统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。     

改进 GＲNN 网络预测致密砂岩气层压裂产能

致密砂岩储层孔隙度小、渗透率低、含气饱和度低,基本上没有自然产能,需要进行压裂,所以压裂产能的预测很重要。广义回归神经网络 ( GＲNN) 稳定,对样本数量的要求低。产能预测关键是样本的选取以及扩展因子的选取。在原有的 GＲNN 预测产能的基础上,利用交叉验证法改进 GＲNN 网络,选取最优的样本确定最优的 GＲNN 网络结构,利用循环判断法,选取最优的扩展因子。改进的 GＲNN 神经网络可以避免确定 GＲNN 网络结构和扩展因子过程中过多的人为影响。笔者利用灰色关联分析法分析压裂产能的影响因素,利用改进的 GＲNN 网络有针对性地建立适合苏里格地区致密砂岩气层的压裂产能预测模型。结果表明该方法在苏里格地区气层压裂产能预测中有较好的应用效果。     

A diagnostic modelling framework to construct indices of biotic integrity: A case study of fish in the Zeeschelde estuary (Belgium)

We propose a coherent regression model building framework to construct fish-based indices. More specifically, we concentrate on the selection of an optimal set of metrics which remains a difficult problem. The paper departs from the observation that an index of biotic integrity (IBI) is analogous to a diagnostic model in medicine assessing the health condition of a patient from a series of biomarkers. In the same vein, an IBI is a diagnostic model predicting the ecosystem condition of a site from a set of (scored) metrics. Metrics are community attributes sensitive to anthropogenic pressure and their scores express the “distance to target” to a reference condition. In a medical context, Receiver Operating Characteristic (ROC) curves are commonly used to assess the diagnostic accuracy of laboratory tests. An ROC curve plots the sensitivity of a test (Se; the capacity to detect a disease or degradation) as a function of its false positive fraction (FPF) which is the complement of the specificity (Sp = 1 - FPF; the capacity to recognise a healthy person or a reference condition). The ROC curve represents the strength of the index to discriminate between degraded and reference sites. Higher curves correspond to stronger tests as then a higher sensitivity can be combined with a lower false positive fraction. Hence, it is intuitively clear to use summary statistics of the ROC curve as criteria to optimise medical tests or biotic indices. In this paper, we illustrate the value of this modelling framework with a case study in the Zeeschelde estuary in Belgium. In essence, a “traditional” IBI is an average of metrics scoring relevant properties of the ecosystem. We demonstrate this average score model (AVG) is a special member of the more flexible predictive logistic model (PLM) family. The selection of a set of metrics becomes equivalent to variable selection in statistical model building. We apply model building techniques as best subsets regression to facilitate the search for an optimal suite of metrics from a candidate set and use cross-validation to avoid overfitting. The results show that a few metrics suffice to discriminate between most-impacted and least-impacted sites.     

采样点数目对反距离加权插值结果的敏感性分析

为了研究采样点数日对反距离加权插值结果的影响,以面积为2．076×10^5km。的自俄罗斯为研究区域,采用数目逐渐变化的采样点作为实验数据,通过交叉验证方法,得到了采样点数目与插值精度之间的关系。结果表明：随着采样点数日减少,插值误差呈现先缓慢上升后迅速上升再缓慢上升的趋势。就本组数据而言,当采样点逐渐减少到约为总采样点数目的70％左右,插值的质量会发生突变。计算采样点的密度,得出每100km。采0．9-1．3个点（即每76-109km2采集1个点）的情况下,捕值精度变化不大。鉴于研究所得,在实际的生产生活中．可供大区域插值时。采样方案设计参考。     

基于交叉证认的EMD滤波及其在GPS多路径效应中的应用

利用交叉证认的方法,提出一种新的基于EMD滤波去噪方法,并将其应用于GPS多路径效应的研究中。通过模拟实验及实测数据分析表明,该方法能够自适应地选择IMF中的信号层数,削弱随机噪声,合理地分离信号和噪声。利用该滤波方法去噪并建立具有重复性的多路径误差效应改正模型,可有效地削弱多路径效应的影响,进而提高GPS动态变形监测的精度。     

霍西煤矿区地表滑坡灾害敏感性数值建模与等级划分

本文以山西省霍西煤矿区为研究区,利用遥感和GIS方法对滑坡灾害的敏感性进行了数值建模与定量评价。利用交叉检验方法构建了径向基核函数支持向量机滑坡敏感性评价模型,并基于拟合精度对模型进行了定量评价;对各评价因子在模型中的重要性进行对比分析;基于空间分辨率为30m的评价因子,通过径向基核函数支持向量机模型获得了霍西煤矿区滑坡敏感性指数值,并利用分位数法将霍西煤矿区的滑坡敏感性分为极高、高、中和低4个等级。结果表明：拟合精度建模阶段和验证阶段分别为87.22%和70.12%;与滑坡敏感性关系最密切的5个评价因子依次是岩性、距道路距离、坡向、高程和土地利用类型;极高和高敏感区域分布了93.49%的滑坡点,面积占总面积的50.99%,是比较合理的分级方案。本研究不仅可以为研究区人工边坡调查和煤矿资源合理开采提供借鉴,对相似矿区的相关工作也具有参考价值。     

基于空间自相关和概率论的土壤重金属异常值的识别方法

数据是开展土壤环境质量研究的基础,在实验过程中,由于系统误差和人为误差导致数据异常、降低数据质量,进而对污染评价、修复与管理决策等后续工作带来误判。目前对于此方面缺乏深入的研究和探讨。基于此,本研究提出一种甄别土壤重金属异常数据的方法,并以北京市土壤Cd含量数据为例,对该方法的有效性进行了验证。结果显示,北京市651个土壤Cd数据中有34个数据异常,对甄别出的异常数据进行化学复测,发现76.5%的Cd异常数据（26个）为系统误差和人为误差导致;20.6%的异常数据（7个）为客观存在的异常点。将原始数据修正后,插值预测精度得到了显著提高。Cd异常数据自身的平均相对误差下降44.56%,均方根误差降低33.33%;受异常值影响的邻近点平均相对误差下降20.59%,均方根误差降低17.33%。结果表明本方法可以有效识别出土壤重金属数据中的异常数据,在增加有限样本量和分析时间的前提下提高调查数据质量,为开展区域土壤调查,保障数据质量提供有效的工具。     

Application of universal kriging for estimation of earthquake ground motion: Statistical significance of results

Universal kriging is compared with ordinary kriging for estimation of earthquake ground motion. Ordinary kriging is based on a stationary random function model; universal kriging is based on a nonstationary random function model representing first-order drift. Accuracy of universal kriging is compared with that for ordinary kriging; cross-validation is used as the basis for comparison. Hypothesis testing on these results shows that accuracy obtained using universal kriging is not significantly different from accuracy obtained using ordinary kriging. Tests based on normal distribution assumptions are applied to errors measured in the cross-validation procedure;t andF tests reveal no evidence to suggest universal and ordinary kriging are different for estimation of earthquake ground motion. Nonparametric hypothesis tests applied to these errors and jackknife statistics yield the same conclusion: universal and ordinary kriging are not significantly different for this application as determined by a cross-validation procedure. These results are based on application to four independent data sets (four different seismic events).     

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.     

Test of high sampling density OSL dating of aeolian samples from the south margin of the Tengger Desert using the global standardised growth curve (gSGC) method

Chronostratigraphic records in the drylands of north China provide basic archives to reveal the dynamic connections between climate changes and the behaviour of aeolian systems. However, the interpretation of aeolian chronostratigraphy is not straightforward and may be associated with significant uncertainty due to a number of external and localised forces. Taking into account this complexity of aeolian systems, excluding preservation and sampling bias from palaeoclimatic signals requires that the interpretation of chronostratigraphic records be based on as many dates from as many sites as possible. Optically stimulated luminescence (OSL) dating provides a direct way to date aeolian sediments. However, the determination of equivalent dose (D_e) using the commonly applied single-aliquot regenerative dose (SAR) protocol is relatively time-consuming for samples with burial doses exceeding 50 or 100 Gy.In this study, the global standardised growth curve (gSGC) method is applied to multi-grain aliquots of coarse quartz from various sites covering a wide region of the south margin of the Tengger Desert in north China to rapidly determine D_e for a large number of aliquots and samples. The large between-aliquot variability in the shape of the dose response curve (DRC) is significantly reduced using a least-squares normalisation procedure. The results of a leave-one-out cross-validation (LOOCV) protocol demonstrates that D_e values determined using the gSGC and SAR methods are very consistent with each other up to at least 200 Gy and 400 Gy, for the 90–150 μm and 63–90 μm fractions, respectively. It suggests that the gSGC protocol can be used as an efficient procedure for D_e determination of a large number of aeolian samples from multiple sites. This in turn provides a better constraint for the interpretation of Late Quaternary aeolian chronostratigraphic records in drylands of north China.