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1.
基于范艾伦辐射带探测卫星的观测数据(2012年9月至2015年11月),收集了584个等离子体层顶密度波动事件,研究了这些事件分布随磁地方时、磁壳值以及地磁活动的变化关系,并使用快速傅里叶变换分析了全部事件平均功率谱.统计结果表明,等离子体层顶密度波动事件主要发生在磁地方时黄昏扇区,其分布与磁地方时和地磁活动具有明显的相关性.等离子体层顶密度波动在1~100mHz区间内具有接近-5/3的功率谱斜率,表明存在二维磁流体动力学湍流.本文统计结果将有助于进一步深入理解等离子体层顶密度波动在内磁层中能量传输的具体作用,并且将促进对等离子体层顶波动的激发、增强与传播过程的进一步研究. 相似文献
2.
Li Zhang Jianping Huang Haipeng Yu Xiaoyue Liu Yun Wei Xinbo Lian Chuwei Liu Zhikun Jing 《大气和海洋科学快报》2021,14(4):58-62
现如今,新冠肺炎(COVID-19)严重威胁着世界各国人民的生命健康.许多流行病学模型已经被用于为政策制定者和世界卫生组织提供决策参考.为了更加深刻的理解疫情趋势的变化特征,许多参数优化算法被用于反演模型参数.本文提议使用结合了高斯-牛顿法和梯度下降法的Levenberg-Marquardt(LMA)算法来优化模型参数.使用四个病例数相对较多的国家来验证这一算法的优势:相较于传统流行病学模型模拟曲线过早过快的到达峰值,应用 LMA 的 Statistical-SIR(Statistical-Susceptible-Infected-Recovered)模型可以更好地拟合实际疫情曲线. 相似文献
3.
《地学前缘(英文版)》2018,9(6):1657-1664
A long slope consisting of spatially random soils is a common geographical feature. This paper examined the necessity of three-dimensional(3 D) analysis when dealing with slope with full randomness in soil properties. Although 3 D random finite element analysis can well reflect the spatial variability of soil properties, it is often time-consuming for probabilistic stability analysis. For this reason, we also examined the least advantageous(or most pessimistic) cross-section of the studied slope. The concept of"most pessimistic" refers to the minimal cross-sectional average of undrained shear strength. The selection of the most pessimistic section is achievable by simulating the undrained shear strength as a 3 D random field. Random finite element analysis results suggest that two-dimensional(2 D) plane strain analysis based the most pessimistic cross-section generally provides a more conservative result than the corresponding full 3 D analysis. The level of conservativeness is around 15% on average. This result may have engineering implications for slope design where computationally tractable 2 D analyses based on the procedure proposed in this study could ensure conservative results. 相似文献
4.
面向矿产资源信息的空间关联性分析 总被引:2,自引:1,他引:1
关联性分析是综合多源、多类数据,发现和挖掘数据中潜在的相关关系,提取和挖掘数据之间的关联性。该方法对于充分利用地质大数据、发现地质要素之间的共生组合规律具有重要的意义。本文首先基于数据的空间位置,将不同类型的地质空间数据建立联系,形成空间属性数据库;其次应用统计方法,对不同来源数据中的属性特征进行分析,发现热液型金矿的形成与火山作用存在明显相关性;最后基于Apriori算法提取热液型金矿的伴生矿与侵入岩的频繁项集,发现伴生矿与侵入岩酸碱性二者密切相关。因此,在今后的工作中,有望应用空间关联性的方法对地球物理、地球化学、遥感等多源地质数据开展深入的研究分析。 相似文献
5.
Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site). 相似文献
6.
利用逐小时风云卫星TBB资料、逐小时中国自动站与CMORPH降水产品融合数据以及国家级地面观测站24小时累积降水量,统计分析2010~2016年夏季,伴随下游地区(104°E以东)降水的青藏高原云团东传过程以及东传过程中镶嵌于云团中的中尺度对流系统(Mesoscale Convective System,简称MCS)特征。结果表明,共出现120次伴随下游降水的高原云团东传过程,6月出现最频繁,但持续时间较长的过程多出现在7月。云团向东传播的主要三条路径是平直东传、沿长江折向东传和复合东传。其中路径二——沿长江折向东传中的过程是高影响过程,因为过程次数较多(46次),过程平均持续时间较长(62小时),在下游地区引发的降水日数和暴雨日数最多。属于东传过程的MCS在7月形成最多,集中分布在青藏高原东坡、云贵高原东部、长江沿岸及其以南地区。高原MCS影响长江中下游地区降水主要是通过向东传播的形式实现,因为即使生命史更长的中α尺度对流系统(Meso-α Convective System,简称MαCS)也鲜少直接移动至110°E以东地区。不同区域的中α尺度持续性拉长形对流系统(Permanent Elongated Convective System,简称PECS)的日变化特征显示,东传过程MCS更容易在夜间从高原东坡向东传播至下游地区。在三条路径中,路径二中的东传过程MCS数量最多、在下游地区发展最旺盛并与降水日数和覆盖范围存在更好的对应关系。 相似文献
7.
Statistical facies classification from multiple seismic attributes: comparison between Bayesian classification and expectation–maximization method and application in petrophysical inversion 下载免费PDF全文
We present here a comparison between two statistical methods for facies classifications: Bayesian classification and expectation–maximization method. The classification can be performed using multiple seismic attributes and can be extended from well logs to three‐dimensional volumes. In this work, we propose, for both methods, a sensitivity study to investigate the impact of the choice of seismic attributes used to condition the classification. In the second part, we integrate the facies classification in a Bayesian inversion setting for the estimation of continuous rock properties, such as porosity and lithological fractions, from the same set of seismic attributes. The advantage of the expectation–maximization method is that this algorithm does not require a training dataset, which is instead required in a traditional Bayesian classifier and still provides similar results. We show the application, comparison, and analysis of these methods in a real case study in the North Sea, where eight sedimentological facies have been defined. The facies classification is computed at the well location and compared with the sedimentological profile and then extended to the 3D reservoir model using up to 14 seismic attributes. 相似文献
8.
Based on the body strain record of Tiantanghe station from 2008 to 2014,we make a statistical analysis of the relationship between the maximum amplitude of the body strain record and the surface-wave magnitude,epicenter distance of the earthquakes,which occurred in the Chinese mainland and its surrounding areas with MS≥6. 0 and the rest of the world with MS≥7. 0. According to statistical results,we propose a statistical formula between the surface-wave magnitude of earthquake and the maximum amplitude of the body strain record,the epicenter distance: M_S~*= 0. 37 ln A_max+ 0. 57 ln D + 0. 07. We can also derive a theoretical estimation formula for the maximum amplitude: A_max=e~(2. 7(M_S~*-0. 07))D~(-1. 54). This demonstrates that the maximum amplitude of the body strain record increases exponentially with the increase of the surface-wave magnitude, and decreases with the increase of the epicenter distance,and shows a negative correlation with their product. We further discuss the necessity of adding instruments with high frequency sampling to earthquake monitoring, and dicuss the prospects for precise earthquake prediction in future. 相似文献
9.
10.
Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave energy flux. In the initial tests, the ECMWF model and the statistical models are compared directly. The statistical models do better at short horizons, producing more accurate forecasts in the 1-5 h range. The ECMWF model is superior at longer horizons. The convergence point, at which the two methods achieve comparable degrees of accuracy, is in the area of 6 h. By implication, the physics-based model captures the underlying signals at lower frequencies, while the statistical models capture relationships over shorter intervals. Further tests are run in which the forecasts from the ECMWF model are used as inputs in regressions and neural networks. The combined models yield more accurate forecasts than either one individually. 相似文献