OWL本体理论探索和应用开发逐渐受到广泛重视.     

地震尺度下碳酸盐岩储层的岩石物理建模方法(英文)

碳酸盐岩油藏的强非均质性以及孔隙结构的复杂性,使得作为连接油藏参数与地震参数重要桥梁的岩石物理模型,以及作为油藏预测和定量表征最有效工具的流体替换成为岩石物理建模的难点与重点。在碳酸盐岩储层复杂孔隙结构与地震尺度下碳酸盐岩储层非均质性分析基础上,研究采用岩石网格化方法,将地震尺度下非均质碳酸盐岩储层岩石划分为具有独立岩石参数的均质岩石子体,根据岩石孔隙成因与结构特征采用不同岩石物理模型分步计算岩石子块干岩石弹性模量,并根据不同孔隙连通性进行流体替换,计算饱和不同流体岩石弹性模量。基于计算的岩石子块弹性模量,采用Hashin-Shtrikman-Walpole弹性边界计算理论方法实现地震尺度下碳酸盐岩储层弹性参数计算。通过对含有不同类型孔隙组合碳酸盐岩储层模型的弹性模量进行计算与分析,明确不同孔隙对岩石弹性参数的影响特征,模拟分析结果与实际资料认识一致。