Nonrenewable resources in economic, social, and environmental sustainability

This paper reviews the linkages among energy and mineral resources and economic, social, and environmental sustainability. Nonrenewable resources are shown to be integral components of sustainability, regardless of which paradigmatic definition of the concept is invoked. Potential measures of the degree to which nonrenewable resources contribute to or detract from sustainability are presented. We conclude that a set of such measures should be incorporated in the comprehensive framework of sustainability currently being developed by the Federal government. National scale indicators could be presented within the structure of the seven criteria from the Santiago Declaration, whereas a life cycle or materials flow approach could be used when sustainability of specific resources is at issue.     

A proposal for comparing the reliabilities of alternative seismic hazard models

Differences between statistical unertainty and modeling uncertainty are briefly discussed. It is pointed out that, when different models are proposed for the interpretation of reality, the uncertainty cannot be described in terms of mean value and coefficient of variation. The important question is: which of the proposed models is more reliable than the others? The attention, then, is concentrated on the comparison between different models proposed for the estimate of the required quantity, looking for a criterion leading to the selection of the most reliable one. A criterion for comparison of different models is suggested. In the example of application considered in this paper, it proved to be effective, so that the continuation of numerical experiments, exploring different and more complex situations, seems promising.     

Magma traps: Part II,application

A model of the lithostatic control of the ascent of magma, described in Part I (this volume), is tested against data from the Upper Cretaceous-Lower Tertiary sedimentary and volcanic region of central West Greeland: the thickness of sedimentary rock; the thickness of the pillow breccias; the total thickness of the lava pile; the depth of the post volcanic paleosurface. The local development is largely determined by a single parameter, the proportion of crustal thinning, and requires a magma source at 75 km depth with differentiation at 11 km depth. The model is applied in outline to the development of continental and orogenic volcanism in New Zealand.     

Multiscale approach to geo‐composite cellular structures subjected to rock impacts

Geo‐composite cellular structures are an efficient technological solution for various applications in civil engineering. This type of structure is particularly well adapted to resisting rockfalls and can act as a defensive structure. However, the design of such structures is for the most part empirically based; this lack of research‐based design stagnates optimization and advanced development. In this paper, the mechanical behaviour of a geo‐composite cellular structure is investigated using a multi‐scale approach, from the individual cell made up of an assembly of rocky particles contained in a wire netting cage to the entire structure composed of a regular array of cells. Based on discrete modelling of both the cell and structure scales, a computational tool has been developed for design purposes. Copyright © 2007 John Wiley & Sons, Ltd.     

基于Landsat-8遥感影像和LiDAR测深数据的水深主被动遥

主被动遥感结合反演远海岛礁周边水深信息,不仅可以有效弥补传统测深方法覆盖范围小且费时费力的不足,也可为航运安全、海洋减灾、生态环境保护等领域提供基础资料。以夏威夷瓦胡岛周边水深反演为例,应用Landsat-8多光谱遥感数据和机载Li DAR测深数据,开展了不同密度Li DAR测深数据对水深多光谱遥感反演精度的影响分析、不同水深网格化处理方法对水深遥感反演结果的影响分析和基于少量Li DAR控制区块的大区域水深反演能力分析三方面的研究工作。结果表明:(1)Li DAR测深数据密度的改变对水深反演结果的影响不大,变化后的水深反演结果与原始的水深反演结果相比,平均相对误差变化在0.3%以内,平均绝对误差变化在0.03m以内;(2)采用均值格网处理方法的多光谱遥感水深反演精度要略高于采用中值格网处理方法的水深反演精度,具体体现在均值的平均绝对误差要比中值的低0.04~0.05 m,平均相对误差低1%~10%,反演结果的残差分布显示在0~2 m和20~25 m的水深段内均值统计法的残差分布更集中且其平均值接近于0 m,而在其它水深段二者的残差分布基本相同;(3)基于少量Li DAR控制区块的大区域遥感水深反演结果较为理想,两个检查区块的水深反演结果 R2、平均绝对误差和平均相对误差分别为:0.877,1.66 m,3.5%和0.941,1.62 m,28.4%。反演结果分段分析表明各水深段内反演的精度都比较理想,平均绝对误差除20~25 m水深段外,均低于2.5 m,平均相对误差除0~2 m,2~5 m外,均低于25%。     

GOCE+EGM08+RTM组合模型计算高程异常

针对EGM08重力场模型构建过程中存在的不足,提出用GOCE重力场模型替换EGM08模型的中低频部分,用剩余地形模型RTM拓展EGM08模型的甚高频信号。模拟分析表明,GOCE模型能大幅提高高程异常计算的精度,而RTM对高程异常的贡献也不可忽视。实测GPS/水准数据表明,GOCE模型对高程异常的贡献达到43%,而RTM也贡献了1cm的精度。     

南海东北陆缘的地壳速度结构及其构造意义: 来自广角地震剖面的约束

南海东北部深部地壳结构蕴含着南海陆缘伸展张裂过程的重要信息。在南海东北陆缘布设的一条广角地震测线(DP13)沿NW-SE方向依次穿过东沙隆起和台西南盆地。本文利用射线追踪和正演走时拟合软件RayInvr构建地壳纵波速度结构,模型表明：沉积层速度1.6～4.6 km/s,厚度0.5～3.8 km,横向分布不均匀,沉积基底起伏剧烈;莫霍面埋藏深度由陆架区的25.5 km急剧减小到陆坡下方的13 km,随后向下陆坡远端增深至16 km;陆架处东沙隆起下方地壳厚度从～25 km减薄到～21 km,下陆坡远端地壳厚约10～13 km,地壳拉张因子分别为1.3～1.5和2.6～3.1,表现为轻微和中等减薄;陆坡区台西南盆地内地壳厚度从17 km急剧减薄至7～8 km,地壳拉张因子高达4.6,呈超伸展减薄;地壳厚度由陆向海非单调减薄,地壳伸展具有明显的空间差异性;陆架-上陆坡和下陆坡下地壳底部发现两个相对孤立的不连续高速体,速度分别为7.0～7.5 km/s和7.0～7.3 km/s,厚度分别3～5 km和1～3 km,前者位于古太平洋俯冲带前缘,几乎与南海东北部高磁异常重叠,推测由中生代古太平洋板...     

Evaluation of the Historical Sampling Error for Global Models

Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is to use an ensemble forecast; however, this is very time-consuming. The historical sampling approach is an alternative way to generate the ensemble,by picking some snapshots from historical forecast series.With this approach, many ensemble-based assimilation schemes can be used in a deterministic forecast environment. Furthermore, considering the time that it saves, the method has the potential for operational application.However, the historical sampling approach carries with it a special kind of sampling error because, in a historical forecast, the way to integrate the ensemble members is different from the way to integrate the initial conditions at the analysis time(i.e., forcing and lateral boundary condition differences, and ‘warm start' or ‘cold start' differences). This study analyzes the results of an experiment with the Global Regional Assimilation Prediction System-Global Forecast System(GRAPES-GFS), to evaluate how the different integration configurations influence the historical sampling error for global models. The results show that the sampling error is dominated by diurnal cycle patterns as a result of the radiance forcing difference.Although the RMSEs of the sampling error are small, in view of the correlation coefficients of the perturbed ensemble, the sampling error for some variables on some levels(e.g., low-level temperature and humidity, stratospheric temperature and geopotential height and humidity), is non-negligible. The results suggest some caution must be applied, and advice taken, when using the historical sampling approach.     

Data selection using support vector regression

Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection(thinning)methods to preserve essential information. Satellites, such as Wind Sat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression(SVR), is developed and tested. To manage large on-line satellite data streams, observations from Wind Sat are formed into subsets by Voronoi tessellation and then each is thinned by SVR(TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods(random selection, averaging and Barnes filtering), producing a 10% thinning rate(90% data reduction), low mean absolute errors(MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s-1and correlations 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.