Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and -ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data.     

Rainfall–runoff trends in the south‐eastern USA: 1938–2005

An extensive dataset (230 precipitation gauges and 79 stream gauges) was used to analyse rainfall–runoff relationships in 10 subregions of a 482000 km² area in the south‐eastern USA (Maryland, Virginia, North Carolina, South Carolina and Georgia). The average annual rainfall and runoff for this study area between 1938 and 2005 were 1201 and 439 mm, respectively. Average runoff/rainfall ratios during this period varied between 0·24 in the southernmost Coastal Plain subregion to 0·64 in the Blue Ridge Province. Watershed elevation and relief are the principal determinants governing the conversion of rainfall to runoff. Temporal rainfall variation throughout the south‐eastern USA ranges from ～40% above and below normal while the variation for runoff is higher, from ? 75% to + 100%. In any given year there can exist a ± 25–50% error in predicted runoff deviation using the annual rainfall–runoff regression. Fast Fourier Transform and autoregressive spectral analysis revealed dominant cyclicities for rainfall and runoff between 14 and 17 years. Secondary periodicities were typically between 6–7 and 10–12 years. The inferred cyclicity may be related to ENSO and/or Central North Pacific atmospheric phenomena. Mann–Kendall analyses indicate that there were no consistent statistically significant temporal trends with respect to south‐eastern US rainfall and runoff during the study period. The results of U‐tests similarly indicated that rainfall between 1996 and 2005 was not statistically higher or lower than during earlier in the study period. Copyright © 2008 John Wiley & Sons, Ltd.     

利用多通道的光学超晶格产生对, 纠缠的连续变量频率梳

提出了一种实验上可行的,通过光学谐振腔内多通道参量下转换过程直接产生多色连续变量对纠缠频率梳的方案,腔内增益介质为周期极化的准相位匹配的钽酸锂(LiTaO₃)一维光学超晶格.通过连续变量纠缠判据证明了每个通道中产生的每对参量光之间是相互纠缠的.讨论了对纠缠频率梳的纠缠特性随系统参数的变化.此方案在量子通信网络中有着一定的应用前景.     

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A physically constrained wavelet-aided statistical model for multi-decadal groundwater dynamics predictions

A physically constrained wavelet-aided statistical model (PCWASM) is presented to analyse and predict monthly groundwater dynamics on multi-decadal or longer time scales. The approach retains the simplicity of regression modelling but is constrained by temporal scales of processes responsible for groundwater level variation, including aquifer recharge and pumping. The methodology integrates statistical correlations enhanced with wavelet analysis into established principles of groundwater hydraulics including convolution, superposition and the Cooper–Jacob solution. The systematic approach includes (1) identification of hydrologic trends and correlations using cross-correlation and multi-time scale wavelet analyses; (2) integrating temperature-based evapotranspiration and groundwater pumping stresses and (3) assessing model prediction performances using fixed-block k-fold cross-validation and split calibration-validation methods. The approach is applied at three hydrogeologicaly distinct sites in North Florida in the United States using over 40 years of monthly groundwater levels. The systematic approach identifies two patterns of cross-correlations between groundwater levels and historical rainfall, indicating low-frequency variabilities are critical for long-term predictions. The models performed well for predicting monthly groundwater levels from 7 to 22 years with less than 2.1 ft (0.7 m) errors. Further evaluation by the moving-block bootstrap regression indicates the PCWASM can be a reliable tool for long-term groundwater level predictions. This study provides a parsimonious approach to predict multi-decadal groundwater dynamics with the ability to discern impacts of pumping and climate change on aquifer levels. The PCWASM is computationally efficient and can be implemented using publicly available datasets. Thus, it should provide a versatile tool for managers and researchers for predicting multi-decadal monthly groundwater levels under changing climatic and pumping impacts over a long time period.     

Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response

Following many applications artificial neural networks (ANNs) have found in hydrology, a question has been rising for quantification of the output uncertainty. A pre‐optimized ANN simulated the hydraulic head change at two observation wells, having as input hydrological and meteorological parameters. In order to calculate confidence intervals (CI) for the ANN output two bootstrap methods were examined namely bootstrap percentile and BCa (Bias‐Corrected and accelerated). The actual coverage of the CI was compared to the theoretical coverage for different certainty levels as a means of examining the method's reliability. The results of this work support the idea that the bootstrap methods provide a simple tool for confidence interval computation of ANNs. Comparing the two methods, the percentile requires fewer calculations and yields narrower intervals with similar actual coverage to that of BCa. Overall, the actual coverage was proved lower than desired when not modeled points were present in the data subset. Copyright © 2011 John Wiley & Sons, Ltd.     

近25 a气候变化对江苏省粮食产量的影响

利用1986-2010年江苏省63个气象站的常规气象数据和粮食单产统计资料,分析了苏北、苏中、苏南地区和江苏全省三种时间尺度的气候变化特征;基于自助抽样(bootstrap)和一元线性回归的方法,研究了各区和全省粮食产量对作物年(11月一次年10月)、夏粮—秋粮生长季(11月-次年5月和6-10月)和月尺度气候要素的响应;并定量评价了过去25 a气候变化对各区和全省粮食产量的影响以及各气候要素的贡献.结果表明:1)在作物年、夏粮—秋粮生长季以及月尺度上,三区和全省各气候要素均发生了不同程度的变化,且存在一定的时空差异.在不断发展的农业管理措施和技术以及气候的共同作用下,三区和全省粮食单产显著(p＞0.01)增加,其中,全省增加趋势为66.89 kg·hm-2·a-1.2)除苏南地区对作物年尺度上的气候变化响应不显著外,粮食产量对降水的不随时间变化的负响应关系(即随降水的增加而减小,减小而增加)均在不同时间尺度和地区得到了体现,说明降水对这些地区粮食生产的影响十分重要;其中,苏北、苏中和全省粮食产量随作物年降水的增加(减少)而减小(增加),平均速率分别为0.19％·(10 mm)-1、0.09％·(10 mm)-1和0.11％·(10 mm)-1.3)三类模型结果均显示气候变化使得苏北、苏南和江苏粮食产量减小,但结果略有差异,其中,利用月气候要素建立的模型C的结果显示气候变化对粮食单产(总产)的影响最大,其均值分别为-6.51％·(10 a)-1(-11.28×108kg· (10 a)-1)、-3.27％·(10 a)-1(-2.36×108 kg·(10 a)-1)和-1.34％·(10 a)-1(-4.45×108kg·(10 a)-1).另外,为了系统而全面地评估气候变化对粮食产量的影响,考虑月尺度的气候变化的影响是十分必要的.     

共线SPDC下剩余脉冲泵浦光滤光特性研究

从宽带脉冲泵浦光的光谱及滤光片的滤光特性参数出发,分析了自发参量下转换（SPDC）产生纠缠光源的系统中各类滤光片对剩余脉冲泵浦光的滤除效率。分析结果表明,适当组合不同类型的滤光片可以有效地滤除剩余脉冲泵浦光,并从实验上验证了分析结果的正确性。该研究为脉冲泵浦光条件下SPDC过程产生的纠缠光源的有效测量提供了理论指导和实验验证。     

Impact of the Mass Parameter on Particle Dynamics in a Ring Configuration of N Bodies

Parameters play a very important and determinative role in the dynamics of a dynamical system as well as in the formation of its particular characteristics. In this paper we investigate the way in which a large scale variation of the mass parameter, influences the behavior of a mass-less particle which moves in the vicinity of a ring arrangement of N-bodies. More precisely, we study the impact of this parameter on periodic motions and their characteristics.     

Terrain Pattern Recognition and Spatial Decision Making for Regional Slope Stability Studies

A terrain partition scheme is presented that allows the identification of regions with high landslide risk in natural terrain zones on the basis of geomorphometric criteria from moderate resolution DEMs. The key factor being the terrain segmentation to aspect regions (regions formed by points preserving the same aspect direction) instead of using an artificial regular-grid terrain partition scheme. The study area is in western Greece (NW Peloponnesus) whereas a moderate resolution digital elevation model with spacing 75 m is used. Landslide inventory analysis and knowledge conceptualization identified that the landslide susceptibility of a particular aspect region is high, if the mean elevation is low and the mean gradient is high. Each aspect region was parametrically represented on the basis of its mean gradient and elevation. The domain of each parameter was divided to seven slices (classes) on the basis of the observed density. Subsequent knowledge based mapping identified aspect regions with high landslide susceptibility for the following spatial rule: (a) “mean slope in class 6 or 7” and (b) “mean elevation in class 1 to 5”. Alternatively the rule is expressed as mean slope to be equal or greater than 15^∘ whereas mean elevation to be in the range 0 to 750 m. These identified zones correspond to regions where historical landslides occurred (populated coastal areas in the North) as well as to south regions (natural terrain zone) where no landslide record is available, because of the limitations posed by the natural terrain landslide mapping program in Greece. The presented terrain segmentation technique combined to the spatial decision-making process, provided both an object framework for integrating geomorphometric parameters and a method for landslide risk analysis in natural terrain zones.