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加蓬G4-188区块是一个新的海外勘探区块,在钻井过程中遇到了井壁垮塌、气侵、井斜控制困难等诸多难题,影响了钻井施工的速度。针对这些难题,实施了解决G4-188区块钻井施工难点的各项技术措施,为G4-188区块后续钻井施工提供了有益的技术支持。 相似文献
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Examining the Relationship Between Spatial Configurations of Urban Impervious Surfaces and Land Surface Temperature 总被引:1,自引:0,他引:1
The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs. 相似文献
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快速城市化进程改变了城市地区原有下垫面,不透水层面积增加,暴雨期间汇流时间缩短,洪峰流量加大,进而导致城市内涝加剧,严重影响城市的防洪安全.本研究以广州市南沙区万顷沙网河区为研究对象,基于水量平衡的调蓄演算方法,对研究区域蓄排设施的规模进行分析;构建MIKE11一维网河模型,对涝区水情进行模拟,校核排涝工程规模,并对工... 相似文献
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The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (TBs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the TB signature. SSM/I TBs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover. 相似文献
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