甘肃舟曲江顶崖滑坡成因分析与综合治理措施

大型土石混合体滑坡是一类受工程地质条件、降水或地震等多种因素影响的滑坡类型,研究其滑坡成因及治理方法对防灾减灾、保障人民生命财产安全具有重要意义。本文以舟曲县江顶崖滑坡为例,通过历史资料调研和现场地质勘察、数值模拟等手段,对该滑坡的成因和稳定性进行详细分析,并提出相应的综合治理措施。结果表明:该滑坡的地形地貌、松散的岩土体是引发滑坡的内在因素,而连续降雨以及江水对滑坡前缘冲刷是滑坡失稳的外在因素。在对滑坡成因分析的基础上,通过改进抗滑桩护壁结构、率先应用装配式框架支护,结合坡面裂缝整治工程等综合治理措施有效地控制住了该滑坡的变形。该滑坡的综合治理措施可为类似的高大边坡工程以及大型土石混合体滑坡治理提供参考。     

Optimal lake-marsh pattern determination in lake-marsh systems based on the eco-hydrological processes management

The major hydrological factors in lake-marsh systems are water level (depth), water surface area, and water volume. The key index for determining the lake-marsh pattern is water level, which leads to the variation of lake-marsh patterns under natural hydrological alternations. In addition, the vegetation structure also affects the lake-marsh pattern. With socioeconomic development and climate change, the ‘land use’ and ‘water use’ competitions appear more seriously between a lake-marsh system and its surrounding socioeconomic system, also inside the lake-marsh system. The possible optimal lake-marsh pattern could solve the contradictions mentioned above. As few studies focus on this issue, this study proposed an optimal lake-marsh pattern determination method with eco-hydrological management on relieving the land use and water use competitions. The optimal lake-marsh pattern determination method considers the protection objects (water depth demands), water supply (precipitation, surface water, and groundwater), and water demand (especially evapotranspiration) in the system at annual and monthly scales. Calculation and analysis were performed for the optimal pattern of the Wolonghu Wetlands as an example. The results mainly showed that the lake-marsh pattern of the Wolonghu Wetlands cannot be achieved on meeting both the maximum ecological services values and minimum water shortage amount under present natural condition. With artificial regulation measures, the possible optimal annual lake-marsh patterns can be obtained based on both ecological and hydrological objectives, with the area ratio of lake and marsh in the range of 0.650:0.350 and 0.726:0.274, the corresponding water level of lake body was of 86.85 to 87.0 m. This study could provide references for the Wolonghu Wetlands management, also for similar lake-marsh systems and other ecological systems.     

一种灰度体素结构分割模型下的机载LiDAR 3D滤波算法

针对现有的基于机载LiDAR数据的滤波算法未能充分利用数据提供的所有信息及其所采用的数据结构表达复杂、存在信息损失等缺陷,提出了一种灰度体素结构分割模型下的机载LiDAR 3D滤波算法。算法首先以综合利用机载LiDAR数据的高程及强度信息为目的将点云数据规则化为灰度（体素内激光点的平均强度的离散化表示）体素结构,然后基于各体素间的空间连通性和灰度相似性准则,将灰度体素结构分割并标记为若干个3D连通区域,最后依据地面与其它目标的高差特性提取与其对应的3D连通区域。算法优势在于:基于体素结构设计,为3D滤波算法;综合利用了地面目标的几何及辐射特征,对比传统滤波算法可应用于更复杂的场景;滤波结果为3D地面体素形式,可直接用于创建地面3D模型。实验采用国际摄影测量与遥感协会（International Society for Photogrammetry and Remote Sensing, ISPRS）提供的不同密度的机载LiDAR基准测试数据测试了邻域尺度参数的敏感性及提出的算法的有效性,并和其他经典滤波算法做对比。定量评价的结果表明,51邻域为最佳空间邻域尺度;点云密度为0.67点/m²的数据集1的滤波平均完整率、正确率及质量分别为0.9611、0.9248及0.8934;点云密度为4点/m²的数据集2的滤波平均完整率、正确率及质量分别为0.8490、0.8531及0.7404;对比其全经典滤波算法本文算法在高密度点云数据滤波时表现更佳。     

Laboratory Granular Flows generated by Slope Failures

Summary. A series of flume experiments has been set up to analyze the behavior of water sand mixture flows, as a consequence of slope failures induced by water table raising and rainfall. The flows initially accelerated but at a certain stage of the process, unsteady deposition of the sand occurred, preceded by the transformation of the movement from flow to sliding. The comparison of such phenomena with other flume experiments, performed in similar conditions, showed that differences in flow behavior are dependent on a variety of aspects that are difficult to take into account. A numerical modeling of laboratory flows has been performed, using a cellular automata model based on the grain inertia regime. Despite the relatively good agreement between modeled and experimental phenomena, the study shows the difficulties of taking the actual behavior of granular flows into account. Author’s address: Chiara Deangeli, Politecnico di Torino, Ditag, corso Duca degli Abruzzi 24, 10129 Torino, Italy     

Shear strength characteristics of Irbid clayey soil mixed with iron filling and iron filling–cement mixture

Iron filling and iron filling–cement mixture were used to improve the shear strength characteristics of Irbid clayey soil. For this purpose, five types of Irbid clay soils were obtained and mixed with iron filling and iron filling–cement mixture at different percentages. Two sets of prepared samples were mixed with the admixture. The first set was prepared by mixing the soil samples with iron filling alone at 2.5, 5.0, 7.5, and 10% by dry weight of the soil. The second set was prepared by mixing with iron filling–cement mixture at equal ratio of the same percentages of the first set. An unconfined compression test was performed in this study to measure the shear strength properties of the soils. The test results showed that the increase in the percentages of the iron filling and iron filling–cement mixture up to 10% will result in increasing the maximum dry density of the soil and increase the unconfined compressive strength and the secant of modulus of elasticity of the clayey soil. Also, the addition of iron filling–cement mixture increased the unconfined compressive strength and secant modulus of elasticity of the clayey soil higher than the addition of iron filling alone.     

The derivation of an Arctic sea ice normal through temporal mixture analysis of satellite imagery

The use of temporal mixture analysis (TMA) for creating a long-term baseline, or environmental “normal” is described. TMA is a promising analysis method derived from the hyperspectral image processing technique of spectral mixture analysis (SMA). TMA is algebraically identical to SMA, except that it is applied to temporal spectra rather than to electromagnetic spectra. TMA has particular potential to extract climate signals from long image sequences.     

An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing

Land use and land cover classification is an important application of remote-sensing images. The performances of most classification models are largely limited by the incompleteness of the calibration set and the complexity of spectral features. It is difficult for models to realize continuous learning when the study area is transferred or enlarged. This paper proposed an adaptive unimodal subclass decomposition (AUSD) learning system, which comprises two-level iterative learning controls: The inner loop separates each class into several unimodal Gaussian subclasses; the outer loop utilizes transfer learning to extend the model to adapt to supplementary calibration set collected from enlarged study areas. The proposed model can be efficiently adjusted according to the variability of spectral signatures caused by the increasingly high-resolution imagery. The classification result can be obtained using the Gaussian mixture model by Bayesian decision theory. This AUSD learning system was validated using simulated data with the Gaussian distribution and multi-area SPOT-5 high-resolution images with 2.5-m resolution. The experimental results on numerical data demonstrated the ability of continuous learning. The proposed method achieved an overall accuracy of over 90% in all the experiments, validating the effectiveness as well as its superiority over several widely used classification methods.     

A model for dilatation, densification, and static liquefaction of loose sands

Utilizing basic balance laws of saturated granular media, a theoretical model for describing the phenomena of dilatation and densification of dry sand, as well as the increase of pore water pressure of saturated sand during shear loading, is developed. An expression for the free-energy function of loose sands is postulated, and the problem of static shearing of a sand sample is analyzed. Predictions of the model for pore water pressure are compared with those obtained experimentally for dry pluviated and moist vibrated samples of fine Ottawa sand and solid-glass beads and discussed.     

Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model

Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples.     

线性/非线性光谱混合模型估算白刺灌丛植被覆盖度

及时监测干旱与半干旱区光合/非光合植被覆盖度时空变化,可以为指导荒漠化防治工程及植被衰退机制研究提供重要信息。本文以甘肃民勤典型植被白刺灌丛为研究对象,通过地面控制性光谱实验获取混合光谱、端元光谱与丰度信息,开展线性与非线性光谱混合模型(包括核函数非线性和双线性混合模型)估算光合和非光合植被覆盖度的对比研究,采用全限制最小二乘法进行模型解混,分别获取各样本数据中各类端元丰度及其精度信息,通过模型分解的均方根误差(RMSE)与地面验证精度确定用于光合和非光合植被覆盖度估算的最佳光谱混合模型,其中参考端元丰度采用神经网络(NNC)分类算法对数字影像进行分类获取。结果表明:(1)引入阴影端元的四端元模型相对于传统的三端元模型(光合/非光合植被与裸土)能有效提高光谱解混的精度,并提高光合和非光合植被覆盖度估算精度;(2)对白刺灌丛来说,光合植被、非光合植被、裸土及阴影间多重散射混合效应存在,但混合效应不够显著;考虑非线性参数的核函数非线性光谱混合模型表现略低于线性光谱混合模型,因此非线性光谱混合模型在估算白刺灌丛光合和非光合植被覆盖度时相对于线性光谱混合模型没有明显优势;(3)基于光合/非光合植被、裸土与阴影四端元的线性光谱混合模型可以实现白刺灌丛光合和非光合植被覆盖度的准确估算,光合植被覆盖度估算RMSE为0.11 77,非光合植被覆盖度估算RMSE为0.0835。