利用InSAR技术获取高寒高海拔地区高精度DEM

本文以Sentinel-1A SLC数据为原始影像,利用InSAR技术获取新疆西天山中部高寒高海拔地区小区域DEM,将获得的DEM与常用的SRTM v4 DEM和GDEMDEM进行对比分析。结果表明：利用InSAR技术处理Sentlnel-1A SLC数据可以获得分辨率为15 m的高精度DEM,该DEM数据精度优于SRTM v4 DEM和GDEMDEM,能更好地描绘地表地形细节,可作为输入数据获取地面高精度形变信息,为工程建设和地质灾害评价提供重要的基础数据。此外,该方法对DEM数据的更新也具有重大意义。     

HYSPLIT模式轨迹计算误差分析

基于NCEP 1°×1°再分析资料GDAS和NCEP2.5°×2.5°再分析资料GBL,利用美国NOAA 空气资源实验室ARL研发的一种用于计算和分析大气污染物输送、扩散轨迹的混合单粒子拉格朗日综合轨迹模式HYSPLIT,对2010年3月12日12时和田站距地100 m高度空气质点的3 d前向轨迹进行了模拟,分析了轨迹计算中的积分误差和分辨率误差.此次轨迹模拟试验中,积分误差对轨迹计算误差的贡献很小.随着积分时间的延长,积分误差略有增大.积分误差并未随着驱动数据分辨率的提高而减小.分辨率误差在积分的各个时刻并不相同,它与地形高度与天气系统有关.在轨迹模式中,轨迹计算基于风速在时间和空间上的线性插值,轨迹模拟的不确定性也与插值有关.使用不同分辨率的驱动数据对轨迹计算结果影响显著,分辨率误差对轨迹计算误差的贡献远大于积分过程中截断近似等带来的积分误差.     

顾及系统误差的GEO卫星几何法定轨PDOP值分析

针对GEO卫星几何法定轨中系统误差对PDOP值的影响进行研究,首先回顾了经典几何法定轨的基本原理,然后根据顾及系统误差的几何法定轨原理推导出PDOP值计算公式,并利用5个跟踪站的模拟数据,计算了多种系统误差情况下PDOP值。结果表明：系统误差对GEO卫星几何法定轨的PDOP值影响很大;采用顾及系统误差的几何法定轨方法可以较好地削弱系统误差对PDOP值的影响;国外布设跟踪站比仅在中国布设跟踪站能更好地削弱系统误差对PDOP值的影响。     

速度误差对时间偏移和深度偏移的影响分析

地震数值计算表明:地震速度模型出现误差,对时间偏移和深度偏移有不同的影响,且深度偏移结果的误差往往大于时间偏移的误差,影响了深度偏移方法的广泛应用。根本原因在于时间偏移使用的是均方根速度,而深度偏移使用的是层速度,因此在速度模型相同的情况下,层速度的变化远大于均方根速度的变化。地震数值模拟分析同时说明,深度偏移结果的误差还取决于地下介质的结构和岩性。当界面二边速度差异比较大时,速度摄动值δ引起深度偏移结果的误差,远大于界面二边速度平缓变化的情况。深度成像的误差等于速度摄动值δ和界面二边速度对比度vi+1/vi的乘积。     

Propagation of vertical and horizontal source data errors into a TIN with linear interpolation

Digital elevation models (DEMs) have been widely used for a range of applications and form the basis of many GIS-related tasks. An essential aspect of a DEM is its accuracy, which depends on a variety of factors, such as source data quality, interpolation methods, data sampling density and the surface topographical characteristics. In recent years, point measurements acquired directly from land surveying such as differential global positioning system and light detection and ranging have become increasingly popular. These topographical data points can be used as the source data for the creation of DEMs at a local or regional scale. The errors in point measurements can be estimated in some cases. The focus of this article is on how the errors in the source data propagate into DEMs. The interpolation method considered is a triangulated irregular network (TIN) with linear interpolation. Both horizontal and vertical errors in source data points are considered in this study. An analytical method is derived for the error propagation into any particular point of interest within a TIN model. The solution is validated using Monte Carlo simulations and survey data obtained from a terrestrial laser scanner.     

小波分析在雷击大气电场波形去噪分析中的应用

雷击时大气电场信号中混杂的噪声信号影响对真实大气电场信号的分析、提取和使用。文中通过计算仿真信号和去噪前后真实电场信号的均方误差,分析选取不同的小波函数、阈值法和小波分解层数对去噪效果产生的影响。考虑雷击时大气电场信号的信噪比,选用sym5小波和db5小波与minimaxi和rigrsure阈值法结合,且对小波的分解层数选用6~7层,进行大气电场波形的去噪处理,其去噪效果比较理想。最后,将该方法用于实际雷击大气电场波形数据的去噪处理,对雷击时电场波形的突变性检测中,在20个样本中有14个在首次雷击前1.5~2 h具有突变过程,占70%,这为将大气电场仪资料引入其他气象参数预报模型提供了切入点。     

Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model

This study used spatial autoregression（SAR） model and geographically weighted regression（GWR） model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity. Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased. SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices（i.e.,farmland density and temporal change）. The GWR model has smooth process when constructing the farmland spatial model. The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations. The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious. The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.     

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