监测网的可区分测度

监测网的设计阶段，变形体的变形特性不是完全清楚的情况下，须考虑监测网的可区分测度．本文详细讨论了监测网的可区分问题，导出了有关的公式，并以算例加以分析和说明，最后论述了监测网设计的可区分准则．     

黄土孔隙性分类判别的人工神经网络方法

运用人工神经网络的一典型模型——“反向传播”神经网络,对洛川黄土孔隙性的实测数据进行了分析,建立了洛川黄土孔隙性预测的计算机智能专家系统。结果表明,神经网络方法性能良好,可望成为黄土孔隙性分类、判别的有效辅助手段。     

Topological properties of disjoint channel networks within enclosed regions

Enclosure of some portion of one or more natural stream-drainage basins by superposition of a rectangle on a map of drainage network results in fragmentation of the natural basins into a set of disjoint channel networks. Each of these may have some channel links and forks of the natural network plus truncated links intersected by the enclosure boundary. The topological properties of the network elements in the enclosure are used to set up a model of random network patterns, in which the number of disjoint channel networks is expressed as a function of the number of links and forks in the enclosures. This function is shown to be a multiplicative constant times the square root of the number of links or forks. Empirical data on square and rectangular enclosures of several sizes from the Inez (Kentucky)Quadrangle map showed that the predicted multiplicative constants do not agree with observation, but that the square-root relation seems to hold at least to a first approximation. The models thus can be used as a norm against which deviations of real-world enclosures from network pattern randomness can be studied.     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.     

ANN技术在岩溶地下水可开采量评价中的应用——以山西晋祠泉域为例

以晋祠泉域为例,分析该泉域水文地质特征。应用神经网络技术(ANN)建立泉域内县代表性的难老泉岩溶地下水位与各种补排项之间定量数学模型,对该泉域地下水可开采量进行了评价。研究结果表明,所建立的岩溶地下水位多因素神经网络模型具有较好的拟合精度,仿真程度较高,所得到的地下水可采资源量评价结果与该地区地下水开发利用实际情况较为一致。同时,还计算了不同降雨条件下地下水的可开采量,使其对地下水的开采规划更具有指导意义。     

GPS测量在巴郎沟水电站施工控制网中的应用

介绍了巴郎沟水电站GPS施工控制网的布设、基线处理及平差方法,分析了GPS施工控制网精度及基线粗差,实现了在隐蔽地区较高精度GPS施工测量的应用.     

Neo-liberal ruptures: Local political entities and neighbourhood networks in El Alto, Bolivia

In October 2003, hundreds of thousands of Bolivians took to the streets demanding the resignation of President Gonzalo Sánchez de Lozada. After 20 years of neo-liberal policies - and the failures to improve the living conditions of the majority - the proposal to export natural gas via Chile was taken by the population as yet another step to sustain an unjust political order. Facing a direct challenge by the population the Sánchez de Lozada administration responded with indiscriminate military force. The result was 63 dead and over 300 wounded, which deepened and extended the social rage and eventually forced the resignation of the President. The neo-liberal project - promoted and defended by Sánchez de Lozada - collapsed. The city of El Alto was the epicentre of the challenges to the legitimacy of this political order. This article focuses on the role of local political entities and neighbourhood networks from El Alto in articulating political spaces that challenged the legitimacy of the institutional infrastructure and led to the October 2003 ruptures in the neo-liberal project. Furthermore, I make the case that the particular histories and memories (of “relocalized” miners and indigenous/peasants) that converged in and defined this city were pivotal in the organization of a “political subsoil” that surged to the surface during the October 2003 events.     

Tectonic implications of damage zone-related fault-fracture networks revealed in drill core through the Nojima fault, Japan

1800 m of drill core through the Nojima fault zone, Japan, reveals subsidiary fault and fracture networks that developed in the fault zone that triggered the 1995 Ms 7.2 Kobe earthquake. The subsidiary fault zones contain a fault gouge of < 1 cm bounded by thin zones of foliated cataclasite or breccia. Fractures are filled with calcite veins, calcite-cemented breccias, clay, and iron-oxide and carbonate alternation of the granitic host rock. These features are typical of extensional fractures that form the conduit network for fluid flux close to a major fault zone. The zone of distributed deformation surrounding the main fault is 50 m in width, and the dip of the Nojima fault at > 1 km depth is 75°. The fault-fracture networks associated with the Nojima fault zone are coseismic and were filled with carbonate and fine-grained material during repeated seismic-related infiltration of the fault zone by carbonate-bearing subsurface water. This study shows that fault-related fracture networks plays an important role as fluid flow conduits within seismically active faults, and can change in character from zones of high permeability to low permeability due to cementation and/or pore collapse.     

基于改进SOFM的矢量量化图像压缩

在介绍矢量量化和自组织特征映射神经网络的基础上，针对基于自组织特征映射神经网络的矢量量化算法，在初始码书生成、获胜神经元搜索策略以及调整获胜码字及其拓扑领域权值等方面进行改进。实验结果表明改进算法具有合理性和有效性。     

Texture classification of Mediterranean land cover

Maximum likelihood (ML) and artificial neural network (ANN) classifiers were applied to three Landsat Thematic Mapper (TM) image sub-scenes (termed urban, agricultural and semi-natural) of Cukurova, Turkey. Inputs to the classifications comprised (i) spectral data and (ii) spectral data in combination with texture measures derived on a per-pixel basis. The texture measures used were: the standard deviation and variance and statistics derived from the co-occurrence matrix and the variogram. The addition of texture measures increased classification accuracy for the urban sub-scene but decreased classification accuracy for agricultural and semi-natural sub-scenes. Classification accuracy was dependent on the nature of the spatial variation in the image sub-scene and, in particular, the relation between the frequency of spatial variation and the spatial resolution of the imagery. For Mediterranean land, texture classification applied to Landsat TM imagery may be appropriate for the classification of urban areas only.