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1.
��Ͽ����P���ٶȲ��������о�   总被引：2，自引：0，他引：2  
?????????????????10????2001~2006???17 243??P????????????о????????????P???????????????:???????P???????????????????????仯????????????;??????μ????????????仯??????????????????????????;1979??????5.1???????????????????仯???λ??λ?????????????????  相似文献   

2.
??20?????????????????????????????????????????μ??Σ???????????μ??о???????????·?????????????????о???????????????????о????????????δ????????о??????  相似文献   

3.
???t??????λ??????λ????????????GPS?????????????????д?????????????????????????????·????????????????λ????????????????з????????????λ??????????е?EW??NS???????????7.3??7.1??16.0 mm??????λ??EW??NS???????????5.0??5.2??14.7 mm????????????λ???????????λ?????1.9~2.3 mm?????ù????????????о??????λ??????????????????????????????????????е?????????????????????λ??????????0.1 Hz??????????????????????????0.1 Hz??????????????????????????λ??????????????????????????????????????????????????????????????????????????????????????????????????????????λ????????????????????????????????????????????????λ?????????????????????????????????  相似文献   

4.
HVF������GPS��·��ЧӦ�о��е�Ӧ��   总被引：3，自引：0，他引：3  
????????Helmert????????????Vondrak????????????HVF??????????????GPS??·??Ч????о????????????????????????????÷???????????Vondrak?????????????????????????????????Ч????????????????е????????????????÷????????·????????????GPS??·??Ч?????????????????????Ч????????·??Ч??????????????????GPS??λ?????  相似文献   

5.
???????????????GLONASS??????棬?о??????????????????????????????????????????????????Ч????????г??????????????????????????????????????1~300 s???????Ч???????????????????????????????????????????????300 min???X??Y??Z????????????266.43??246??13??336.06 m?????????????24???????????2 ms?????????????????GLONASS??????档
??  相似文献   

6.
?????????????GNSS???????????????????????????λ???????????????????3????????????3??GNSS???????????????GNSS??????????????μ????????????????????????????????????￡??????????????????3????????????????????????????????????????????????????????????????????????????????????????????λ??????????????????????????????з???3.0?????????????????о??д????????????  相似文献   

7.
��D-InSAR�о���ķ�����α䳡   总被引：4，自引：0，他引：4  
?????????D-InSAR????????????ENVISAT??????????????????????????????о?????????α??е???ü????????????  相似文献   

8.
???????ScanSAR??????2008??3??21?????????????α????????????ScanSAR?????????????????400 km??400 km??????????α??????????????100 km??100 km??IM????????α??????????????????????????????ScanSAR?????????????????е???????????ScanSAR??????????IM???????????????????????о??о??и???????????????????  相似文献   

9.
??????????????3????Σ???9?????????????SRTM3??о????????????????潨???????HASM???????ú????????????SRTM30??SRTM3?????????????????????TIN??SPLINE??IDW????????????????????????????????SRTM30??HASM?????????????????????????????????????????????????????????????θ???????????????HASM???????HASM????θ????????????С???????????????????θ??????????????????HASM??С???????θ?????????????????SRTM30????????????θ???????????????SRTM30??  相似文献   

10.
??????????ж???·????GPS??????2005??2007????????????????????????????????????ε?ò??????????????о???????????ж???·??????????????????????????????????????Щ??????????????????1???о?????????????????????-??????????????????????????????????????????￡????????????????????????????????????????????????????????????2???????????????????????α??η?????????????????η?????????й??????????1????????????????????????????????????????-????????????3?????????-?????????????F76??F67??F50 3???????????????????????????????-??????????????F73??F65????λ???丽?????????????????У????????????????????????????ε??????  相似文献   

11.
区域滑坡易发性评价对滑坡灾害防治具有重要意义，贵州省思南县由于其特殊的自然地理和地质条件，受滑坡地质灾害的影响非常严重，因此，非常有必要对思南县的滑坡易发性进行评价。在滑坡编录的基础上，采用由RS、GIS和GPS组成的3S技术，获取了思南县的数字高程模型、坡度、坡向、剖面曲率、坡长、岩土类型、地表湿度指数、距离水系的距离、植被覆盖度和地表建筑物指数10个滑坡影响因子；再在频率比和相关性分析的基础上，利用逻辑回归模型对思南县的滑坡易发性进行了评价并绘制了易发性分布图。结果表明:利用逻辑回归模型预测思南县滑坡易发性的准确率(AUC值)达到0.797，较为准确地预测出了思南县滑坡分布规律；极高和高滑坡易发区主要分布在高程低于600 m、地表坡度较大且以软质岩类为主的区域；而极低和低滑坡易发区主要分布在高程较高、地表坡度较小且以硬质岩类为主的区域。   相似文献   

12.
At 5 am 24 th June 2017, a catastrophic landslide hit Xinmo Village, Maoxian County, Sichuan Province, China. The slide mass rushed down from an altitude of 3400 m and traveled 2700 m in a high velocity. The 13 million m~3 deposition buried the whole village and caused about 100 deaths. The source area of the landslide is located in a high steep slope, average slope angle is 40o and maximal angle is 65o. The strata are interbedded Triassic Zagunao Formation metamorphic sandstone and slate with the dip slope angle of 45°. Based on high-resolution satellite remote sensing image, UAV image, DEM data, and field investigation, failure mechanism, travel features, and deposit characteristics were analyzed. The results showed that this landslide was influenced by Songpinggou Fault zone. According to the topography before the failure, the landslide is located in the back scarp of an antecedent landslide induced by Diexi Earthquake in 1933. The bedding slope provided potential rupture surface. Historical seismic activities and long-term gravitational deformation caused rock mass accumulated damages. Weathering and precipitation weakened the rock mass and finally induced shearing and tension failure. A huge block detached from the top rock slope, pushed the past landslide deposits in the middle part, rushed out of the slope bottom in a high velocity and buried the Xinmo Village. The rapid movement entrained and brought the soil into the Songping Gully which recoiled with and bounced back from the opposite mountain.  相似文献   

13.
大量穿越山地丘陵区的高压输电线路杆塔基础常位于滑坡灾害高易发斜坡地段, 施加适当防护措施提高其稳定性, 是保障输电线路持续安全运行的关键。为研究不同防护措施对杆塔基础滑坡的防护效果, 以湖北省巴东县燕子滑坡为地质原型, 设计制作物理试验模型, 分别开展了极端降雨条件下滑坡在无防护、施加抗滑桩与格构护坡时的物理模型试验, 从试验角度揭示了滑坡变形破坏特征与不同防护措施的防护效果。试验结果表明: 在2种极端降雨工况(50, 100 mm/h)下, 无防护的滑坡体历经了坡表冲刷、裂缝扩展、局部垮塌变形与整体滑动的演化过程; 抗滑桩措施对滑坡整体的防护效果显著, 滑坡整体处于稳定状态, 杆塔基础变形较小, 杆塔倾斜率满足规范, 但坡表会出现冲刷垮塌现象; 格构护坡措施能有效减少坡面冲刷和坡脚垮塌风险, 但在持续强降雨条件下对杆塔基础的整体稳固作用稍弱。物理模型试验结果与滑坡历史变形和实际治理效果吻合, 试验结论可为类似杆塔基础滑坡的破坏机理研究与防护工程设计提供借鉴。   相似文献   

14.
At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.  相似文献   

15.
Topographic attributes have been identified as the most important factor in controlling the initiation and distribution of shallow landslides triggered by rainfall.As a result,these landslides influence the evolution of local surface topography.In this research,an area of 2.6 km 2 loess catchment in the Huachi County was selected as the study area locating in the Chinese Loess Plateau.The landslides inventory and landslide types were mapped using global position system(GPS) and field mapping.The landslide inventory shows that these shallow landslides involve different movement types including slide,creep and fall.Meanwhile,main topographic attributes were generated based on a high resolution digital terrain model(5 m × 5 m),including aspect,slope shape,elevation,slope angle and contributing area.These maps were overlaid with the spatial distributions of total landslides and each type of landslides in a geographic information system(GIS),respectively,to assess their spatial frequency distributions and relative failure potentials related to these selected topographic attributes.The spatial analysis results revealed that there is a close relation between the topographic attributes of the postlandsliding local surface and the types of landslide movement.Meanwhile,the types of landslide movement have some obvious differences in local topographic attributes,which can influence the relative failure potential of different types of landslides.These results have practical significance to mitigate natural hazard and understandgeomorphologic process in thick loess area.  相似文献   

16.
??????8.0????????????????????????????????????λ??????????????η????????ν??????????????????8.0??????GPS??InSAR????α??????????????????????????????????????????1?????????????????GPS??InSAR??????????α????????????97.27%;2??????????????????????????????????????0??20 km??????????????λ???????????????????????????????????????????0??20 km???????????????????10 m????????10??15 km?????????????6.5 m??3??????????????????????????????????????????????????????????103??????????????????????121????????????????????138??????????????????????????105??4?????????????????8.7??10 20 Nm???????Mw7.9??  相似文献   

17.
High-speed landslide is a catastrophic geological disaster in the mountainous area of southwest China. To predict the movement process of landslide reactivation in Chenjiaba town, Beichuan county, Sichuan province, China, we simulated the movement process of two landslide failures in Chenjiaba via rapid mass movement simulation and unmanned aerial vehicle images(UAV), and obtained the movement characteristic parameters of the landslides. According to a back analysis, the most remarkable fitting rheological parameters were friction coefficient(μ=0.18) and turbulence(). The parameter of landslide pressure was applied as the zoning index of landslide hazard to obtain the influence zone and hazard zoning map of the Chenjiaba landslide. Results show that the Duba River was blocked quickly with a landslide accumulation at the maximum height of 44.14 mwhen the Chenjiaba deposits lost stability. The hazard zoning map indicated that the landslide hazard degree is positively correlated with the slope.This landslide assessment is a quantitative hazard assessment method based on a landslide movement process and is suitable for high-speed landslide. Such method can provide a scientific basis for urban construction and planning in the landslide hazard area to avoid hazards effectively.  相似文献   

18.
Guizhou Karst Plateau is located at the center of the karst region in Asia, where landslides are a typical disaster. Affected by the local karst environment, the landslides in this region have their own characteristics. In this study, 3975 landslide records from inventories of the Guizhou karst plateau are studied. The geographical detector method is used to detect the dominant casual factor and predominant multi-factor combinations for the local landslides. The results show that landslides are prone to areas on slopes between 10° and 35°, of clay rock, in close proximity to gullies, and especially in areas of moderate vegetation, dryland, and mild rocky desertification. Continuous precipitation over 10 days has a great effect on landslide occurrence. Compared with the individual factors, the impact of two-factor interaction has greater explanatory power for landslide volume. The volume of earthquake-induced landslides is predominantly controlled by the interactions of faults and slopes, while that of humaninduced landslides is affected by the interactions of land cover and hydrological conditions. For rainfallinduced landslides, the dominant interactions vary in different regions. In the central karst basin, the interactions between faults and precipitation can explain over 90% of the variations in landslide volumes. In the southern hilly karst region, the interactions between lithology and slope can explain over 71% of the variations in landslide volume and those between fault and land-use can explain 50% of the variations of the landslide volumes in the northeastern mountainous karst region.  相似文献   

19.
为了阐明地震滑坡的运动特性并对其进行致灾距的预测,基于遥感影像解译和野外调查数据,借助经验公式法,分析了汶川地震滑坡水平最大运移距离L与前后缘高差H之间的相关性,给出了经验公式;探讨了不同滑坡之间滑程的差异与异常。结果表明:若已知H,可用L=aH+b或L=aHb对总位移进行预测初探;将视摩擦系数H/L=0.45作为汶川地震高速远程型滑坡的上限较合适;滑坡体积、源区破裂面积与L呈正相关,与H/L呈负相关;地震滑坡易发生在山脊线平行于断裂带、垂直于地震波传播方向的山体两侧;崩塌型滑坡易发前后缘高差范围在10~100m之间,大型高速远程型滑坡易发前后缘高差大于200m;滑坡源区易发坡度分布在25°51°之间,滑床坡降变化范围为0~58°,高速远程型滑坡的滑床坡降主要在8°20°之间;分析认为滑程差异和异常是距离效应、能量传递与岩体挡板效应、滚动润滑与气垫效应、体积与破裂面积效应、地质因子、地形因子、颗粒级配与颗粒流效应等因素综合作用的结果,考虑上述因素有益于滑坡-碎屑流致灾距的预测分析。   相似文献   

20.
Regolith thickness is considered as a contributing factor for the occurrence of landslides. Although, mostly it is ignored because of complex nature and as it requires more time and resources for investigation. This study aimed to appraise the role of regolith thickness on landslide distribution in the Muzaffarabad and surrounding areas, NW Himalayas. For this purpose regolith thickness samples were evenly collected from all the lithological units at representative sites within different slope and elevation classes in the field. Topographic attributes (slope, aspect, drainage, Topographic Wetness Index, elevation and curvature) were derived from the Digital Elevation Model (DEM) (12.5 m resolution). ArcGIS Model Builder was used to develop the regolith thickness model. Stepwise regression technique was used to explore the spatial variation of regolith thickness using topographic attributes and lithological units. The derived model explains about 88% regolith thickness variation. The model was validated and shows good agreement (70%) between observed and predicted values. Subsequently, the derived regolith model was used to understand the relationship between regolith thickness and landslide distribution. The analysis shows that most of the landslides were located within 1–5 m regolith thickness. However, landslide concentration is highest within 5–10 m regolith thickness, which shows that regolith thickness played a significant role for the occurrence of landslide in the studied area.  相似文献   

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