A fundamental solution of a multilayered half-space due to an impulsive ring source

The fundamental solutions of axisymmetric elastodynamic problem for the multilayered half-space due to an impulsive ring source acting within a layered elastic media are derived in time domain with the aid of Laplace–Hankel mixed transform and transfer matrix techniques. In addition, an effective numerical procedure, which utilizes the fast Hankel transform algorithm, is also proposed to calculate these solutions. Illustrative examples have been given to demonstrate that the fundamental solutions can be readily evaluated and the numerical results are of high accuracy. The present solutions can be directly applied to determine the transient wave fields caused by a seismic source and show the potential application to the elastodynamic problems solved by the boundary element method.     

人工神经网络遥感影像分类模型及其与知识集成方法研究

以多层感知器（MLP）为例，探讨了地学知识与ANN融合进行遥感影像分类的方法。首先对MLP网络结构、学习算法及其改进进行分析；然后总结了MLP进行遥感影像分类的一般方法和存在的缺陷；发展了基于知识的MLP神经网络遥感影像分类模型，并具体利用基于规则的MLP方法进行了遥感土地覆盖分类的实验，把获得的结果与传统统计方法与一般ANN方法进行了综合比较，获得了有意义的结果。     

Modelling an inland Delta aquifer system to evolve pre-development management schemes: a case study in Upper Thamalakane River valley, Botswana, southern Africa

 Groundwater modelling studies have been found to be a potential tool in planning the pre-development management of groundwater resources in newly developing aquifer systems. One such study was attempted in Upper Thamalakane River valley, Okavango Delta, Botswana (southern Africa). There are three major aquifers separated by two aquitards in the valley portion. The top two aquifers are freshwater bearing zones and the bottom one is saline. The hydrological set-up of the basin is complex, as the groundwater flow directions are opposite in the upper-unconfined and in the lower-confined aquifers. A preliminary multilayer model was developed for this aquifer system by making use of only available data. The hydrodynamic behavior was then studied under two prediction scenarios to evolve appropriate management decisions for locating the well field (large diameter wells) in the upper aquifer by making use of induced river infiltration during the flood season. The aquifer response for variable river-flow conditions was studied and the induced river infiltration was quantified. Received: 27 August 1998 · Accepted: 8 March 1999     

潜北断层下降盘潭口盐泥隆起塑性变形机制及油气地质意义

江汉盆地潜江凹陷潜北断层下降盘潭口凸起的成因是该区多年关注的问题之一。通过地震剖面解析,认为是由渔洋组—新沟嘴组和荆沙组—潜江组两套多层盐泥侵—底辟刺穿形成的大型盐泥隆起,主要形成于荆河镇组沉积之后,将完整的潜江凹陷分割成3个次洼;盐泥隆的成因是在差异负载和重力滑脱动力作用下潜北断裂下降盘塑性侧向上侵推挤,与上升盘重力滑脱推覆造成南北双向挤压上拱, 形成了以汉水断层面为底板、中部为盐泥隆核、上层盐泥上部膝折—断褶穹窿3层结构;强烈塑性变形使潭口地区潜北断层面中部塑化—揉皱消失, 上部强拱左旋翻转成平卧断层;各个构造层盐泥塑性变形程度和形态差异与构造层盐泥含量、盐泥比有关;盐泥隆是形成各种油藏类型的关键因素,潜三段、潜四上段、新沟嘴组中被盐泥隆和刺穿作用形成的地层、岩性油藏是该区具有勘探潜力的油藏类型,且被近期钻探证实。     

Artificial Neural Networks in Proglacial Discharge Simulation: Application and Efficiency Analysis in Comparison to the Multivariate Regression; A Case Study of Waldemar River (Svalbard)

Artificial neural networks were applied to simulate runoff from the glacierized part of the Waldemar River catchment (Svalbard) based on hydrometeorological data collected in the summer seasons of 2010, 2011 and 2012. Continuous discharge monitoring was performed at about 1 km from the glacier snout, in the place where the river leaves the marginal zone. Averaged daily values of discharge and selected meteorological variables in a number of combinations were used to create several models based on the feed‐forward multilayer perceptron architecture. Due to specific conditions of melt water storing and releasing, two groups of models were established: the first is based on meteorological inputs only, while second includes the preceding day's mean discharge. Analysis of the multilayer perceptron simulation performance was done in comparison to the other black‐box model type, a multivariate regression method based on the following efficiency criteria: coefficient of determination (R²) and its adjusted form (adj. R²), weighted coefficient of determination (wR²), Nash–Sutcliffe coefficient of efficiency, mean absolute error, and error analysis. Moreover, the predictors' importance analysis for both multilayer perceptron and multivariate regression models was done. The performed study showed that the nonlinear estimation realized by the multilayer perceptron gives more accurate results than the multivariate regression approach in both groups of models.     

A numerical method for the two layer shallow water equations with dry states

A numerical method is proposed for solving the two layer shallow water equations with variable bathymetry in one dimension based on high-resolution f-wave-propagation finite volume methods. The method splits the jump in the fluxes and source terms into waves propagating away from each grid cell interface. It addresses the required determination of the system’s eigenstructure and a scheme for evaluating the flux and source terms. It also handles dry states in the system where the bottom layer depth becomes zero, utilizing existing methods for the single layer solution and handling single layer dry states that can exist independently. Sample results are shown illustrating the method and its handling of dry states including an idealized ocean setting.     

Using ANNs to analyse effects of the Three Gorges Dam on sedimentation in Dongting Lake,China

Since a river system is a unidirectional network, the upstream influencing factors often interfere with those downstream. We quantify the effects of the TGD on the sediment exchange processes between the Yangtze River and Dongting Lake. Based on yearly sediment load data from 1981 to 2008, two multi-layer perceptron (MLP) models were constructed to predict the potential sedimentation in Dongting Lake without implementation of the TGD. The sediment discharge at Yichang station decreased from 622.5 to 61.1 Mt/year between 1981–1985 and 2003–2008, while the sedimentation in Dongting Lake reduced from 127.3 to 6.6 Mt/year. The MLP models indicate that only 27.9% of the decreased sediment load at Yichang station and 16.9% of that in Dongting Lake is caused by the TGD, while the rest is caused mostly by other upstream climate variations and anthropogenic impacts.

EDITOR A. Castellarin; ASSOCIATE EDITORN. Ilich     

A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall—runoff models

Abstract

The performances of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are: the simple neural network (SNN), the radial basis function neural network (RBFNN) and the multi-layer perceptron neural network (MLPNN). Daily data of eight catchments, located in different parts of the world, and having different hydrological and climatic conditions, are used to enable comparisons of the performances of these three methods to be made. In the case of each catchment, each neural network combination method synchronously uses the simulated river flows of four rainfall—runoff models operating in design non-updating mode to produce the combined river flows. Two of these four models are black-box, the other two being conceptual models. The results of the study show that the performances of all three combination methods are, on average, better than that of the best individual rainfall—runoff model utilized in the combination, i.e. that the combination concept works. In terms of the Nash-Sutcliffe model efficiency index, the MLPNN combination method generally performs better than the other two combination methods tested. For most of the catchments, the differences in the efficiency index values of the SNN and the RBFNN combination methods are not significant but, on average, the SNN form performs marginally better than the more complex RBFNN alternative. Based on the results obtained for the three NN combination methods, the use of the multi-layer perceptron neural network (MLPNN) is recommended as the appropriate NN form for use in the context of combining simulated river flows.     

双叶模型在冬小麦田冠层CO2通量多层模拟中的应用

综合考虑农田生态系统中水、热、CO2输送所涉及的大气、水文、生物等生物物理过程,以Farquhar等提出的叶片尺度光合作用生物化学过程机理模型为理论基础,对其进行空间尺度扩展,并改进冠层分层方法,建立了均匀农田与大气之间物质输送和能量交换的多层模式,在模式中运用双叶模型,同时考虑叶片氮素水平垂直差异,对2008年4—5月华北平原冬小麦生长旺季农田生态系统中冠层CO2通量进行了模拟研究,并利用涡度相关观测的通量数据对模型的有效性加以验证,结果表明:在冠层多层空间,小麦拔节至孕穗期和开花至乳熟期叶片氮含量随冠层高度的衰减系数分别为0.793(R2=0.698)和1.374(R2=0.728),冠层内叶片氮含量的空间分布可以用以相对累积叶面积指数为自变量的函数来描述;模型分别计算各层阴、阳叶的光截取、气孔传导、光合作用等,最终计算冠层上方CO2通量,冬小麦农田净生态系统生产力模拟值与实测值相关显著(R2=0.78),模拟的CO2通量日变化特征晴天昼间比阴雨天和夜间的效果好;在考虑丛聚影响的叶片非随机分布的密集农田中,阴叶对总初始生产力的贡献率在35.7%左右,对生产力贡献很重要。分层统计显示,作物最终产量的形成主要...     

独库高速公路克扎依—巩乃斯段雪崩易发性评价

独库高速公路克扎依—巩乃斯段以高山地貌为主,地形切割剧烈,为雪崩发育提供了有利的地形条件,对该区域进行雪崩易发性评价是独库高速公路安全建设及运行的重要前提。通过遥感解译和现场调查等手段获取149个雪崩点的因子数据,通过对因子进行相关性检测,筛选出10个评价因子,构成雪崩评价因子体系。在此基础上,运用K均值聚类法和随机法提取出非雪崩点和原始雪崩点构成样本集,通过机器学习中的多层感知器、支持向量机算法对研究区域开展雪崩易发性评价。研究结果表明,随机法和K均值聚类法提取出的样本集分别带入算法中训练,R-SVM、R-MLP、K-SVM、K-MLP四种模型的Kappa系数均大于0.6,4组模型对验证数据集的预测结果与实际值存在高度的一致性。经多层感知器训练的AUC值由0.762提高至0.983,经支持向量机训练的AUC值由0.724提高至0.951。基于本研究预测性能最佳的K-MLP模型分区显示该研究区雪崩发育对拟建线路影响较小,但对于隧道洞口可能会造成威胁。本研究可为独库高速公路建设、运营以及雪崩灾害防治工作提供理论支撑和方法参考。