三维地质体重构中空间数据插值方法的研究

空间插值方法的选择是地质三维可视化当中能真实、准确的再现工程研究对象的关键要素。为此，本文针对建立三维地质体的体数据模型插值算法的特点，对各种常见空间插值方法的特点进行了比较分析。最后结合某矿区的实测勘探数据，采用Kriging方法进行了三维地质体的重构，取得了与工程现场较为一致的效果，证明了该方法在地矿领域的适用性。     

基于Qt和OpenGL的三维地质建模

利用曲面插值方法将用户实际测绘出的地层离散控制点插值成光滑的曲面,进一步获得光滑连续的地质界面的三维地质模型,用于表达各种地质界面。首先介绍几种适合中、小规模数据的常用插值方法,并对比其插值效果,然后介绍基于层次I3样条插值方法,该方法可应用于大规模散乱数据插值中。软件基于Qt和OpenGL进行开发,已经在工程地质中得到很好的应用。     

Flow guided interpolation – A GIS-based method to represent contaminant concentration distributions in groundwater

This paper introduces a new interpolation method to estimate the spatial distribution of contaminant concentrations in groundwater. The method is intended to identify areas of risks in early investigation stages when groundwater sampling data is typically scarce and available interpolation methods fail to provide reasonable results. As a consequence, the method does not only incorporate available sampling data, but also makes use of information about the groundwater flow field, in order to “guide” the interpolation with e.g. ordinary kriging or inverse distance method. The guidance includes the augmentation of available data by auxiliary point data and the segmentation of the estimated plume area into a series of sectors. The method is evaluated for several settings and different sampling data sets. Each data set reflects a specific level of field investigations at the model site, an abandoned military base in Potsdam near Berlin, Germany. The results reveal that flow guidance improves the representation of contaminant distribution for all cases examined in this study compared to “unguided” interpolation. These findings are underpinned by the results of the method’s application to real sampling data. The method especially shows its strength when data of only a few sampling points are available.     

Interpolation methods comparison

In many fields, spatial interpolation is used to evaluate physical data in a continuous domain. The many different techniques offer different performances, according to the characteristics of initial data points. The aim of this paper is to provide help in choosing and evaluating the technique that best suits the data set: a few indices measure the quality of interpolation by different viewpoints. An extensive test-case is reported, involving four different interpolation methods. Data sets are generally connected with environmental topics or taken from literature.     

空间插值分析算法综述

空间插值分析算法是一种应用于将离散点的测量数据转换为连续数据表面的算法,能够将连续数据曲面与其他空间现象的分布情况进行比较,它在空间信息方面具有广泛的应用场景,尤其是地理信息方面.对泰森多边形法、反距离权重插值法、样条函数插值法、克里金插值法等空间插值算法的插值原理和应用场景进行综述,对空间插值分析算法的进展和未来研究方向进行了探讨.     

基于地统计方法的二氧化碳浓度空间插值研究

克里格法（Kriging）是地质统计学的主要内容之一,从统计意义上说,是从变量关系和变异性出发,在有限区域内对区域化变量的取值进行无偏、最优估计的一种方法：从插值角度讲是对空间分布的数据求线性最优、无偏内插估计的一种方法。克里格法的适用条件是区域化变量存在空间相关性。将临安市内的二氧化碳作为区域化变量．根据不同的半变异函数理论模型,采用普通Kriging法,通过对比分析得到：基于地统计的插值方法。根据半变异函数云图和试验方差最小的原理．选择合适的半变异函数理论模型进行变量的空间插值．能够较好地模拟区域化变量的空间连续分布格局．并取得较好效果。     

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding “patterns” by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods.     

Integrating geostatistics and remote sensing for mapping the spatial distribution of cattle hoofprints in relation to malaria vector control

Globally, malaria is still a persistent health problem affecting more than 200 million people. With about 90% of malaria cases occurring in Sub-Saharan Africa, it becomes imperative to understand the environmental factors contributing to malaria vector proliferation. The cattle hoofprints are known to be some of the productive breeding sites for Anopheles (An.) arabiensis and An. fenestus in Southern and East African countries. Therefore, this study aimed at testing the potential of integrating field data and Sentinel-2 satellite imagery for mapping cattle hoofprint distribution in the Vhembe District, South Africa. The purpose was to improve the predictability of mosquito breeding sites in the study area by using field point dataset and Sentinel-2 data. Due to the difficulty of sampling all locations in the study area, the spatial interpolation was employed to create continuous surfaces of cattle hoofprints, using limited sampled point observations. The sampled point observations were then correlated with Sentinel-derived variables for predicting cattle hoofprints at unsampled locations. The ordinary Kriging (OK), co-Kriging (CK) and step-wise multiple linear regression (SMLR) were used due to their ability to incorporate both field point data and ancillary datasets. The CK was the best performing interpolation method, with R² = 0.69 for validation dataset (n = 33), compared to OK (R² = 0.57) and SMLR (R² = 0.25). The resulting co-Kriging semivariogram shows that the combination of field data and remote sensing dataset improves the prediction accuracy of cattle hoofprint distribution. Findings from this study demonstrated that the interpolation error for estimating cattle hoofprints/100 m² can be minimized greatly by using CK (RMSE = 0.2; MAD = 0.04) than with both OK (RMSE = 2.39; MAD = 2.11) and SMLR (RMSE = 5.20; MAD = 4.55) methods. Furthermore, the results from this study indicate that there is a high number of cattle hoofprints in malaria-prone areas at the study site than in the malaria-free areas. Studies such as this provide the platform for developing an operational platform for long-term monitoring of areas susceptible to malaria, risks, and control management.     

Regression using hybrid Bayesian networks: Modelling landscape–socioeconomy relationships

Modelling environmental systems becomes a challenge when dealing directly with continuous and discrete data simultaneously. The aim in regression is to give a prediction of a response variable given the value of some feature variables. Multiple linear regression models, commonly used in environmental science, have a number of limitations: (1) all feature variables must be instantiated to obtain a prediction, and (2) the inclusion of categorical variables usually yields more complicated models. Hybrid Bayesian networks are an appropriate approach to solve regression problems without such limitations, and they also provide additional advantages. This methodology is applied to modelling landscape–socioeconomy relationships for different types of data (continuous, discrete or hybrid). Three models relating socioeconomy and landscape are proposed, and two scenarios of socioeconomic change are introduced in each one to obtain a prediction. This proposal can be easily applied to other areas in environmental modelling.     

基于卫星定位信号的车辆行驶里程计算方法

本文针对当前各种车联网应用服务中,基于车辆实时经纬度信息的里程计算与统计误差问题,提出一种低采样频率下的车辆行驶里程计算方法.首先对起点进行信任度验证,消除起点漂移带来的影响;然后采用连续大角度过滤、大距离过滤、大速度过滤、大加速度过滤相结合的方法进行噪点过滤;在此基础上,采用速度插值与角度插值相结合的方法对弯道进行里程补偿;另外,针对丢点数据,采用基于路径规划的电子地图调用方式进行里程补偿,进一步提高算法的准确性.经实验验证,在较低的位置信号采样频率下,该方法仍有较高的里程计算精确度和执行效率.     

基于法向量距离的路面坑槽提取方法

以路面高程激光点云为研究对象,提出一种基于法向量距离的路面坑槽提取方法.首先对路面高程点云数据进行数据清洗;其次采用自适应最优邻域的PCA方法估算路面点云数据的法向量,通过计算路面点云中采样点到其局部二次曲面的切平面的法向距离作为法向量距离;以法向量距离描述采样点的三维空间特征,并通过阈值分割自动提取路面坑槽点云集合,...     

Visual inspection of multivariate volume data based on multi-class noise sampling

Visualizing multivariate volume data is useful when the user wants to inspect the correlational distributions of multiple variables in a spatial field. Existing solutions commonly rely on color blending or weaving techniques to show multiple variables on a sampling point, probably causing heavy visual confusion. This paper presents an alternative solution that employs a multi-class sampling technique to generate spatially separated sampling points for multiple variables and illustrates the sampling points of each variable individually. We combine this new sampling scheme with the conventional direct volume rendering mode, iso-surface mode, and the cutting plane mode to support interactive inspection of volumetric distributions of multiple variables. The effectiveness of our approach is demonstrated with the IEEE VIS Contest 2004 Hurricane dataset and a 3D nuclear fusion simulation dataset.     

基于LIDAR数据的城市数字表面模型生成技术

激光雷达(LIDAR)数据是一种新型数据源,它产生的是高密度点云数据。为了更加方便地应用这些数据,首先要生成数字表面模型(DSM)。采用传统的方法生成城市DSM,对城市区域复杂性的考虑不足,也没有对数据存在的缺值情况进行相应处理,故无法生成高质量的城市DSM。该文阐述了一种新颖的生成高质量城市DSM的方法,兼顾城市的复杂性和LIDAR传感器本身的特点。试验证明,该方法生成的DSM与传统方法相比,具备更好的效果。     

Radar image texture as a function of forest stand age

Data on forest variables are required for environmental and forest management applications. Numerous authors have shown significant correlations between mean radar response intensity and forest variables (age, height or biomass) but few studies have explored the spatial characteristics of the radar response for varying forest states. In this Letter, variation in the most commonly used texture features is shown as a function of an indicator of forest growth (age) for a controlled homogeneous test site (monospecific, even‐aged forest, with identical sylvicultural practices and a sampling that covers all forest stages from sowing to harvest). Significant linear relationships between some texture features and stand age are observed. Moreover, the quality of some fits indicates that texture could be used instead of the usual intensity–age relationships that saturate for mature stands.     

A multigrid method for the estimation of geometric anisotropy in environmental data from sensor networks

This paper addresses the estimation of geometric anisotropy parameters from scattered spatial data that are obtained from environmental surveillance networks. Estimates of geometric anisotropy improve the accuracy of spatial interpolation procedures that aim to generate smooth maps for visualization of the data and for decision making purposes. The anisotropy parameters involve the orientation angle of the principal anisotropy axes and the anisotropy ratio (i.e., the ratio of the principal correlation lengths). The approach that we employ is based on the covariance Hessian identity (CHI) method, which links the mean gradient tensor with the Hessian matrix of the covariance function. We extend CHI to clustered CHI for application in data sets that include patches of extreme values and clusters of varying sampling density. We investigate the impact of CHI anisotropy estimation on the performance of spatial interpolation by ordinary kriging using a data set that involves both real background radioactivity measurements and a simulated release of a radioactive plume.     

Mapping wildfire occurrence at regional scale

When assessing fire danger, interpolation of the dependent variable—historic fire occurrence—is required in order to statistically compare and analyze it with human factors, environmental parameters and census statistics. To confirm the compatibility between the distinct data types, occasionally, for this kind of spatial analysis, historical observations of the primary wildland fire (given as x and y coordinates) must be transformed either to continuous surfaces or to area data. The simple overlay approach converts single point observations to area data. However, this procedure assumes lack of spatial uncertainties that would otherwise result in serious errors caused by the positional inaccuracies of the original point observations.Here, we used kernel density interpolation to convert the original data on wildland fire ignition into an expression of areal units, defined by a raster grid and, subsequently, by the administrative borders of the municipalities in two study areas in Spain. By overlaying a normal bivariate probability density function (kernel) over each point observation, each ignition point was considered an uncertain point location rather than an exact one.     

基于线性插值的电网频率测量方法

针对常用的硬件测频法和软件测频法分别存在硬件设计复杂、软件测量误差较大的问题,提出了一种基于线性插值的电网频率测量方法。该方法首先找出半波内与第一个过零点最近的2个采样点,利用线性插值算出该过零点与后一个采样点之间的时间T1;其次找出半波内与下一个过零点最近的2个采样点,利用线性插值算出该过零点与前一个采样点之间的时间T3;再次计算半波内第一个和最后一个采样点之间的时间T2;最后将T1、T2、T3相加,即可算出电网频率f。测试结果表明,该方法测量误差为±0.05Hz,且计算过程简单。     

Mapping wildfire occurrence at regional scale

When assessing fire danger, interpolation of the dependent variable—historic fire occurrence—is required in order to statistically compare and analyze it with human factors, environmental parameters and census statistics. To confirm the compatibility between the distinct data types, occasionally, for this kind of spatial analysis, historical observations of the primary wildland fire (given as x and y coordinates) must be transformed either to continuous surfaces or to area data. The simple overlay approach converts single point observations to area data. However, this procedure assumes lack of spatial uncertainties that would otherwise result in serious errors caused by the positional inaccuracies of the original point observations.Here, we used kernel density interpolation to convert the original data on wildland fire ignition into an expression of areal units, defined by a raster grid and, subsequently, by the administrative borders of the municipalities in two study areas in Spain. By overlaying a normal bivariate probability density function (kernel) over each point observation, each ignition point was considered an uncertain point location rather than an exact one.     

基于毫米波雷达稀疏点云的人体行为识别方法

目前利用毫米波雷达进行人体行为识别的方法在复杂场景下无法很好的区分相似动作,与此同时模型的鲁棒性和抗干扰能力也相对较差;针对以上两个问题,提出了一种通用的基于毫米波雷达稀疏点云的人体行为识别方法,该方法首先利用K-means++聚类算法对点云进行采样,然后使用基于注意力特征融合的点云活动分类网络进行人体行为特征的提取和识别,该网络可以兼顾点云的空间特征以及时序特征,对稀疏点云的运动有灵敏的感知能力;为了验证所提出方法的有效性和鲁棒性,分别在MMActivity数据集和MMGesture数据集上进行了实验,其在两个数据集上取得97.50%和94.10%的准确率,均优于其它方法;此外,进一步验证了K-means++点云采样方法的有效性,相较于随机采样,准确率提升了0.4个百分点,实验结果表明所提出方法能够有效的提升人体行为识别的准确率,且模型具有较好的泛化能力。     

Nonparametric uncertainty representation method with different insufficient data from two sources

The uncertainty information of design variables is included in the available representation data, and there are differences among representation data from different sources. Therefore, the paper proposes a nonparametric uncertainty representation method of design variables with different insufficient data from two sources. The Gaussian interpolation model for sparse sampling points and/or sparse sampling intervals from a single source is constructed through maximizing the logarithmic likelihood estimation function of insufficient data. The weight ratios of probability density values at sampling points are optimized through minimizing the total deviation of the fusion model, and the fusion Gaussian model is constructed based on the weight sum of the optimum probability density values of sampling points for Source 1 and Source 2. The methodology is extended to five different fusion conditions, which contain the fusion of uncertain distribution parameters, the fusion of insufficient data and interval data, etc. Five application examples are illustrated to verify the effectiveness of the proposed methodology.