均布荷载下钢纤维混凝土叠合梁抗剪性能试验研究

本文通过１１根叠合梁的试验，分析了均布荷载作用下钢纤维混凝土叠合梁的抗剪性能，提出了无腹筋和配箍筋钢纤维混凝土叠合梁斜截面受剪承载力的计算公式。研究结果表明，钢纤维的掺入改善了叠合梁的抗剪性能，提高了斜截面受剪承载力     

Estimating Net Radiation from Air Temperature for Diffusion Modelling Applications in a Tropical Area

A simple and straightforward expression isproposed, which is based on the Fourier seriesdecomposition technique, for estimating hourly averagednet radiation from the corresponding values of airtemperature at a tropical location in West Africa. Thecomparisons made of the estimated net radiation withactual measurements (case studies) clearlydemonstrate that the scheme is efficient, both inclear and under cloudy weather conditions. Theestimates are also shown to be better than thosedetermined from an alternative relationship usingparametrizations for the shortwave and longwave components at the surface. The presentprocedure can easily be implemented in existingmeteorological preprocessors to calculate the sensibleheat flux for the Gaussian-based air pollutionmodels.     

干涉SAR的二维相位展开算法研究

该文讨论了干涉合成孔径雷达的二维相位展开算法。先简要给出了ＩＮＳＡＲ相位差图误差的统计特性，并说明了该误差可以近似为高斯型噪声。然后介绍了利用ＦＦＴ的最小二乘和二维相位展开算法，实验表明ＬＳ－ＦＦＴ算法对高斯白噪声具有很好的抗干扰性。     

The relation between intensity and magnitude for Italian earthquakes

This paper describes the problems concerning the relation between intensity and magnitude, which are substantially different quantities by nature. In consideration of the necessity to translate magnitude values into intensity values, andvice versa, to find magnitude values for historical earthquakes, we have searched for a correspondence that may exist between intensity and magnitude, hypothesising that magnitude values were distributed as a known function. Therefore, we have analysed two distribution functions, first the Gaussian distribution, then a box function, of which the goodness of fit has been estimated by the ² test. In conclusion, the probability distribution of magnitude vs intensity seems better described by a Gaussian curve.     

A non-parametric bivariate entropy estimator for spatial processes

In this paper, entropy is presented as an alternative measure to characterize the bivariate distribution of a stationary spatial process. This non-parametric estimator attempts to quantify the concept of spatial ordering, and it provides a measure of how Gaussian the experimental bivariate distribution is. The concept of entropy is explained and the classical definition presented, along with some important results. In particular, the reader is reminded that, for a known mean and covariance, the bivariate Gaussian distribution maximizes entropy. A relative entropy estimator is introduced in order to measure departure of an experimental bivariate distribution from the bivariate Gaussian. Two case studies are presented as examples.     

Multivariate Spatial Modeling for Geostatistical Data Using Convolved Covariance Functions

Soil pollution data collection typically studies multivariate measurements at sampling locations, e.g., lead, zinc, copper or cadmium levels. With increased collection of such multivariate geostatistical spatial data, there arises the need for flexible explanatory stochastic models. Here, we propose a general constructive approach for building suitable models based upon convolution of covariance functions. We begin with a general theorem which asserts that, under weak conditions, cross convolution of covariance functions provides a valid cross covariance function. We also obtain a result on dependence induced by such convolution. Since, in general, convolution does not provide closed-form integration, we discuss efficient computation. We then suggest introducing such specification through a Gaussian process to model multivariate spatial random effects within a hierarchical model. We note that modeling spatial random effects in this way is parsimonious relative to say, the linear model of coregionalization. Through a limited simulation, we informally demonstrate that performance for these two specifications appears to be indistinguishable, encouraging the parsimonious choice. Finally, we use the convolved covariance model to analyze a trivariate pollution dataset from California.     

Using robust kriging and sequential Gaussian simulation to delineate the copper- and lead-contaminated areas of a rapidly industrialized city in Yangtze River Delta,China

A total of 540 topsoil samples (0–15 cm), 188 subsoil samples (20–40 cm), and four individual soil profiles were collected in this study for mapping the Cu- and Pb-contaminated areas in soils of Zhangjiagang city, an industrialized city in the Yangtze River Delta region of China. Robust geostatistical methods were applied for identifying possible spatial outliers of Cu and Pb data, and then a sequential Gaussian simulation was employed for delineating the potential areas where Cu or Pb concentration was affected by diffuse pollution. The results showed that the spatial outliers of Cu and Pb were strongly associated with various types of factories. The anthropogenic input of Cu to soils at local hotspots was closely related to emissions of printing and dyeing, metallurgical, and chemical factories, whereas a lead oxide factory and a chemical factory resulted in a considerable increase of Pb in the topsoil of the study area. Approximately 30% of the total land area of the study was at potential risk from the Cu or Pb diffuse pollution resulting from rapid industrialization of the area over the past 20 years.     

小波束叠前深度偏移和照明:基于Marmousi模型的测试

小波束源有很强的局部性和方向性,它可以很容易的实现局部照明和偏移,除此之外,它的偏移效果也要好于常规的偏移方法。本文介绍了基于框架理论的小波束叠前深度偏移的基本原理。我们解释了基于Gaussian函数的G-D框架。小波束分解提供了局部空间和方向上的信息。我们在小波域中,分别应用矩形窗和Gaussian窗合成了小波束源和小波束记录,并基于傅立叶有限差分算子,对marmousi模型进行了叠前深度偏移试算。通过对单个,以及多个小波束源偏移结果对比分析,验证了Gaussian小波束叠前深度偏移方法的有效性。     

ESTIMATIONS OF THE SHAPE AND VOLUME OF A STONY DEBRIS FLOW FAN

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基于高斯过程回归的SRTM数据空洞填补方法

针对现有插值方法对复杂地表模拟能力不足的问题,发展了一种基于非平稳(神经网络)核的高斯过程回归(GPR)方法,并将其应用于SRTM DEM空缺数据填补。以山区SRTM DEM的模拟数据空洞为研究对象,将GPR模拟结果与传统插值方法(TIN、SPLINE和IDW)比较表明:GPR填补精度高于传统插值方法,且空缺区域模拟曲面较好的保持了地形特征。