An averaging method to study the motion of lunar artificial satellites I: Disturbing Function

We describe a semi-analytical averaging method aimed at the computation of the motion of an artificial satellite of the Moon. In this paper, the first of the two part study, we expand the disturbing function with respect to the small parameters. In particular, a semi-analytic theory of the motion of the Moon around the Earth and the libration of the lunar equatorial plane using different reference frames are introduced. The second part of this article shows that the choice of the canonical Poincaré variables lead to equations in closed form without singularities in e = 0 or I = 0. We introduce new expressions that are sufficiently compact to be used for the study of any artificial satellite. This revised version was published online in July 2006 with corrections to the Cover Date.     

Point Cumulative Semivariogram for Identification of Heterogeneities in Regional Seismicity of Turkey

Geological events are neither isotropic nor homogeneous in their occurrences. These two properties present difficulties for spatial modeling of regionalized variables. This paper presents a point cumulative semivariogram (PCSV) technique for quantifying the heterogeneity characteristics of the phenomenon concerned. The basis of the methodology is to obtain experimental PCSVs for each measurement point which led to estimation of radius of influence around each site. In addition, the experimental PCSVs provide basic information about the heterogeneity of the geological variable in the region, and furthermore many useful interpretations can be made concerning the regional variability of the variable. It provides the measure of cumulative similarity of a regional variable around any measurement site. Because PCSV is a means of measuring total similarity, maps at fixed similarity levels are provided in order to document the regional heterogeneity. Identification of heterogeneities depends on the comparison of fixed PCSV values at a multitude of irregularly scattered sites. The PCSV methodology has been applied to the regional seismic data of Turkey.     

Spatial Breakdown Point of Variogram Estimators

In the context of robust statistics, the breakdown point of an estimator is an important feature of reliability. It measures the highest fraction of contamination in the data that an estimator can support before being destroyed. In geostatistics, variogram estimators are based on measurements taken at various spatial locations. The classical notion of breakdown point needs to be extended to a spatial one, depending on the construction of most unfavorable configurations of perturbation. Explicit upper and lower bounds are available for the spatial breakdown point in the regular unidimensional case. The difficulties arising in the multidimensional case are presented on an easy example in IR² , as well as some simulations on irregular grids. In order to study the global effects of perturbations on variogram estimators, further simulations are carried out on data located on a regular or irregular bidimensional grid. Results show that if variogram estimation is performed with a 50% classical breakdown point scale estimator, the number of initial data likely to be contaminated before destruction of the estimator is roughly 30% on average. Theoretical results confirm the previous statement on data in IR^d , d 1.     

Clustering stochastic point process model for flood risk analysis

Since the introduction into flood risk analysis, the partial duration series method has gained increasing acceptance as an appealing alternative to the annual maximum series method. However, when the base flow is low, there is clustering in the flood peak or flow volume point process. In this case, the general stochastic point process model is not suitable to risk analysis. Therefore, two types of models for flood risk analysis are derived on the basis of clustering stochastic point process theory in this paper. The most remarkable characteristic of these models is that the flood risk is considered directly within the time domain. The acceptability of different models are also discussed with the combination of the flood peak counted process in twenty years at Yichang station on the Yangtze river. The result shows that the two kinds of models are suitable ones for flood risk analysis, which are more flexible compared with the traditional flood risk models derived on the basis of annual maximum series method or the general stochastic point process theory. Received: September 29, 1997     

GPS技术在金川露天矿边坡变形监测中的应用

本文结合金川露天矿边坡变形GPS监测研究,主要论述监测基点的选择,基点WGS-84坐标的确定,变形监测网的布设,GPS数据采集、处理和质量,坐标转换的实施等,给出反演推出的若干四等三角点及其它控制点的变形数据,这些数据和造成变形的物理成因符合得很好,最后概括出GPS技术应用于变形观测的若干优点。     

Pelagic tar, dissolved/dispersed petroleum hydrocarbons and plastic distribution in the Cretan Sea, Greece

During the first cruise of R/V ‘Philia’ in July 1997 within the framework of the TALOS programme supported by the Greek Ministry of Physical Planning and Public Works, the sampling of floating tar, litter and sea water for dissolved/dispersed petroleum hydrocarbons (DDPH) was carried out in the Cretan Sea. Analysis of these data has allowed a first assessment of the status of floating marine pollution in the region. DDPH measurements showed a mean concentration of 0.145 μg/l of chrysene equivalents (n = 24). Tar and plastics concentrations were in the range of 1–4280 and 0–1160 μg/m², respectively. Mean pelagic tar concentration was 318 μg/m², more than two times higher than what was reported for the area in previous studies. Based on in situ hydrographic observations there is strong evidence that most of the floating tar enters the Cretan Sea through the Ionian Sea.     

Cross-Correlation Detection of Point Sources in the WMAP First Year Data

We apply a Cross-Correlation (CC) method developed previously for detecting gamma-ray point sources to the WMAP first year data by using the Point-Spread Function of WMAP and obtain a full sky CC coefficient map. We find that the CC method is a pow- erful tool to examine the WMAP foreground residuals which can be further cleaned accord- ingly. Evident foreground signals are found in the WMAP foreground cleaned maps and the Tegmark cleaned map. In this process 101 point sources are detected, and 26 of them are new sources additional to the originally listed WMAP 208 sources. We estimate the ?ux of these new sources and verify them by another method. As a result, a revised mask file based on the WMAP first year data is produced by including these new sources.     

Streamflow estimation using optimal regional dependency function

The determination of spatial dependency of regionalized variable (ReV) is important in engineering studies. Regional dependency function that leads to calculation of weighting coefficients is required in order to make regional or point‐wise estimations. After obtaining this dependency function, it is possible to complete missing records in the time series and locate new measurement station. Also determination of regional dependency function is also useful to understand the regional variation of ReV. Point Cumulative Semi‐Variogram (PCSV) is another methodology to understand the regional dependency of ReV related to the magnitude and the location. However, this methodology is not useful to determine the weighting coefficient, which is required to make regional and point‐wise estimations. However, in Point Semi‐Variogram (PSV) proposed here, weighting coefficient depends on both magnitude and location. Although the regional dependency function has a fluctuating structure in PSV approach, this function gradually increases with distance in PCSV. The study area is selected in Mississippi river basin with 38 streamflow stations used for PCSV application before. It is aimed to compare two different geostatistical models for the same data set. PSV method has an ability to determine the value of variable along with optimum number of neighbour stations and influence radius. PSV and slope PSV approaches are compared with the PCSV. It was shown that slope slope point semi‐variogram (SPSV) approaches had relative error below 5%, and PSV and PCSV methods revealed relative errors below 10%. Copyright © 2009 John Wiley & Sons, Ltd.     

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建筑物立面分类与DIM点云滤波

针对目前基于近景摄影测量方法构建建筑物立面模型过程中因密集影像匹配（DIM）点云噪声所引起的建筑物立面TIN网格模型畸变问题,本文借鉴机器学习中样本学习的思想,对建筑物立面进行了分类并对DIM点云提出了相应的滤波方法,以达到去除DIM点云噪声和改善其TIN网格模型畸变的目的。其中,针对平面结构立面,采取先对点云样本进行学习计算构建数学立面模型所需参数,再对该立面模型设定阈值并对其点云进行滤波处理的方法;针对曲面结构立面,则结合DIM点云特性先将点云样本分类标记归为立面点与非立面点,再进行样本特征值学习,使用Logistic回归算法迭代计算求解最佳回归系数,从而构建滤波分类器的方法对立面点云进行滤波处理。试验结果表明,本文滤波处理方法能将立面DIM点云噪声有效识别并去除,而且使用该方法处理后所得点云构建的建筑物立面TIN网格模型精细化程度得到有效提高,模型质量得到明显改善。