基于小波变换的盆地沉积-构造波动分析

波动分析是一种定量研究盆地沉积-构造演化的方法.本文分析了滑动窗口法在盆地沉积波动分析数理方法中的诸多不足,采用小波变换的多尺度分解思想对研究区19个点处的信号做了处理,方法是使用db4小波对沉积速率直方图做Level9次分解.结果表明:以东营凹陷616-2点为例做沉积波动分析发现,通过小波变换可以获得能量波(A)、次能量波(n)、沉积演化波(g)和高频波(l)四个分解波,其中周期波n、g、l的周期分别是91、50、14 Ma;平面对比各分解波发现,能量波反映的是盆地沉积的整体趋势和盆内某点的平均沉积速率,次能量波反映的是盆地构造演化周期和断陷结构,沉积演化波反映的是板块聚敛控制下的盆地沉积-剥蚀演化,沉积干涉波反映的是盆地多尺度周期波叠加所呈现的地层沉积速率;空间尺度波动分析表明,一期构造运动会在空间内产生一个特定的构造波,而多个构造波在空间内会有规律地相互干涉,即产生多期构造叠加.     

短基线INSAR相干点探测及应用

提出一种结合子孔径相关测度的时序高相干点探测方法,首先对时序SAR影像进行谱分解获得子孔径视图,通过时序子孔径相关测度进行强散射点筛选,然后分别根据振幅离差和干涉相位空间相关性,对目标点进行相位稳定性分析,探测出既满足强散射且在时间序列上散射稳定的高相干点。利用改进的点探测方法和短基线INSAR技术,对北京2003—2009年间40景ASAR影像进行相位建模,获取地面沉降时空分布特征,分析典型地物时序沉降过程,研究北京地面沉降与地下水开采关系。结果表明:相比已有方法,本文结合子孔径相关测度的高相干点探测结果更准确可靠;利用本文点探测方法反演的历史累积沉降信息,与水准结果一致,平均速率之差在3.69 mm/a以内,中误差为1.36 mm/a;研究区地面沉降最大速率达92.25 mm/a,空间不均匀分布明显,地面沉降量与地下水开采量呈分段的非线性相关。     

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??????????????(SVD,Singular Value Decomposition)????????????????????????????M-P??????????????????ó????????????????????????M-P??????;??????????????????????????С??????С?????????????????????????????????????????????????????????;?????????С??????С??????????????????????????????????????δ???????С??????С?????;????????????????????????????????????????????????Ч???     

中国旅游业技术效率及其分解的时空格局——基于DEA模型的研究

以省(市)为核算单位,利用数据包络分析模型(DEA),评价1999～2008年间中国旅游产业技术效率,并将之分解为纯技术效率与规模效率,探寻旅游效率的时空特征及其演变的源泉。结论如下:(1)中国旅游业的综合技术效率平均值仅为0.330,1999～2008年间旅游效率呈现先下降后增加的趋势,由于受"非典"大爆发的影响,2003年旅游效率明显下降;(2)各省(市)旅游技术效率存在着明显的区域的差异,经济发展水平较高的东部地区,旅游效率较高。经济发展水平较低的中部和西部地区的旅游效率较低,二者均在全国平均效率水平以下;(3)纯技术效率和规模效率整体都不高,提升空间很大。从分解效率对总效率的贡献来看,纯技术效率对总效率的影响及制约能力略强于规模效率;(4)大部分省(市)旅游产业都处于规模报酬递增阶段,通过扩大规模可以获得更高的收益。     

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.