Conditional Simulation of Random Fields with Bivariate Gamma Isofactorial Distributions

This work focuses on a random function model with gamma marginal and bivariate isofactorial distributions, which has been applied in mining geostatistics for estimating recoverable reserves by disjunctive kriging. The objective is to widen its use to conditional simulation and further its application to the modeling of continuous attributes in geosciences. First, the main properties of the bivariate gamma isofactorial distributions are analyzed, with emphasis in the destructuring of the extreme values, the presence of a proportional effect (higher variability in high-valued areas), and the asymmetry in the spatial correlation of the indicator variables with respect to the median threshold. Then, we provide examples of stationary random functions with such bivariate distributions, for which the shape parameter of the marginal distribution is half an integer. These are defined as the sum of squared independent Gaussian random fields. An iterative algorithm based on the Gibbs sampler is proposed to perform the simulation conditional to a set of existing data. Such ‘multivariate chi-square’ model generalizes the well-known multigaussian model and is more flexible, since it allows defining a shape parameter which controls the asymmetry of the marginal and bivariate distributions.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

基于改进随机蕨算法的GIS增强现实应用研究

随机蕨算法由于其简单高效,实时性好的特点,在计算机视觉领域得到了广泛的应用。但是其在构造二值特征过程中采用的基于像素点灰度值比较的方法对图像变形的适应性较差。本文基于方向梯度直方图对光照变化,图像变形鲁棒性较好的特点,利用统计方向梯度直方图取代像素灰度值进行比较,以此构造特征点二值特征。基于改进的随机蕨算法,本文设计了一种将增强现实技术(AR)应用于GIS的框架模型。实验结果表明,经过改进的随机蕨算法对目标识别的正确率得到了有效提升,且算法的实时性较好,适用于GIS的增强现实应用。     

高建筑对周围建筑雷击保护距离的模拟

在已有先导连接参数化方案的基础上,选取近地面层为研究区域,保持方案中其他基本参量不变,通过改变闪电的空间形态,在同一建筑分布的背景下进行多次闪电模拟。研究多个建筑之间的屏蔽作用以及建筑雷击保护距离与建筑相关特征参数之间的关系,结果表明:高建筑对矮建筑具有屏蔽作用,并存在一个临界保护距离,当高、矮建筑高度分别为190 m和165 m,宽度均为20 m时,建筑之间的距离在12 m以内,矮建筑受高建筑完全保护不遭受雷击;建筑之间的距离超过12 m,矮建筑遭雷击次数明显增多。     

线性相关“可传递性”的几何意义

从几何角度对随机变量显著相关可传递性质进行了分析,得出了样本容量为n(n≥3)随机变量显著相关可传递性质的概率的几何表达式。用Monte-Carlo方法验证了几何分析的合理性,并对此问题展开了一定的讨论。     

改进支持向量机的高分遥感影像道路提取

针对支持向量机受分类数的限制在高分辨率遥感影像中无法直接获取高精度道路网信息的问题,该文提出一种新的混合的基于支持向量机的方法:首先,利用模糊C均值聚类方法将输入的遥感影像分为3类,以减少支持向量机的错分现象;其次,运用支持向量机将不同类别的像素分为道路类和非道路类;最后,应用马尔科夫随机场对分类结果进行噪声去除,并采用形态学进行后处理,进而得到精确道路网信息。实验结果表明:该算法不仅能够从高分辨率遥感影像中提取出道路网,而且精度优于直接使用支持向量机算法以及对比算法。     

基于随机森林模型的云南元阳梯田地形因子分析

为探究地形因子对梯田空间分布的影响,以云南元阳梯田坝达流域为研究区,运用GeoEye-1遥感卫星数据(2010年,1 m分辨率)和数字高程模型(20 m分辨率),提取梯田空间分布信息以及海拔高程、地形坡度、地形坡向、地面曲率、高程变异系数、地形起伏度、地面粗糙度、地表切割度、到水系距离9个地形因子。采用随机森林(Random Forest)方法建模,结合ROC曲线和AUC值对模型进行精度评价,根据随机置换残差均方减少量和因子作用曲线,探讨地形因子对梯田分布的影响规律。研究结果表明:该流域内梯田总面积1 158395 hm2,模型AUC值为0.947,海拔高度因子的随机置换残差均方减少量为38814,到水系距离因子为199.77,地面坡向因子为80.26,三者占总值的73.45%。由此可见,元阳梯田的空间分布主要受海拔高度、到水系距离、地面坡向3类地形因子的影响,其因子曲线表明梯田分布与地形因子间呈非线性关系。     

珠江流域降水集中度时空变化特征及成因分析

基于珠江流域内43个常规气象站点1960~2012年的逐日降水资料,计算了流域内各站点长期降水集中度(LCI)和逐年降水集中度(ACI);采用Mann-Kendall趋势检验法和Sen’s坡度检验法检测ACI时间上的变化趋势;同时采用反距离权重插值法(IDW)对LCI的区域特征和ACI的变化趋势进行空间插值以分析降水集中度的时空分布规律;采用随机森林(RF)算法对降水集中度的影响因子进行重要度分析。结果表明:(1)珠江流域逐年降水集中度ACI的年际变化不明显,东南部呈上升趋势,西北部呈下降趋势;(2)珠江流域西北部长期降水集中度LCI值偏小,即降水分布较平均;东南部长期降水集中度LCI值偏大,即降水较集中,表明该地区降水极值情况发生的几率相对较高,该空间分布趋势可能是受距离海洋的远近及海拔的影响;(3)众多气候影响因子中,东亚夏季季风(EASMI)对珠江流域的降水集中度影响最明显。     

Diagnosis of earth-fill dams by synthesised approach of sounding and surface wave method

The spatial distribution of the strength inside the earth-fill is identified by the sounding tests. In this research, the Swedish weight sounding (SWS) is employed, and the spatial high-density test is performed to identify the spatial correlation structure. Furthermore, the synthesised approach of the SWS and surface wave method, which is one of the geophysical method, is proposed to compensate the shortage of each approach. Consequently, the correlation structure of an earth-fill could be identified accurately, and the high resolution of the spatial distribution could be visualised based on the survey results.     

基于改进K-SVD字典学习方法的地震数据去噪

为实现更好的地震数据去噪技术,笔者引入一种新的算法:快速迭代收缩阀值法(FISTA),通过FISTA和K-奇异值分解(K-SVD)不断迭代更新K-SVD字典,利用更新得到的K-SVD字典对地震数据进行稀疏表示,去除稀疏系数中较小的数值,使数据中的随机噪声得到压制。对层状模型合成地震记录,Marmousi模型合成地震记录以及实际地震数据进行对比实验,得出FISTA算法较OMP算法能更好地提高地震数据的信噪比,同时有效地保护了反射信号。