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基于傅里叶级数的中国沿海地区Tm模型精化研究

本文采用中国沿海地区13个探空站2010~2014年实测地表温度T_s与平均温度T_m数据,利用傅里叶级数分析法精化中国沿海地区T_m模型,并将2015年探空站实测T_m数据与精化模型进行对比检验。结果表明,精化模型在T_m探测方面具有更高的计算精度,其计算大气可降水量的误差概率分布趋近于正态分布,具有较强的稳定性。     

一种变形监测中中长基线的单历元模糊度解算算法

针对已有单历元模糊度解算算法只适用于短基线的情形,根据误差传播率确定模糊度精度,依据误差确定模糊度搜索空间,提出一种利用GPS双频观测信息实现中长基线下单历元模糊度固定的算法。首先,利用电离层约束法得到双差模糊度之间的线性约束条件;其次,利用变形监测中监测站点坐标近似已知的条件进行约束,反算获得近似模糊度;最后,根据监测站点已有的观测数据精度给出搜索空间,搜索固定模糊度。结果表明,该方法可以实现中长基线下单历元的模糊度固定,对实时变形监测有一定的参考价值。     

GNSS变形监测网短基线时间序列噪声特性分析

利用时间跨度为5 a的GNSS短基线时间序列对噪声特性进行分析,发现长周期噪声分量(随机游走噪声)。选取最优噪声模型,评估不同噪声模型对测站周期振幅和线性速度估值的影响。结果表明,短基线时间序列中有色噪声应顾及闪烁噪声和随机游走噪声,对于表现出随机游走噪声的分量,可能与测站的真实运动有关;假设只有白噪声时求得的速度估值与最优噪声模型下求得的速度估值存在0.4～0.6 mm/a的偏差,对周期振幅的影响可以忽略。     

1960-2015年青海三江源地区降水时空特征

青海三江源地区是中国生态系统最为敏感和脆弱的地区,其降水特别是生长季降水的波动,是影响本区及江河中下游水资源安全、生态系统可持续发展的关键因素。综合线性趋势、Mann-Kendall检验、BG分割算法、R/S、EEMD等多方法细致辨识了1960-2015年研究区降水量序列的时空特征。结果显示：① 三江源降水量总体呈现弱增趋势,21世纪以来降水量显著增加,各子源区气候倾向率不尽相同;② 年、季降水量自东南向西北递减,澜沧江源区夏季降水和黄河源区秋季降水呈弱减趋势,雨量弱减区在空间上呈斑块状分布;③ 年、季降水量年代际变化和增湿率的空间差异较明显,春夏季降水气候倾向率与经纬度、海拔的复相关性显著高于冬季;④ 20世纪90年代中后期,各子源区降水总体显现增强信号,并于2002年前后发生突变;⑤ 年际和低值年代际显著周期是造成降水量变动的主要因素;⑥ 除澜沧江源区夏季降水趋于减少外,其他年、季降水量变化呈现增幅不一的转湿趋势;⑦ 横向比较各子源区可见,长江源区降水变化更能表征高原气候变化。研究结果显示,研究区降水时空序列变化具有明显的区域和季节差异性特征,与以往类似研究存在些许差异,可见为有效提高气候序列演变过程及突变诊断的准确性,仍需进一步融合多方法实施集成分析。     

Stochastic bivariate time series models of waves in the North Sea and their application in simulation-based design

In this paper, we present and evaluate three long-term wave models for application in simulation-based design of ships and marine structures. Designers and researchers often rely on historical weather data as a source for ocean area characteristics based on hindcast datasets or in-situ measurements. The limited access and size of historical datasets reduces repeatability of simulations and analyses, making it difficult to assess the sampling variability of performance and loads on marine vessels and structures. Markov, VAR and VARMA wave models, producing independent long-term time series of significant wave height (H_s) and spectral peak period (T_p), is presented as possible solutions to this problem. The models are tested and compared by addressing how the models affect interpretation of design concepts and the ability to replicate statistical and physical characteristics of the wave process. Our results show that the VAR and VARMA models perform sufficiently in describing design performance, but does not capture the physical process fully. The Markov model is found to perform worst of the tested models in the applied tests, especially for measures covering several consecutive sea states.     

基于EMD方法的地心运动时间序列分析

针对时间序列混有的高频信息会影响地心运动规律分析的问题,采用网平移法对IGS提供的GNSS周解进行解算,得到2007-2017年地心运动时间序列,对其进行分解重构,剔除高频项,并利用重构时序对地心运动规律作进一步分析探讨。结果表明：本文解算的地心运动在T_x、T_y和T_z方向的精度均为毫米级。EMD方法重构的时序保留了原序列的基本信息,且抑制了高频项的影响,提高了周期贡献率,3个方向的贡献率分别提高了12.3%、16.7%及6.3%。通过分析重构后的时序发现,周年项振幅为各周期对应振幅的最大值,分别为2.32、1.89和2.07 mm;T_y和T_z方向长期变化趋势较T_x更为明显,分别为0.13和-0.27 mm/a;半年项较小,且在T_x和T_y方向上具有时变性。此外,还发现了一些其他较小的年际变化。     

Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach

The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.     

基于支持向量机的GNSS时间序列预测

在深度学习的理论框架下,针对预测全球卫星导航系统(GNSS)时间序列,传统的经验风险最小化预测模型误差大精度低,泛化性能差且对历史数据的经验依赖大的问题.提出一种采用结构风险最小化原则的基于支持向量机(SVM)的时间序列预测模型.通过和多层的BP神经网络预测模型预测效果比较,结果证明SVM预测模型拥有更好的时间序列预测效果.     

下扬子地区幕府山组陆缘海-台地黑色细粒沉积岩系沉积学和孔隙结构特征

黑色细粒沉积岩系沉积学和孔隙结构研究对于沉积环境恢复、页岩气储层评价和有利储层预测具有重要意义.利用下扬子地区最新完钻的XYZ-1钻井资料,综合钻井岩心、岩石薄片、XRD分析、氩离子-场扫描电镜等手段,开展幕府山组细粒沉积岩系的沉积学和孔隙结构研究.结果表明,幕府山组岩性组成以块状、纹层状深色(碳质)泥岩、浅灰色纹层状...