海洋纤维增强热塑性立管极限承载拉力预测方法

海洋新型纤维增强热塑性立管因其可盘卷、耐腐蚀、耐疲劳和轻质化等优点,在深水油气开发中应用前景十分广阔。热塑性立管具有复合材料的各向异性、受力耦合效应及复杂的本构关系,且承受浮体运动和复杂海洋环境载荷,其失效模式尚未明确。针对轴对称载荷作用下纤维增强热塑性立管极限承载力问题,进行热塑性管稳态热传导和热应力的理论推导,求解了稳态温度和应力分布,首次给出了在任意温度载荷作用下管体径向位移的解析解,并直接求解其径向、轴向、环向和剪切应力。采用各向同性层Von Mises和各向异性层最大应力（Max Stress）准则或Tsai-Hill准则判定热塑性管的失效,基于应力分布、失效准则和二分法计算了热塑性管的极限载荷。温度载荷、纤维铺设角度和径厚比对管道的应力分布影响显著。不同温度载荷会改变失效指数沿径向的变化趋势,增大轴向拉力将增大热塑性管的失效指数,选用不同的失效准则在管体失效判定上存在一定的差异。热塑性管温度越低、纤维铺设角越小及径厚比越大,管道对轴向拉伸载荷的承载能力越强。     

哈尔滨市地下水中29种抗生素分布特征研究

当前对抗生素滥用监管及其研究正在加强,近年来中国主要水域中抗生素均有不同程度的检出,地表水及地下水中抗生素的污染状况持续受到关注。因进入环境中的抗生素种类繁多、结构复杂,一般实验室难以实现同时分析多种类抗生素。本文在哈尔滨市共采集地下水样品26组,采样范围包括人口密集、工业生产、农畜业等生活生产地区。利用超高效液相色谱-三重四极杆串联质谱联用技术分析了样品中的磺胺类、喹诺酮类、大环内酯类、β-内酰胺类、四环素类、林可酰胺类等6大类共29种典型抗生素含量,研究了哈尔滨市地下水中典型抗生素的检出及分布状况。结果表明：①哈尔滨市地下水中6大类典型抗生素均有不同程度检出,其中以磺胺类、喹诺酮类、大环内酯类、四环素类为主,检出率分别为61.5%、46.2%、42.3%、38.5%;②哈尔滨市地下水检出的抗生素含量范围在0.02~612ng/L之间,其中磺胺噻唑、磺胺嘧啶、林可霉素检出的最高浓度超过100ng/L,相比于国内外部分地区（如中国北京、天津,西班牙巴塞罗那）喹诺酮类整体含量偏低;③检出抗生素含量较高的采样点位主要分布在城市的中部、南部和东部地区,这些区域也是该市人口相对密集区,且附近普遍分布有制药厂、家禽牲畜养殖厂、城市排污口等。由此揭示了哈尔滨市城市地下水中抗生素分布特征受人类生产生活活动影响且具有明显的相关性。     

鄂尔多斯盆地煤储层低温氮等温吸附特征分析

利用美国Micromritcs ASAP 2000自动等温吸附仪,测试了鄂尔多斯盆地煤储层孔隙特征。在测试数据分析的基础上,确定了等温吸附和脱附曲线3种类型:第1类属孔结构是具有平行权构成的狭缝毛细孔型;第2类为发育一端尖灭的不平行的裂隙型;第3类属“墨水瓶”型孔。总结了鄂尔多斯盆地煤储层几种常见的比表面积分布曲线及累计孔体积分布曲线模型。     

内蒙古维拉斯托锡多金属矿石英脉形态分带定量分析

为探讨维拉斯托锡多金属矿床含锡石英脉形态分带的成矿动力学机制,通过利用分形和混合分布工具对断层脉带和上、下节理脉带进行定量分析。上、下节理脉带在脉厚、钨锡品位分形维数和混合筛分分布方面均具有相似性,暗示了两者可能具有相似的成矿机制。与上、下节理脉带相比,断层脉带的脉厚和钨锡品位分形维数均较小,断层脉带内的聚焦化流动、较低的脉体成核率、脉体叠加生长和矿化叠加富集可能是导致断层脉带的厚脉和富矿比例均高于上、下节理脉带的重要原因。     

Fingerprinting the 8.2 ka event climate response in a coupled climate model

Using results from coupled climate model simulations of the 8.2 ka climate event that produced a cold period over Greenland in agreement with the reconstructed cooling from ice cores, we investigate the typical pattern of climate anomalies (fingerprint) to provide a framework for the interpretation of global proxy data for the 8.2 ka climate event. For this purpose we developed an analysis method that isolates the forced temperature response and provides information on spatial variations in magnitude, timing and duration that characterise the detectable climate event in proxy archives. Our analysis shows that delays in the temperature response to the freshwater forcing are present, mostly in the order of decades (30 a over central Greenland). The North Atlantic Ocean initially cools in response to the freshwater perturbation, followed in certain parts by a warm response. This delay, occurring more than 200 a after the freshwater pulse, hints at an overshoot in the recovery from the freshwater perturbation. The South Atlantic and the Southern Ocean show a warm response reflecting the bipolar seesaw effect. The duration of the simulated event varies for different areas, and the highest probability of recording the event in proxy archives is in the North Atlantic Ocean area north of 40° N. Our results may facilitate the interpretation of proxy archives recording the 8.2 ka event, as they show that timing and duration cannot be assumed to correspond with the timing and duration of the event as recorded in Greenland ice cores. Copyright © 2011 John Wiley & Sons, Ltd.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

A new standardized Palmer drought index for hydro‐meteorological use

Accepting the concept of standardization introduced by the standardized precipitation index, similar methodologies have been developed to construct some other standardized drought indices such as the standardized precipitation evapotranspiration index (SPEI). In this study, the authors provided deep insight into the SPEI and recognized potential deficiencies/limitations in relating to the climatic water balance it used. By coupling another well‐known Palmer drought severity index (PDSI), we proposed a new standardized Palmer drought index (SPDI) through a moisture departure probabilistic approach, which allows multi‐scalar calculation for accurate temporal and spatial comparison of the hydro‐meteorological conditions of different locations. Using datasets of monthly precipitation, temperature and soil available water capacity, the moisture deficit/surplus was calculated at multiple temporal scales, and a couple of techniques were adopted to adjust corresponding time series to a generalized extreme value distribution out of several candidates. Results of the historical records (1900–2012) for diverse climates by multiple indices showed that the SPDI was highly consistent and correlated with the SPEI and self‐calibrated PDSI at most analysed time scales. Furthermore, a simple experiment of hypothetical temperature and/or precipitation change scenarios also verified the effectiveness of this newly derived SPDI in response to climate change impacts. Being more robust and preferable in spatial consistency and comparability as well as combining the simplicity of calculation with sufficient accounting of the physical nature of water supply and demand relating to droughts, the SPDI is promising to serve as a competent reference and an alternative for drought assessment and monitoring. Copyright © 2013 John Wiley & Sons, Ltd.     

Size and spatial distribution of loess slides on the Chinese Loess Plateau

ABSTRACT

The size and spatial distribution of loess slides are important for estimating the yield of eroded materials and determining the landslide risk. While previous studies have investigated landslide size distributions, the spatial distribution pattern of landslides at different spatial scales is poorly understood. The results indicate that the loess slide distribution exhibits a power-law scaling across a range of the size distribution. The mean landslide size and size distribution in the different geomorphic types are different. The double Pareto and inverse gamma functions can coincide well with the empirical probability distribution of the loess slide areas and can quantitatively reveal the rollover location, maximum probability, and scaling exponents. The frequency of loess slides increases with mean monthly precipitation. Moreover, point distance analysis showed that > 80% of landslides are located < 3 km from other loess slides. We found that the loess slides at the two study sites (Zhidan and Luochuan County) in northern Shaanxi Province, China show a significant clustered distribution. Furthermore, analysis results of the correlated fractal dimension show that the landslides exhibit a dispersed distribution at smaller spatial scales and a clustered distribution at larger spatial scales.     

SOE技术在测绘成果网络分发服务系统中的应用研究

作为测绘信息化的重要组成部分,测绘成果网络分发服务系统的建设提升了测绘成果信息化管理水平,促进了测绘成果的推广应用。本文在总结福建省测绘成果网络分发服务系统建设成果的基础上,基于SOE技术解决其在推广应用过程中存在问题,提高了测绘成果的公共服务能力与水平。     

印度区域地质矿产概况

印度共和国是印度板块的主体,也是冈瓦纳大陆的重要组成部分,主要由七个古老克拉通（陆块群）、分隔克拉通的活动带与盆地等构成。自北向南依次为：①喜马拉雅活动带,主要为具有元古代基底的古近纪-新进纪活动带;②印度河-恒河平原过渡带（山前坳陷带）,主要由为第四系、古近系-新进系和第四系冲积物构成;③印度半岛克拉通,主要由西塔尔瓦尔、东塔尔瓦尔、巴斯塔、辛本,本德尔坎德、阿拉瓦利和印度南部麻粒岩地体等7个太古宙陆块（或次级克拉通）群构成;④萨德布尔活动带;⑤东高止山活动带;⑥德干高原玄武岩省（LIP）（图1）。