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1.
�α��쳣����Ź�ϵ������ʶ   总被引:4,自引:0,他引:4  
??7?ξ???α???????о???????? 1)????α?????????????????仯????????????????α?????????????????仯??????????3???????????4????????????ж??????α?????? 2)?α??????????棻 ????3)?α????????????????????????????????α??????????????????????????????α???????????????????????????ж??????????????? ???? 4????????????????ж??????????????????α???????α????????????????????  相似文献   

2.
????????????????ε??????ζ??????????????????????????Ρ???????ζ????????????????任????????α??η????????????????????????  相似文献   

3.
????б?????峱????????????????Ms5.1???????и???400 km??Χ??????α?????????з????????????????????Щ????????????????仯?????????????????????仯????????????????л???????????  相似文献   

4.
�������ؿǴ����α�(GPS)���İ�����   总被引:1,自引:1,他引:0  
??????????GPS????????????????о????????????????α估???????Ms5.1?????????????????????????????????????????????N??S????????????????????????仯?????????????????S??N????????????仯???????????????????????????????????????????????????????????????1 ?????????  相似文献   

5.
2001??11??14????????????????8.1????????????????400 km???????,????????????????е??????????????????ERS??2 SAR?????????SRTM DEM???????????·????·???????洦????????????????α?????????????:?????????????????????????DEM????·???????????????????;???????????????????????????????????  相似文献   

6.
???????????????????????????????????????????α?????????????????????, ?о????????????????崹????????????????????????????α???????????????????????????????????????????????仯????ε?ò?????????????????????????????????????, ?????????????б??;????????????????????е???????????е???????е????????;????????????????1.00??2.00 mm/a??  相似文献   

7.
���ö̻��߲�ָ�����ͼ�����ر��α䳡   总被引:4,自引:1,他引:3  
????????????????????????е?????????????????????????????????????????????α???????м??α???????????????????????????????????????????????????????????????????????????????????????????Delaunay??????????????С?????????????λ????????????????????λ???????????????????????α???λ???н?????С???????????α?????????????С????????????????α??????????????????????????????α????仯?????????Ч??  相似文献   

8.
�ϲ�λ������ĵر��α�����   总被引:2,自引:0,他引:2  
?????????????????????????????????????ζ??λ??????????α???????о????????????????????????????α???????????????1)????????????????α?????????????????????????????????????С?????α?????н?????2)???????????????Σ??????????????????α????????????λ??????????????????????????α????????????????  相似文献   

9.
????GPS??????????????????????????????????????????????????????????Ω??????棬???????????????б????α?????????????????,??????GPS????????????????峱????????????GPS??????????α??????????????????????????????????豸???????????GPS???????峱????????????????????????豸?????????????????????????????????????????100 mm??  相似文献   

10.
?????????????????DInSAR???????????????????????????????????á?????????壨PS?????????????Ч??????????????????????????SAR???????????С???????SBAS????????SAR???????????У???????????????о??????????2008??1???2010??8??????8??ENVISAT?????SAR?????????SBAS??????????????????α???????????????????С???????α????????-27.11????-55.92????-67.61 mm/a????????α?????????α???????????????????£?????????SBAS?????????е???α????????  相似文献   

11.
大型人工线状地物是人类改造自然的产物,同人类生活息息相关。本文从大型人工线状地物定义出发,阐述了其形变现象、成因及衍生灾害;并利用多基线差分雷达干涉测量技术(DifferentialSyntheticApertureRadarInterferometry,DInSAR)实施大型人工线状地物形变监测。通过多数据源实验(ENVISATASAR,广州;PALSAR,香港大屿山;TerraSAR-X,深圳),分析了当前高级DInSAR方法,包括永久散射体和相干目标法在监测大型人工线状地物形变上的能力。实验结果表明,采用了不同的影像干涉对组合策略,永久散射体法适合大数据量SAR影像处理,而相干目标法适合小数据量SAR影像分析。微波穿透性和垂直临界基线随波长增加而增加。因此,在波段选择上,低相干区宜选用长波SAR数据(比如ALOSPALSAR)以获取稳健反演结果;而高相干区宜选用短波TerraSAR-X或者ENVISATASAR数据,以获取高精度地表形变场。结合线状地物几何和物理特性,分别从先验基础GIS/GPS数据、SAR数据源选择、PS点提取和模型改进四方面进行分析和探讨,认为面向线状地物形变监测多基线DInSAR模型的研发是亟待解决的问题。  相似文献   

12.
???ALOS PALSAR??Envisat ASAR?????????????α?????????L???κ?C????SAR?????????????????????L?????????????????????????????????????????α??????????α???????????PALSAR??????????????е?????????????????λ????????????????  相似文献   

13.
为探测汶川Ms8.0地震引起的地表形变,用ALOS卫星PALSAR L波段SAR数据与GPS观测数据,采用两轨雷达差分干涉方法,计算出了地震影响区约83 194 km 2 范围内的同震干涉图与形变场。此外,以16个GPS点的形变数据为参考,对干涉形变的精度进行分析,结果表明:除近断层区域两类数据存在较大差异外,整体吻合程度较高。  相似文献   

14.
????ERS-1/2??1996???1998??????C????SAR??????????????洦???????????????????????????????????α????????????????????????????????????????ò????????????Ч?????е????α???????????Ч?????????????????????????????????н???????????????????????????????????????????  相似文献   

15.
高强度煤炭开采产生巨大的地表形变,形变相位梯度过大导致干涉测量解缠错误,单一采用常规DInSAR及其衍生技术都无法获得地表沉陷主值。本文提出一种新的解决方案,即联合利用DInSAR与偏移量追踪技术(Offset-tracking)各自的技术优势,实现开采区大变形的准确提取,并基于GAUSS函数模型拟合恢复沉陷区剖面形态。基于2012年2月13日和2012年11月27日两景高分辨率SAR数据(RADARSAT-2,5 m精细波束模式(MF5))为数据源,以神东矿区布尔台矿、寸草塔一矿、二矿为研究区,采用常规DInSAR技术获得亚厘米级沉陷区边界,边界沉陷值处于-0.01~ -0.02 m;利用偏移量追踪方法获取米级地表沉陷中心主值,中心沉陷值集中在-1.0~ -4.0 m。将2种方法监测到沉陷信息分段融合,最后采用GAUSS函数模型重构矿区开采沉陷下沉特征曲线。结果表明,偏移量追踪方法可弥补DInSAR技术监测大量级形变信息的不足,联合技术可完整获取高强度采区的大形变沉陷。  相似文献   

16.
利用欧空局ENVISAT-ASAR影像数据,提取夏威夷基拉韦厄火山区域由2007-06-17~19小规模火山喷发引起的地表形变场,并结合GPS时间序列分析喷发前后形变特征。结果表明,此次小规模喷发造成Makaopuhi火山口附近发生明显地表形变,LOS向形变值最大超过30 cm。将研究区内同期GPS观测值投影至LOS方向,其结果与差分干涉所得形变量具有较高的一致性,均方根误差为1.8 cm。  相似文献   

17.
本文采用关联维的计算方法,对四川西部道孚6.9级地震和青海共和6.9级地震过程中的垂直地形变序列分维数进行了计算,得到了如下初步结论和认识:(1)上述两震区远场地形变序列分维数为0.63~0.73,而近场(震中区)序列分维数为0.12~0.49,近场出现明显的降维现象;(2)孕震期间地形变观测序列分维数为0.32~0.46,而震后时期则为0.72~0.85,孕震过程中地形变序列存在明显的降维现象。  相似文献   

18.
昆仑8.1级地震前青藏块体东北缘构造运动特征   总被引:9,自引:7,他引:2  
利用青藏块体东北缘1993、1999和2001年的GPS观测资料和1980年以来的跨断层流动形变资料,分析了昆仑8.1级地震前青藏块体东北缘地区水平运动演变与断层异常活动的一些特征。结果显示:震前水平运动与变形强度减弱,断层形变异常发育。结合块体和构造研究认为,青藏块体内部8.1级大震的蕴育和发生,对块体边界构造区域影响显著;震后调整和应力转移可能加速块体东北部某些构造部位应变能的积累。  相似文献   

19.
Frozen ground degradation plays an important role in vegetation growth and activity in high-altitude cold regions. This study estimated the spatiotemporal variations in the active layer thickness(ALT) of the permafrost region and the soil freeze depth(SFD) in the seasonally frozen ground region across the Three Rivers Source Region(TRSR) from 1980 to 2014 using the Stefan equation, and differentiated the effects of these variations on alpine vegetation in these two regions. The results showed that the average ALT from 1980 to 2014 increased by23.01 cm/10 a, while the average SFD decreased by 3.41 cm/10 a, and both changed intensively in the transitional zone between the seasonally frozen ground and permafrost. From 1982-2014, the increase in the normalized difference vegetation index(NDVI)and the advancement of the start of the vegetation growing season(SOS) in the seasonally frozen ground region(0.0078/10 a, 1.83 d/10 a) were greater than those in the permafrost region(0.0057/10 a,0.39 d/10 a). The results of the correlation analysis indicated that increases in the ALT and decreases in the SFD in the TRSR could lead to increases in the NDVI and advancement of the SOS. Surface soil moisture played a critical role in vegetation growth in association with the increasing ALT and decreasing SFD. The NDVI for all vegetation types in the TRSR except for alpine vegetation showed an increasing trend that was significantly related to the SFD and ALT. During the study period, the general frozen ground conditions were favorable to vegetation growth, while the average contributions of ALT and SFD to the interannual variation in the NDVI were greater than that of precipitation but less than that of temperature.  相似文献   

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