鲁东南岚山头超高压变质岩流体包裹体特征与板片折返史

鲁东南岚山头含柯石英榴辉岩主要产于花岗质片麻岩内,是苏鲁超高压变质带主要榴辉岩体密集分布区之一。流体包裹体研究表明,榴辉岩矿物及细脉石英中捕获有四种类型包裹体:在超高压-高压榴辉岩相条件下捕获的 N_2±CO_2包裹体;在高压榴辉宕重结晶阶段被捕获的 CO_2-H_2O 包裹体和含子矿物高盐度 H_2O 溶液包裹体:在超高压岩石折返过程中的最晚(角闪岩相退变质甚至更晚)阶段捕获的低盐度 H_2O 溶液包裹体。利用榴辉岩矿物及脉体石英中捕获的流体包裹体相互期次关系,可以对本区超高压变质作用板片折返过程中的流体演化史进行重建。     

线图形简化与移位算子的协同方法

在地图综合过程中,线的图形简化和移位算子通常是分别执行的,图形简化和移位有时都会产生新的空间冲突。本文试图把这两种算子进行协同,避免在地图综合过程中进行空间冲突的多次探测,提高地图综合数据处理的效率。本文通过把线图形简化转换为线上的点移位,并构建邻近地图目标之间的移位传播路径,在考虑空间上下文关系和地图感受规则的前提下,使移位过程能考虑到线图形简化,并尽量保持有关地图目标的空间特征。最后,以道路和其周边的建筑物群为例,验证了该算法的有效性和可行性。     

采用弯曲进行道路化简冲突避免的方法

针对道路化简后可能导致要素间空间关系前后不一致的问题,提出了一种采用弯曲进行道路化简冲突避免的方法。首先,以弯曲为单元分析道路化简前后弯曲的形态变化规律,对化简过程产生的要素间空间冲突类型进行了归纳和总结;其次,基于道路弯曲与要素之间的空间关系,提出了相应的冲突判别规则;最后,利用弯曲化简的可控性和弯曲组的间接化简策略实现对化简冲突的避免。试验表明,该方法能有效地识别和避免道路化简产生的空间冲突,确保化简前后道路与其他要素空间关系的一致性。     

顾及拓扑一致性的水系三维曲线化简

鉴于常规曲线化简方法应用于水系曲线化简时难以顾及水系要素的三维特征及其拓扑关系,本文提出了一种顾及拓扑一致性的水系三维曲线化简方法。该方法首先对D-P算法进行三维扩展,实现水系中单条河流三维曲线化简,然后构建水系树结构表达其拓扑关系,最后按照水系树的层次顺序依次进行河流曲线化简和干流与支流的拓扑关系重构。试验结果表明,该方法化简精度高,既能保持水系的三维形态特征,又能保证河流交汇处的拓扑一致性。     

广义残差地形模型及其在局部重力场逼近中的应用

研究了残差地形模型中的非调和性问题,比较了基于棱柱体和球冠体的积分模型,提出了基于球冠体积分的广义残差地形模型。以泊松小波径向基函数为构造基函数,结合广义残差地形模型,融合多源实测重力数据构建了局部区域重力场模型。研究结果表明:基于棱柱体积分的残差地形模型精度较低,在山区可能引入毫伽级以上的误差,建议采用更为接近真实地形表面的球冠体积分模型。相比于原始的残差地形模型,基于球冠体积分的广义残差地形模型能更为精确地逼近局部重力场模型中地形因素引起的高频效应。     

基于规则格网的DEM插值实验

通过地形建模,将6个地形隶属函数按照101×101,112×112,126×126,144×144,168×168,201×201,257×257 7种格网密度生成6种局部地形单元的规则格网DEM;使用反距离加权(IDW)等7种插值算法,将前6种格网密度下的DEM插值成257×257规格;从原始257×257DEM中随机抽取检查点计算残差,并对残差中误差进行分析。通过分组插值实验,运用控制变量法、方差分析等方法研究地貌类型、采样密度和插值算法对DEM插值精度的影响。     

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。     

Underwater Navigation Based on Real-Time Simultaneous Sound Speed Profile Correction

Precise sound speed profile (SSP) information is critical when using sonar for underwater terrain navigation. Nevertheless, acquiring SSP information in real-time is difficult, especially in underwater navigation environment. To account for this problem, this paper presents an underwater navigation method that applies real-time SSP correction to the terrain measurements. The method uses a probe to measure the surface sound speed simultaneously. Meanwhile, redundancy of topographic measurement data is exploited to derive the equivalent SSP information. The acquired equivalent SSP is updated continuously with particle filter algorithm. The terrain measurements can be corrected in real-time with the equivalent SSP to improve the performance of underwater terrain navigation. By removing the impact of inaccurate SSP from the terrain measurements, the proposed method can achieve precise and robust underwater navigation result without using an underway-profiling instrument. Simulated results confirm the good performance of the proposed method.     

Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties.