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991.
利用1980~2012年Hadley中心的HadISST再分析海温资料、美国NCEP/NCAR月平均再分析资料和中国160站月平均降水资料,研究了前冬澳大利亚周边海温与长江流域夏季降水关系。研究表明,澳大利亚周边冬季逐月海表温度距平(SSTA)EOF分解第一模态具有全区一致模形式,澳大利亚周边海温一致模具有季节持续性,可从前一年冬季一直维持到当年的夏季。用Ni?o3.4指数剔除海温场中ENSO线性信号后,表明澳大利亚周边海温一致模可能是独立于ENSO位相的区域局地现象。基于冬季澳大利亚周边海温一致模形式,定义了表征长江流域夏季降水一致模指数(CMI),对长江流域夏季降水具有一定的短期预测意义。澳大利亚周边海温一致模与长江流域夏季降水显著相关,一方面是因为澳大利亚周边海温异常能激发南北半球遥相关,造成西太平洋副热带高压异常,从而影响长江流域夏季降水;另一方面,澳大利亚周边海温异常导致热带地区南北半球对流异常,尤其使得菲律宾周边地区的对流活动异常,进而造成长江流域夏季降水异常。  相似文献   
992.
汕头3种人工红树林湿地大型底栖动物群落的比较   总被引:1,自引:1,他引:0  
对广东省汕头市莲阳河口区恢复背景相同的拉关木(Laguncularia racemosa)、无瓣海桑(Sonneratia apetala)、海桑(Sonneratia caseolaris)3种人工林和光滩湿地中大型底栖动物群落进行比较研究。结果显示,4种生境中大型底栖动物优势种(Y0.02)存在差异;相似性分析检验(One-Way ANOSIM)表明4种生境间大型底栖动物群落结构差异显著。等级聚类和非参数多变量标序也显示4种生境间的大型底栖动物群落结构差异显著。3种人工林中,无瓣海桑、海桑人工林中大型底栖动物群落结构较为相似;3种人工林中大型底栖动物物种多样性和生物量从大到小的排列次序为:拉关木海桑无瓣海桑。BIOENV分析表明大型底栖动物分布与林地土壤p H、有机质、砂粒和粉粒含量、红树植物平均株高、平均胸径、平均基径和冠幅(南北×东西)这些理化因子显著相关,这进一步证实了红树植物种类对林地大型底栖动物多样性和分布的影响。  相似文献   
993.
随着遥感对地观测技术的发展,卫星遥感在海岸带动态监测中占据愈来愈重要的地位,探索海岸带遥感监测的最佳方法,对今后的海岸带监测工作有重要的意义。文章从数据源、信息提取方法、监测频率这3个维度出发,选择深圳市两个典型的海岸带区域(人类活动频繁区域和人类活动稀少区域)作为研究区,针对两种海岸带区域寻求其海岸带土地利用遥感监测的最佳方法。研究结果表明:对于人类活动频繁区域(深圳湾北岸),数据源最好选择高分辨率ZY-3卫星影像,土地利用信息提取方法选择支持向量机分类,监测周期1~2年为最佳;对于人类活动稀少区域(大鹏湾东岸),数据源则可以选择分辨率较低的Landsat 8卫星影像,土地利用信息提取方法依然以支持向量机分类方法为最佳,监测周期则为5年。  相似文献   
994.
铜陵矿集区3000 m科学钻揭示,舒家店地区在1775 m以下存在红色的正长花岗岩,从该深度继续钻进约700 m后仍为这种岩石,而这种岩石在地表或其他钻孔中从未见到过。研究表明,正长花岗岩以富硅、富碱,富含高场强元素如Zr、Nb、Y和轻稀土元素(Eu除外)和较低的Mg O、Ba和Sr的含量为特征,全岩的10000Ga/Al比值为2.6,Zr+Nb+Ce+Y350×10-6,稀土元素配分模型具有明显的Eu负异常,微量元素原始地幔标准化曲线具有明显的Ba、Sr、P、Ti亏损,表现出A型花岗岩的地球化学属性。锆石LA-MC-ICP-MS U-Pb定年结果表明,该A型花岗岩的年龄为126 Ma,与长江中下游地区其他A型花岗岩的时代一致,属于长江中下游地区三期岩浆活动当中的第二期岩浆活动的产物。研究表明,长江中下游地区3次岩浆作用与岩石圈的多次拆沉作用密切相关,而且每一次拆沉作用都会使壳幔附近的深位岩浆房向地壳浅部迁移,而A型花岗岩则是迁移到浅部的深位岩浆房诱发的地壳部分熔融形成的岩浆,沿断裂带侵位而形成,也标志着本区的构造背景在126 Ma时处于拉张的高峰期。  相似文献   
995.
996.
Energy geostructures are rapidly gaining acceptance around the world; they represent a renewable and clean source of energy that can be used for the heating and cooling of buildings and for de‐icing of infrastructures. This technology couples the structural role of geostructures with the energy supply, using the principle of shallow geothermal energy. The geothermal energy exploitation represents an additional thermal loading, seasonally cyclic, which is imposed on the soil and the structure itself. Because the primary role of the piles is the stability of the superstructure, this aspect needs to be ensured even in the presence of the additional thermal load. The goal of this paper is to numerically investigate the behaviour of energy pile foundations during heating–cooling cycles. For this purpose, the finite element method is used to simulate both a single and a group of energy piles. The piles are subjected to a constant mechanical load and a seasonally cyclic thermal load over several years, imposed in terms of injected–extracted thermal power. The soil and the pile–soil interface behaviours are reproduced using a thermoelastic‐thermoplastic constitutive model. The thermal‐induced stresses inside the piles and the additional displacements of the foundations are discussed. The group model is used to investigate the interactions between the piles during thermo‐mechanical loading. The presented results are specific to the studied cases but lead to the conclusion that both the thermal‐induced displacements and stresses, despite being acceptable under normal working conditions, deserve to be taken into account in the geotechnical design of energy piles. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   
997.
ABSTRACT

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.  相似文献   
998.
The traditional direct method (i.e. metric ruler and rillmeter) of monitoring rill erosion at plot scale is time consuming and invasive because it modifies the surface of the rilled area. Measuring rill features using a drone‐based technology is considered a non‐invasive method allowing a fast field relief. In the experimental Sparacia area a survey by a quadricopter Microdrones md4‐200 was carried out, and this relief allowed the generation of a Digital Elevation Model (DEM), with a mesh size of 1 cm and a resolution elevation equal to 2 mm, for three plots (L, G and C) affected by rill erosion. At first for the experimental L plot, which is 44 m long, the rill features were surveyed by a ‘manual’ method which was carried out by drawing on the PC screen the rill paths obtained by a visual orthophoto interpretation. This manual method was not applicable for the plots in which rills of limited depth occurred and were not detectable. Then, for both L plot and the other experimental plots having a length ranging from 22 to 44 m, an ‘automatic’ extraction method of rills from DEM was applied. Using an appropriate calculation routine, a vector coverage of transects orthogonal to the main flow direction (i.e. the maximum slope steepness path) was generated. The intersection of each plot DEM with the transect coverage allowed to obtain both the cross sections and the main rill morphological features. For the L plot the comparison between the rill morphological features obtained by the two different methods (manual, automatic) was carried out. Finally, the length–volume relationship and a dimensionless relationship proposed in literature were tested for all studied experimental plots. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   
999.
1000.
用于AVO分析的振幅保真平面波叠前时间偏移   总被引:1,自引:1,他引:0  
To support amplitude variation with offset (AVO) analysis in complex structure areas, we introduce an amplitude-preserving plane-wave prestack time migration approach based on the double-square-root wave equation in media with little lateral velocity variation. In its implementation, a data mapping algorithm is used to obtain offset-plane-wave data sets from the common-midpoint gathers followed by a non-recursive phase-shift solution with amplitude correction to generate common-image gathers in offset-ray-parameter domain and a structural image. Theoretical model tests and a real data example show that our prestack time migration approach is helpful for AVO analysis in complex geological environments.  相似文献   
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