均衡法评价地下水可开采量的风险率

有限数据样本量所关联的不确定性因素,对水资源评价结果的可靠度有着显著影响。在地下水源地勘查与运行过程中,地下水位变幅是一个具明显缓变性质的随机变量。以山东省济宁市承压水源地为例,利用水均衡法评价方法,基于模糊-随机模型,定量研究缓变随机变量在先验分布与后验分布条件下对评价结果的风险影响。结果表明:受不确定性因素时变特性的影响,水源地按勘查成果形成的计划开采量运行,将导致水源地形成过量开采的风险率由13.67%增加至15.75%;考虑不确定性因素时变特性影响,随机变量的不确定性区间由5.64减小到3.60;利用后验信息及时调整开采计划,可保障水源地的供水安全。     

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR,hyperspectral imagery,and large inventory datasets

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.     

康普顿散射成像图像重建算法综述

康普顿散射成像技术因其灵活的系统结构、较低的辐射剂量而广泛应用于传统透射成像技术无法应用的场合,受到研究者的关注。图像重建算法是康普顿散射成像技术的核心,直接决定着重建图像的质量。本文从康普顿散射成像的正向模型讲起,分别对解析和迭代两种重建算法进行了介绍,着重对解析重建算法中的圆弧Radon变换模型和迭代算法中正则化过程中引入的全变分最小化方法进行了阐述和分析。最后,对康普顿散射成像重建算法存在的问题及发展趋势进行了总结。     

Geotechnical parameter estimation in tunnelling using relative convergence measurement

Accurate estimation of geotechnical parameters is an important and difficult task in tunnel design and construction. Optimum evaluation of the geotechnical parameters have been carried out by the back‐analysis method based on estimated absolute convergence data. In this study, a back‐analysis technique using measured relative convergence in tunnelling is proposed. The extended Bayesian method (EBM), which combines the prior information with the field measurement data, is adopted and combined with the 3‐dimensional finite element analysis to predict ground motion. By directly using the relative convergence as observation data in the EBM, we can exclude errors that arise in the estimation of absolute displacement from measured convergence, and can evaluate the geotechnical parameters with sufficient reliability. The proposed back‐analysis technique is applied and validated by using the measured data from two tunnel sites in Korea. Copyright © 2005 John Wiley & Sons, Ltd.     

Direct back analysis by the meshless local Petrov‐Galerkin method and Bayesian statistics

This study presents a preliminary development of a direct back analysis procedure by the meshless local Petrov–Galerkin (MLPG) method and Bayesian statistics and the application of resulting procedure to characterize soil properties using laboratory results. As compared to direct back analyses that are based on the finite element method (FEM), it is intended to show that MLPG‐based direct back analyses may be more suitable for some kinds of characterization problems; for example, involving a complex subsurface stratification or the characterization of soil properties of just an inclusion of a soil profile. The existing MLPG method is first slightly modified to analyse time‐dependent problems. Using the resulting method, quantities to be characterized are evaluated so that they give numerical results as close to measured data as possible. The Akaike information criterion is introduced for simplifying the evaluation. A one‐dimensional finite strain consolidation problem is introduced to do an error analysis for prediction by the proposed MLPG method. Another example illustrates experiences of performing an MLPG‐based direct back analysis. Comparison of MLPG‐based and FEM‐based direct back analyses is taken. Copyright © 2006 John Wiley & Sons, Ltd.     

A discrete Bayesian network to investigate suspended sediment concentrations in an Alpine proglacial zone

In this study, a Bayesian Network (BN) is used to model the suspended sediment concentrations (SSC) in the catchments of the glaciers Noir and Blanc in the Ecrins National Park, France, and at the distal end of the proglacial zone into which both torrents drain. Relationships between air temperature, glacier discharge and SSC are represented as random variables; thereby taking the natural next step from proposed modified rating curve methods which increasingly approximate random variable approaches. Hydrological relationships are propagated through the network via conditional probability distributions computed from 980 field records obtained at three monitoring sites during July 2005. Rainfall affected data are removed from the modelling process. A two‐sample Kolmogorov–Smirnov goodness‐of‐fit (two‐sample KS) test (n = 5) shows good agreement between the probability distributions of SSC predicted by the BN, and those recorded in the field at the outflow of the proglacial zone over an air temperature range of 5–25 °C. The BN performs poorly for air temperatures between 25 and 30 °C and this is attributed to limited field records covering this temperature range. Discussion of the significant limitations surrounding the widespread application of BNs in hydrological modelling are offered with a focus on data volume and temporal limitations. Copyright © 2008 John Wiley & Sons, Ltd.     

Minimum principle and related numerical scheme for simulating initial flow and subsequent propagation of liquefied ground

The problem of predicting the evolution of liquefied ground, modelled as a viscoplastic material, is addressed by combining a minimum principle for the velocity field, which characterizes such an evolution, and a time step integration procedure. Two different numerical schemes are then presented for the finite element implementation of this minimum principle, namely, the regularization technique and the decomposition‐co‐ordination method by augmented Lagrangian. The second method, which proves more accurate and efficient than the first, is finally applied to simulate the incipient flow failure and subsequent spreading of a liquefied soil embankment subject to gravity. The strong influence of liquefied soil residual shear strength on reducing the maximum amplitude of the ground displacement is particularly emphasized in such an analysis. Copyright © 2005 John Wiley & Sons, Ltd.     

湖南古丈山体滑坡影响因子分析

为研究湖南省古丈县2016年7月17日群体滑坡的地质环境与降水诱发成因,基于GIS空间分析平台,利用古丈县1:5万地质灾害详查资料、地形地貌、公路河网、坡度坡向、地下水类型、滑坡体与岩土产状关系等空间数据,分析古丈县多种地质环境因子与滑坡事件的影响系数;采用贝叶斯后验概率分析法确定古丈滑坡降水阈值曲线(I-D曲线);基于多要素地质环境因子、临界降水条件及滑坡触发有效雨量综合分析了本次群体滑坡事件的主要成因。结果表明:出现大于当地滑坡阈值的极端降水事件(一定的滑坡降水强度、持续时间和滑坡触发有效雨量)是诱发古丈群体滑坡的主要外源动力,脆弱的岩土结构、滑坡体与岩层产状一致的滑坡类型、公路(铁路)和河网修建对山体切坡的影响、敏感的坡度区间、高风险的坡向区间等地质因子是导致古丈默戎"7·17"滑坡的地质成因,相关研究方法和结论对该地区未来地质灾害预警预报提供了方法和数据支持。     

结合运动平滑约束与灰度特征的卫星视频点目标跟踪

针对卫星视频条件下的点目标跟踪问题,提出了一种运动平滑约束的贝叶斯分类目标跟踪方法(BMoST)。本方法引入朴素贝叶斯分类器的思想,不依赖目标的任何先验概率,在运动平滑性约束下,利用灰度相似性特征来表达描述目标的似然度,并根据独立假设的贝叶斯定理,建立简化的分类器条件概率修正模型,通过该模型估计目标的后验概率,从而实现目标跟踪。同时,采用卡尔曼滤波辅助、优化跟踪处理,提高算法的稳健性。试验数据采用SkySat和吉林一号拍摄的视频各两段,对6个点目标进行跟踪试验。结果表明,本文提出的方法针对卫星视频的点目标跟踪效果良好,精度达到90%左右,且跟踪轨迹平滑,满足卫星视频后续高级处理和应用需要。     

贝叶斯框架的Copula季节水文干旱预报模型构建及应用

根据季节径流量相关特性,利用标准径流指数(SRI),通过优选Copula函数和径流量分布函数,构建贝叶斯框架的Copula季节水文干旱预报模型,并对阿克苏河西大桥水文站进行实证分析。结果表明：① Gamma、Lognormal、Normal、Gumbel、Exponential 5种分布函数中,Gamma、Gumbel能较好拟合夏、秋季径流量;② Gumbel-Hougaard、Clayton、Frank 3种Copula函数中,Clayton能较好联结夏、秋季径流量分布函数;③ 构建模型预报表明,2001~2009年秋季发生干旱概率较低(24%~38%),以轻微、中度干旱为主,而2010年发生干旱的概率极高(95%),发生异常干旱的概率偏高(81%),与实际发生的干旱情况基本一致;④ 贝叶斯框架下构建的Copula模型能准确预报季节水文干旱发生,减少预报的不确定性,为特定区域干旱预报提供了一条新的途径。