Outlier detection algorithms and their performance in GOCE gravity field processing

The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data.     

Development of a remotely sensed,historical land-cover change database for rural Chihuahua,Mexico

The purpose of this article is to describe the development of a remotely sensed, historical land-cover change database for the northwestern quarter of Chihuahua, Mexico, The database consists of multi-temporal land-cover classifications and change detection images. The database is developed to facilitate future investigations that examine urban–rural linkages as possible drivers of rural land-use and land-cover changes. To develop the needed land-cover change database, this study uses the North American Landsat Characterization (NALC) MSS triplicates because of their temporal depth and spatial breadth. Challenges exist, however, to effective classification and change detection using the NALC triplicates, including illumination differences across multiple scenes and periods caused by topographic and solar variations and the lack of ground reference data for historic periods. Therefore, creation of the database is a four step process. First, extensive pre-processing is performed to enhance comparability of multi-date images. Pre-processing includes topographic correction, mosaic creation and multi-date radiance normalization. Second, ancillary sources of land-cover data are combined with visual interpretations of enhanced images to define reference pixels used to classify the images using the maximum likelihood algorithm. Third, classification accuracy is assessed. Fourth, post-classification change detection is performed. Results indicate significant image improvements after pre-processing that permit very good overall classification (86.26% classified correctly) and change detection. To conclude, findings are presented that indicate significant changes to arid grasslands/shrublands and forest resources in mountainous regions.     

Particle size dependent response of aerosol counters

During an international workshop at the Institute for Experimental Physics of the University of Vienna, Austria, which was coordinated within the Committee on Nucleation and Atmospheric Aerosols (IAMAS-IUGG), 10 instruments for aerosol number concentration measurement were studied, covering a wide range of methods based on various different measuring principles. In order to investigate the detection limits of the instruments considered with respect to particle size, simultaneous number concentration measurements were performed for monodispersed aerosols with particle sizes ranging from 1.5 to 50 nm diameter and various compositions.The instruments considered show quite different response characteristics, apparently related to the different vapors used in the various counters to enlarge the particles to an optically detectable size. A strong dependence of the 50% cutoff diameter on the particle composition in correlation with the type of vapor used in the specific instrument was found. An enhanced detection efficiency for ultrafine hygroscopic sodium chloride aerosols was observed with water operated systems, an analogous trend was found for n-butanol operated systems with nonhygroscopic silver and tungsten oxide particles.     

鄂西火烧坪地区岩溶生态环境系统及生态农业模式初探

通过对鄂西火烧坪地区的自然地理、地质构造、水文地质、土壤、植被及社会经济等方面的初步研究表明，在岩溶化作用十分强烈的岩溶山区，当气候、地形地貌、地质构造等条件匹配较好时，同样存在着一些极其有利于农业、林业发展的特殊生态环境。在这些地区，结合当地植物资源特点和市场需求，通过农作物类型的调整，能够在短时间内使当地摆脱贫穷的面貌。但在发展经济的同时必须特别关注当地土地资源相对缺乏、水资源时空分布不均、生态环境脆弱等不利因素，做到适度开发，稳步发展，才能从根本上实现小康目标。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

渤海湾盆地北部奥陶纪岩相古地理

渤海湾盆地北部主要包括黄骅坳陷、沧县隆起、冀中坳陷和下辽河坳陷,区内奥陶系厚 6 0 0～ 80 0m,主要由石灰岩和白云岩组成。下奥陶统分为冶里组和亮甲山组,中奥陶统分为下马家沟组、上马家沟组和峰峰组,上统缺失。其中下马家沟组、上马家沟组和峰峰组又进一步划分为下段和上段。早奥陶世冶里期和亮甲山期,本区以局限海环境为主,其内散布着一些潮坪和滩。中奥陶世,本区岩相古地理面貌变化旋回极为明显,下马家沟期早期潮坪广布,晚期以局限海为主;上马家沟期早期又是潮坪广布,晚期以开阔海为主;峰峰期早期潮坪广布,晚期又以开阔海为主。在奥陶纪,本区经历了 4个大的海侵-海退旋回,其中上马家沟期晚期是本区乃至整个华北地台的最大海侵期。     

贵州喀斯特地区生态环境恶化的人为因素分析

贵州是喀斯特强烈发育的省区。由于人类活动的影响，近年来贵州喀斯特地区的生态环境出现了日益恶化的趋势，严重影响可持续发展甚至西部大开发战略的顺利实施。本阐述了喀斯地区生态环境恶化的现状，深入探讨了人为因素对喀斯特地区生态环境恶化的影响，为防止喀斯特地区生态环境进一步恶化提供科学依据。     

克孜尔水库大坝的面波检测

1999年 3月 15日克孜尔地震之后对克孜尔水库坝体出现的裂缝进行了面波勘探。首先介绍了面波勘探法的基本原理 ,然后通过典型剖面检测与资料处理找出异常区、带并进行解释。试验表明面波勘探法适用于克孜尔水库大坝监测     

Phénomènes karstiques fossiles et actuels au sein des formations métamorphiques silico-alumineuses de la nappe pan-africaine de Yaoundé (Sud-Cameroun)Fossil and present-time karstic phenomena in silico-aluminous metamorphic formations of the Pan-African nappe of Yaoundé (South-Cameroon)

Numerous karstic features have been recognised in the non-carbonaceous micaschists and gneisses of the Yaoundé Pan-African nappe, south of Cameroon. It is shown that their formation was controlled by the structural features of the rocks. The wells and the pipes in the bedrock outcrops point out a current karstification process, resulting from the plagioclase dissolution by the acid rain waters. Hill wall alveoli and caves, of pre-Miocene age, are exhumed features that were done by dissolution in the aquiferous underground. To cite this article: J.-P. Vicat et al., C. R. Geoscience 334 (2002) 545–550.     

基于GIS的黄河上游龙羊峡库区生态环境遥感监测研究

本项研究以黄河上游生态环境严重退化的龙羊峡库区为研究区，应用遥感与GIS一体化的方法，建立了库区生态环境动态监测信息系统。在系统的支持下，利用多时相、多波段TM遥感数据，对库区近十年来生态环境变化进行监测分析，揭示了库区土地沙漠化、水土流失、土地利用／土地覆盖的现状，以及建库近十年来生态环境时空演化的过程与发展趋势，为库区生态环境治理提供科学依据。本项研究利用遥感和GIS技术，采用生态环境综合分类系统，在一次分类处理中通过多层次不同级别的组合获得了基于同一信息源的土地沙漠化、草场盖度、土地利用处地覆盖等多种环境类型信息，并利用环境信息模型对库区的土壤侵蚀进行了评价。基于GIS的分类后再处理使遥感分类精度有了一定程度的提高。研究表明，在GIS的支持下充分利用遥感信息监测与评价生态环境，是区域生态环境定量化研究的有效途径。