Ecological requirements of Ostracoda (Crustacea) in three types of springs in Turkey

Seasonal occurrence of six ostracod species (Psychrodromus olivaceus, Potamocypris villosa, Ilyocypris inermis, Candona candida, Candona neglecta, and Cypridopsis vidua) varied from May 2003 to June 2004 in three types of spring (Limnocrene, Helocrene, Rheocrene). When I. inermis was only found in the limnocrene spring each month, P. olivaceus was common in all types of springs. The other four ostracods were found in the helocrene spring in fall, winter, and spring seasons. The unweighted pair group mean averages dendrogram separated three main clusters. The first cluster includes two species (C. vidua, C. candida) while I. inermis was found in the second cluster and P. olivaceus, C. neglecta, and P. villosa were found in the third. The occurrence of P. olivaceus and C. neglecta and P. villosa and C. candida was strongly correlated to each other (r=0.750 and 0.850, respectively). The Pearson correlation analyses showed a strong negative relationship between C. neglecta and water temperature (r=−0.607), but other species did not show any significant correlation to any of the environmental variables used (P>0.05). Canonical correspondence analyses (CCA) displayed all species closer to the center of the dendrogram, indicating high levels of species tolerances to environmental variables. Thus, the first axis of the CCA diagram explained 80% of the relationship between six species and five environmental variables.     

基于Bayes方法的四川主要河流水质综合评价

给出了Bayes方法(Bayes Method)的思路和计算方法。据四川8条河12个站点的多年水质监测数据,选择了6个水质指标,以GB3838-2002为评价标准,首次以抽样误差正态分布原理估计Bayes公式中的似然概率,进而基于Bayes公式做水质评价。评价结果表明多年平均情况下水质为Ⅰ、Ⅱ、Ⅲ类,但以Ⅰ类为主,少数为Ⅲ类。     

Lognormal distribution of the observed and modelled neutral thermospheric densities

For analyzing measurements of any kind, it is important to estimate the probability distribution of the measurement errors. When modelling the observations using least-squares fitting, the distribution of the errors plays a vital role in choosing the merit function to be minimized, as unnormally distributed errors (e.g. outliers, or displaying asymmetry around the mean) may substantially skew a least-squares fit of estimated model parameters. Using the CACTUS accelerometer data covering heights of 230–750 km, we will show that the statistical relationship between the commonly used semi-empirical models of neutral thermospheric density (MSIS, DTM) and the observed densities is consistent with lognormal distribution, i.e. the logarithm of the ratio of the measurements to the predictions is approximately normally distributed. This experimental fact may be applied in modelling the neutral thermospheric density. bezdek@asu.cas.cz     

On selection of kernel parametes in relevance vector machines for hydrologic applications

Recent advances in statistical learning theory have yielded tools that are improving our capabilities for analyzing large and complex datasets. Among such tools, relevance vector machines (RVMs) are finding increasing applications in hydrology because of (1) their excellent generalization properties, and (2) the probabilistic interpretation associated with this technique that yields prediction uncertainty. RVMs combine the strengths of kernel-based methods and Bayesian theory to establish relationships between a set of input vectors and a desired output. However, a bias–variance analysis of RVM estimates revealed that a careful selection of kernel parameters is of paramount importance for achieving good performance from RVMs. In this study, several analytic methods are presented for selection of kernel parameters. These methods rely on structural properties of the data rather than expensive re-sampling approaches commonly used in RVM applications. An analytical expression for prediction risk in leave-one-out cross validation is derived. For brevity, the effectiveness of the proposed methods is assessed first by data generated from the benchmark sinc function, followed by an example involving estimation of hydraulic conductivity values over a field based on observations. It is shown that a straightforward maximization of likelihood function can lead to misleading results. The proposed methods are found to yield robust estimates of parameters for kernel functions.     

一种激光扫描数据与数码照片的配准方法

提出了一种激光扫描数据和数字图像配准的方法。该方法基于立体像对匹配点与三维扫描点云的最近邻迭代配准。配准中，采用M-估计的选择权迭代在最邻近点搜索算法中逐步消除立体像对误匹配粗差点的影响，得到正确的收敛结果，并基于地面实验证明此方法是可行的和有效的。     

规则格网DEM自动综合方法的评价

介绍了规则格网DEM自动综合方法的评价策略，应用4种常用自动综合方法对同一区域1：5万DEM进行了综合处理，生成了1：25万DEM数据。从高程值分布、派生坡度情况以及反生等高线叠置比较这3个方面对综合结果进行了评价，并分析了各综合方法影响结果精度的主要原因。实验表明，结构化综合方法具有较高的精度。     

基于牛顿迭代法的干涉SAR基线估计

根据SAR干涉原理,提出基于牛顿迭代法的干涉SAR基线估计算法,利用1996年4月15日和16日的ERS-1/2C波段的单视复数据,用该算法求得的基线值和用从欧洲空间局(ESA)的有关基线数据库中查到该景干涉雷达数据相应的基线值分别进行去平地相位的实验,证明基于牛顿迭代法的干涉SAR基线估计提取的基线参数比较精确。     

胶东金矿床中关键金属资源储量估算与潜力初探SCIEI北大核心CSCD

通过对大尹格庄和夏甸两个超大型金矿床的精金矿与尾矿砂中关键元素含量的分析,发现相对于华北克拉通地壳元素丰度,本次分析的稀贵元素Co、Rh、Ir和Ru,稀散元素Cd、Te和Se,稀有元素W和In均发生了不同程度的富集;特别是Te、Co和Cd超常富集达到伴生组分综合评价品位。根据伴生有用组分综合评价规范和金矿选矿报告相关参数,分别估算了金矿石和精金矿中可利用的关键金属矿产储量,其中夏甸金矿床内Te储量为69吨(精金矿中52吨)、Co储量为604吨(精金矿中413吨),大尹格庄金矿床内Cd储量为224吨(精金矿中206吨),这些均可直接回收利用。矿物学和矿物化学综合研究表明:Te主体以碲金矿、碲银矿、碲铋矿、碲铅矿和陈国达矿等独立矿物存在,与可见金密切共生;Co常以微量元素形式分布在金成矿早阶段的粗粒黄铁矿和磁黄铁矿中;Cd主要以类质同象的形式赋存于金成矿晚阶段的闪锌矿、黄铁矿和黄铜矿中。进一步通过对典型金矿床中黄铁矿原位和/或单矿物的Te与Co含量对比、并结合矿石中Co与Cd元素组成及其区域地球化学空间分布特征,综合约束了其超常富集特征与资源潜力,揭示新城、玲珑和寺庄金矿床及栖霞异常区分别有约2329吨、1035吨、1553吨和22790吨Co资源量,乳山、新城和焦家金矿床有约1529吨、126吨、216吨Te资源量,仓上、新立、三山岛、寺庄和新城金矿床、以及栖霞和招平北段异常区分别有约47.6吨、78吨、63.7吨、69吨、3564吨、7120吨和696吨Cd资源量;即胶东金矿集区具备近期被综合利用或作为未来潜在接替资源的Co、Te和Cd资源条件,且其展布区域广泛、资源潜力巨大。初步研究已显示胶东具有形成大型-超大型Cd、Te和Co矿床的资源条件,但关键金属资源的空间分布极不均一、其超常富集机理与规律尚不清楚,亟需深入研究。     

A tool for the ages: The Probabilistic Cosmogenic Age Analysis Tool (P-CAAT)

While revolutionary to the geomorphic community, the application of terrestrial cosmogenic nuclide (TCN) dating is complicated by geological uncertainties, which often lead to skewed or poorly clustered TCN age distributions. Although a range of statistical approaches are typically used to detect and remove outliers, few are optimized for analysis of TCN datasets. Many are mean- or median-based and therefore explicitly assume a single probability distribution (e.g., Mean Squared Weighted Deviates, Chauvenet's Criterion, etc.). Given the ubiquity of pre- and post-depositional modification of rock surfaces, which occur at different rates in different geomorphic settings, these approaches struggle with multimodal distributions which often characterize TCN datasets. In addition, most statistical approaches do not propagate measurement or production rate uncertainties, which become increasingly important as dataset size or clustering increases. Finally, most approaches provide arithmetic single solutions, irrespective of geologic context.To address these limitations, we present the Probabilistic Cosmogenic Age Analysis Tool (P-CAAT), a new approach for outlier detection and landform age analysis. This tool incorporates both sample age and geologic uncertainties and uses Monte Carlo simulations to eliminate dataset skewness by isolating component normal distributions from a cumulative probability density estimate for datasets with three or more samples. This approach allows geologic context to inform post-analysis interpretations, as researchers can assign landform ages based upon statistically distinct subpopulations, informed by the characteristics of geomorphic systems (e.g., exhumation of boulders as moraines degrade through time). To evaluate the effectiveness of P-CAAT, we analyzed a range of synthetic TCN datasets and compared the results to commonly used statistical approaches for outlier detection. Irrespective of dataset size or clustering, P-CAAT outperformed other approaches and returned accurate solutions that improve in precision as sample size increases. To enable more comprehensive utilization of our approach, P-CAAT is packaged with a GUI interface and is available for download at kgs. uky.edu/anorthite/PCAAT.     

资源量估算的边界分析与矿化体圈定

资源量估算总是在特定的估算域中进行,其边界条件对估值结果有着显著的影响。传统方法采取用工业指标来圈定矿体并进行资源量估算的硬边界条件,过于强调了矿床的经济性,而对矿床的地质和地质统计学规律重视不够。本文介绍了国际上流行的资源量估算域的相关概念及边界分析方法,并通过实际案例来探讨其在资源量估算中的应用,在此基础上提出了利用三维软件进行矿(化)体圈定的基本原则。实践表明,利用软边界约束或圈定矿化体进行品位估计,可能更符合勘查阶段资源量估算的要求,进而降低估算域边界的不确定性带来的估值风险。