Average recurrence intervals of strong earthquakes and potential risky segments along the Taiyuan-Linfen portion of the Shanxi graben system

Introduction In AD 1303, the great Hongtong, Shanxi, earthquake of magnitude 8 caused a very serious disaster, which killed over one hundred thousands people at least (Department of Earthquake Dis- *aster Prevention, State Seismological Bureau, 1995). On the occasion of commemorating this ca-tastrophe having occurred for 700 years, we have important problems that need to be answered: How long the average recurrence interval of the grea…     

Spatially weighted supervised classification for remote sensing

A simple approach for incorporating a spatial weighting into a supervised classifier for remote sensing applications is presented. The classifier modifies the feature-space distance-based metric with a spatial weighting. This is facilitated by the use of a non-parametric (k-nearest neighbour, k-NN) classifier in which the spatial location of each pixel in the training data set is known and available for analysis. A remotely sensed image was simulated using a combined Boolean and geostatistical unconditional simulation approach. This simulated image comprised four wavebands and represented three classes: Managed Grassland, Woodland and Rough Grassland. This image was then used to evaluate the spatially weighted classifier. The latter resulted in modest increase in the accuracy of classification over the original k-NN approach. Two spatial distance metrics were evaluated: the non-centred covariance and a simple inverse distance weighting. The inverse distance weighting resulted in the greatest increase in accuracy in this case.     

基于灰关联分析的GPS高程拟合加权平均函数模型权函数优选

基于灰色系统理论的灰关联分析方法,提出了权函数的优选方法,实例分析表明其具有较好的实用价值.     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

利用跨断层形变资料研究天山中段断层现今活动特征

利用2001年以来天山中段多个跨断层形变场地的测量资料，从断层运动方向和断层平均速率2个方面分析天山中段3条断层现今活动特征。结果表明，霍玛吐断裂呼图壁段断层的活动性质为正、逆断和左、右旋交替活动；北轮台-辛格尔断裂库尔勒段断层表现为右旋运动特征，活动性质为逆断性；妖魔山F2断裂乌鲁木齐段的断层活动性质以逆断为主。妖魔山F2断裂乌鲁木齐段断层的活动程度最高，霍玛吐断裂呼图壁段最低。霍玛吐断裂、北轮台-辛格尔断裂、妖魔山F2断裂垂直方向上基本均处于张性变化，前两者水平向处于挤压状态；断层活动速率的时间进程具有阶段性特征，各个时间段转折点有较好的匹配性。     

基于加权约束的单体建筑物点云表面重建算法

建筑物点云表面重建在高精度城市测绘、虚拟现实等领域有十分广泛的应用前景。由于建筑物的几何形态多变,重建算法普遍存在计算速率慢、拟合精度低和模型结构不完整的问题。为此,本文以单体建筑物为研究对象,提出基于加权约束的单体建筑物点云表面重建算法,在表面初始化过程中充分考虑数据对结构拟合的贡献。在此基础上,构建基于正则集的单体建筑物表面重建算法,实现建筑物拟合过程中的加权拟合误差、近邻结构平滑的同步优化。针对多类建筑物三维点云的实验结果表明,相比传统的建筑物重建策略,本文的加权约束方法可根据不同类型的点云数据设计自适应权重,并选择模型拟合中最优的权重函数,在高噪声、低精度点云数据下能得到更高精度的单体建筑物表面模型。     

利用水准数据的天津市地面沉降时空特征分析

地面不均匀沉降可能对城市的发展与人民的安全造成危害，天津市的地面沉降情况尤为严重。针对该问题，本文收集天津市2005—2012年、2016—2017年水准观测数据，以固定水准点位的沉降量、沉降速率、沉降加速率为状态向量，构建卡尔曼滤波模型，对天津市历年的水准观测数据进行滤波；根据滤波后的结果，本文利用多项式加权内插的方法，以距离、沉降速率、沉降加速率信息确定权值大小，对地面沉降情况进行内插；并以中误差作为精度评定参数，比较多种内插方法的精度。通过对内插结果的试验分析发现，2005—2017年天津市地面累计平均沉降量为394.477 5 mm，最大沉降量为1 143.5 mm；主要沉降区域为北辰、大港、塘沽等地区，且随着时间的增长，这些区域呈现漏斗式下沉。试验证明本文结合卡尔曼滤波与多项式加权内插的方式能够较好地反映地面沉降的时空特征分布情况并对未来一段时间的沉降情况进行预测，对天津市的城市发展及建设有一定的参考意义。     

基于GPT2w模型化加权平均温度反演可降水量

提出一种基于GPT2w模型化加权平均温度反演大气可降水量的方法，并分析附加系统偏差改正的模型化加权平均温度对可降水量的影响。结果表明，基于GPT2w模型化加权平均温度反演的大气可降水量的精度与基于Bevis公式计算的加权平均温度反演的大气可降水量的精度相当；对GPT2w模型化加权平均温度进行系统偏差改正后，大气可降水量的精度有一定改善，但改善率不到1%。     

顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法

稀疏多项式逻辑回归在分类中仅利用图像光谱信息，导致分类效果不太理想。本文提出了一种顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法。首先利用加权均值滤波与拓展形态学多属性剖面对原始高光谱图像进行局部与结构特征提取；然后对二者进行加权平均特征级融合以获取更具唯一性的像元特征；最后由稀疏多项式逻辑回归分类器对融合结果进行分类。结果表明，本文方法能有效地提高分类精度，而且具有较强的稳健性。     

福建省复杂地形下旬平均气温分布式模拟

为建立高时空分辨率的福建省复杂地形下气温栅格数据集，利用福建省及其周边33个常规气象站观测资料，基于数字高程模型（DEM）数据，综合考虑海拔、太阳总辐射、地表长波有效辐射对旬平均气温的影响，模拟了福建省复杂地形下旬均温的空间分布。结果表明：1）常规站验证结果显示：各旬气温绝对误差平均值（MAE）最小为0.46℃，最大为2.3℃，全年平均为0.87℃；加密站验证结果显示，MAE最大为2.3℃，最小0.5℃，全年平均为0.96℃。2）模拟结果能反映旬均温的宏观分布规律与局地细节特征。宏观范围内，旬均温受纬度影响较大，由北至南气温逐渐升高，沿海地区旬均温整体高于内陆，山区旬均温明显较低；局地范围内，各坡向上气温差异显著，海拔越高、坡度越大，差异越明显；地形因子对旬平均温的影响具有季节差异，具体表现为冬季时地形因子对旬均温的影响最大，秋季次之，春夏季节中地形因子对旬均温的影响最弱。