基于模型和空间相关性的GPS时间序列插值方法

针对GPS时间序列的空缺,根据数据固有特性和空间特性,提出基于模型和噪声空间相关性的GPS时间序列插值方法,并以陕西省GPS连续站为例验证该方法对长空缺插值的优势。通过实验分析不同情况的空缺和插值对GPS时间序列速度、周期项以及噪声特性的影响。结果表明,空缺对速度影响较小,较少的观测也可以较好地反映地壳线性运动;空缺对周期项影响较大,利用GPS分析周期性运动时必须考虑空缺的影响;噪声频谱分析显示离散空缺比长空缺影响大,噪声特性分析时应注意。     

雷州半岛雷暴频数变化特征研究

对雷州半岛近50 a来的年雷暴日数变化时间序列进行小波分析,揭示了其雷暴频数多时间尺度的复杂结构,结果表明:雷州半岛平均年雷暴频数线性拟和呈现缓慢下降趋势;平均年雷暴日91 d,1983年及以前基本高于年平均值,1984年之后均低于年平均值;平均年雷暴频数总体趋势为1983年前正距平,1984年后为负距平;在20世纪60年代中期存在准2 a的周期变化,70年代存在显著的2-4 a的周期变化,80年代末至90年代末存在显著的3-4 a的周期变化,在90年代中后期存在显著的准2 a的周期变化。     

中国IGS站点ZTD长时序特征及其与年降水量的关系研究

利用小波变换方法对中国IGS站点ZTD时间序列进行分解与重构,分析ZTD序列的低频趋势项、高频年变化周期项与年降水量的对应关系,并结合气候资料分析原因。研究发现,ZTD低频趋势项与年降水量的变化趋势相同,ZTD高频周期项与年降水量变化存在较好的对应关系,ZTD年周期项峰值的高低对应年降水量的多少。     

变形观测数据时间序列建模中的几个问题

针对在变形观测数据时间序列建模中所遇到的问题,对变形观测数据时间序列建模中的数据预处理、模型选择、模型定阶与系统稳定性检验等问题进行了研究,提出了分析数据趋势项提取的AR模型方法,编写了C语言计算程序,实现了样条函数插值方法、周期项提取的差值法,建立ARMA分析模型,对变形预测数据与实际观测数据进行了比较.结果表明:该方法的正确性以及时间序列分析方法在变形数据处理与分析中的适用性与可行性.     

福建沿海的海面变化及其影响

本文根据福建沿海４个验潮站的验潮资料，计算了各站的局域海平面长趋势变化。结果表明，福建沿海的局域海平面变化速率为１～２ｍｍ／ａ，４个验潮站的平均变化速率为０．９ｍｍ／ａ，最后讨论了海面变化可能引起的次生灾害。     

基于小波变换的GPS水汽与气象要素相关性分析

利用北京市GPS连续观测资料结合气象资料开展GPS水汽与气象要素的相关性分析。首先使用GAMIT软件解算2009-06-01~2012-04-30的北京GPS连续观测网观测数据并结合气压数据获得测站水汽序列;然后用小波变换方法对GPS水汽、温度和气压数据进行分解与重构,并对重构后的数据进行相关性分析。GPS水汽序列的变化趋势与温度呈显著正相关,与气压呈显著负相关。水汽和气压存在年周期和半年周期变化,两者存在显著负相关特性;温度有年周期变化,水汽和温度在d13重构结果的相关性最好,存在显著正相关特性。     

天山地区GPS连续站高程时间序列分析

利用UNAVCO提供的天山地区境内外GPS连续站的观测数据,处理得到各站高程时间序列。进行最小二乘线性拟合,提取速度场,并消除原始序列的线性趋势。然后对时间序列进行谱分析以及小波多分辨率分析。可以看出,天山地区在过去10余年间呈现整体隆升趋势,各个观测站之间抬升的一致性较好,并且各站都表现出明显的季节性变化。     

基于ICEEMDAN与最小二乘的GNSS垂向序列噪声与非构造形变去除

通过完备总体经验模态分解(ICEEMDAN)与最小二乘法开展GNSS垂向序列的噪声与非构造形变去除研究。首先,采用ICEEMDAN方法对GPS垂向序列进行分解,分析GNSS垂向序列的年周期项、半年周期项和趋势项;然后,通过改进的小波阈值和排列熵值法对GNSS垂向序列进行降噪处理;最后,利用最小二乘法拟合非构造形变序列,找出与其最为接近的分解层数并进行改正,获得去除噪声和非构造形变的GNSS垂向形变信息。结果表明,利用ICEEMDAN方法可实现GNSS垂向序列噪声的有效去除;采用ICEEMDAN与最小二乘法可提高GNSS垂向序列形变测量精度,其中RMS值平均减小1.28 mm,WRMS差值平均增大0.33 mm。     

BDS/GPS系统时差的组合实时预报模型分析

构造适用于BDS/GPS系统时差的多项式预报模型,对二次项参数选取、显著周期项确定以及观测权函数选择等进行改进。基于AR模型,进一步构建多项式和AR模型的组合时差预报方法。算例显示,新模型的单天时差预报精度优于0.4ns,显著优于其他传统方法。     

地表温度变化对GNSS连续运行基准站垂向形变影响研究

利用实测地表温度数据,计算温度变化对GNSS基准站观测墩和基岩垂向形变的影响;联合大气负荷变化,综合分析温度变化对基准站垂向形变的影响规律。以新疆GNSS连续运行基准站数据为例,结果表明,温度变化对新疆GNSS连续运行基准站垂向影响的年周期项振幅为1.4～2.1 mm,半周年振幅为0.01～0.33 mm,周年项振幅占主要部分;垂向时间序列变化扣除温度变化影响后,RMS值平均减小0.55 mm,精度提高约9%;温度变化与大气负荷变化对垂向形变影响的趋势基本一致,呈现上半年增加、下半年下降,扣除两项影响后精度提高约13%。研究表明,温度和大气负荷变化均为基准站垂向位移周期性变化的重要影响因素。     

三峡库区堆积层滑坡间歇性活动预测:以白水河滑坡为例

三峡库区堆积层滑坡在季节性降雨和库水位周期波动的影响下呈现间歇性活动特征,滑坡活动强度与诱发因素作用强度和时间关系密切。以三峡库区白水河滑坡为例,分析了堆积层滑坡间歇性活动特征和诱发因素,发现降雨和库水位下降是滑坡变形的主要诱发因素。根据滑坡时序曲线特征,将滑坡累积位移分解为趋势项位移和周期项位移,采用多项式拟合的方法来预测趋势项位移,利用长短期记忆神经网络模型来预测周期项位移,并与极限学习机模型、广义回归神经网络模型的预测结果进行了对比分析,发现长短期记忆神经网络模型预测滑坡间歇性活动精度更高。     

GPS接收机仪器偏差的长期变化特性分析

基于欧洲定轨中心2001-2009年发布的部分IGS测站接收机仪器偏差估值,分析了不同类型接收机DCB的长期变化特性.结果表明:各类接收机DCB的长期时变特征均具备一定的周期性,其中以月和年周期最为明显;接收机DCB的长期变化中还存在一定的波动,部分接收机DCB的稳定性随时间积累逐渐变差,甚至存在偏移.     

全球海水质量季节变化研究

海平面变化是海水密度和海水总质量变化的结果,是全球气候变化研究的重要内容之一。利用GRACE卫星观测得到的重力场系数变化资料及重力卫星测高得到的海平面变化扣除由模式得到的热容海平面变化,研究了海水的质量变化。研究结果显示,由这两种方法得到的海水质量变化有非常明显的季节性特征,且其周年振幅和位相非常接近。     

Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network

An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.     

基于PCA方法的GPS台站时间序列分析

基于10 a以上的全球GPS台站数据,利用主成分分析法及其他数据处理方法,对台站时间序列进行预处理和结果分析,研究其中的非线性周期规律,探讨时间序列的主要影响机制。结果表明,主成分分析法可以将台站残差时空矩阵分解成若干正交成分,GPS台站时间序列的东西方向具有线性漂移趋势,全球大部分GPS台站都存在非线性周期规律,周年项和半周年周期占据主导地位。     

Global distribution of thermosteric contribution to sea level rising trend

The sea level derived from TOPEX/Poseidon(T/P) altimetry data shows prominent long term trend and inter-annual variability.The global mean sea level rising rate during 1993-2003 was 2.9 mm a-1.The T/P sea level trend maps the geographical variability.In the Northern Hemisphere(15°-64°N),the sea level rise is very fast at the mid-latitude(20°-40°N) but much slower at the high-latitude,for example,only 0.5 mm a-1 in the latitude band 40°-50°N.In the Southern Hemisphere,the sea level shows high rising rate both in mid-latitude and high-latitude areas,for example,5.1 mm a-1 in the band 40°-50°S.The global thermosteric sea level(TSL) derived from Ishii temperature data was rising during 1993-2003 at a rate of 1.2 mm a-1 and accounted for more than 40% of the global T/P sea level rise.The contributions of the TSL distribution are not spatially uniform;for instance,the percentage is 67% for the Northern Hemisphere and only 29% for the Southern Hemisphere(15°-64°S) and the maximum thermosteric contribution appears in the Pacific Ocean,which contributes more than 60% of the global TSL.The sea level change trend in tropical ocean is mainly caused by the thermosteric effect,which is different from the case of seasonal variability in this area.The TSL variability dominates the T/P sea level rise in the North Atlantic,but it is small in other areas,and shows negative trend at the high-latitude area(40°-60°N,and 50°-60°S).The global TSL during 1945-2003 showed obvious rising trend with the rate of about 0.3 mm a-1 and striking inter-annual and decadal variability with period of 20 years.In the past 60 years,the Atlantic TSL was rising continuously and remarkably,contributing 38% to the global TSL rising.The TSL in the Pacific and Indian Ocean rose with significant inter-annual and decadal variability.The first EOF mode of the global TSL from Ishii temperature data was the ENSO mode in which the time series of the first mode showed steady rising trend.Among the three oceans,the first mode of the Pacific TSL presented the ENSO mode;there was relatively steady rising trend in the Atlantic Ocean,and no dominant mode in the Indian Ocean.     

基于SSA-Mann Kendall的草原露天矿区NDVI时间序列分析

针对煤矿区植被指数时间序列研究中,存在年际尺度上对植被动态规律刻画不全面、月际尺度上因物候性周期波动导致变化趋势和周期振荡信号微弱难以提取、基于变换的变化检测物理意义不够明确的问题,本文以胜利露天矿区为例,在月际尺度,基于SSA-Mann Kendall重建草原露天矿区的采矿扰动区和伪不变特征区MODIS NDVI时间序列的趋势和周期振荡信号,从显著程度和突变时间2方面对趋势成分进行定量化分析,并结合各特征区周期振荡演变特征揭示采矿扰动下草原露天矿区植被生长的动态规律。结果表明：SSA-Mann Kendall能将NDVI时间序列的微弱信号充分放大,便于提取,并可对趋势成分进行定量化表达,结合周期振荡与趋势成分的演变特征有助于辅助识别矿区植被生长的动态特点;伪不变特征区植被无显著下降趋势,采矿扰动区下降趋势显著,且露天采场较排土场的趋势更为明显,草原露天矿区地表植被损伤具有突发性,突变点多发于矿井开工建设时;扰动形式差异导致部分矿井露天采场和排土场周期振荡演变特征存在差异,露天采场植被消失殆尽,排土场因植被恢复措施而具有更复杂的动态特点。     

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Global sea level change and thermal contribution

The global long-term sea level trend is obtained from the analysis of tide gauge data and TOPEX/Poseidon data. The linear trend of global mean sea level is highly non-umiform spatially, with an average rate of 2.2 mm year-1 in T/P sea-level rise from October 1992 to September 2002. Sea level change duc to temperature vanation (the thermosteric sea level) is discussed. The results are compared with TOPEX/Poseidon altimeter data in the same temporal span at different spatial scales. It is indicated that the ther-mal effect accounts for 86% and 73% of the observed seasonal variability in the northern and southern hemispheres, respectively. The TOPEX/Poseidon observed sea level lags behind the TSI, by 2 months in the zonal band of 40°-60° in both the northern and southern hemispheres. Systematic differences of about 1-2cm between TOPEX/Poseidon observations and thermosteric sea level data are obtained. The potential causes for these differences include water exchange among the atmosphere, land, and oceans, and some pos-sible deviations in thermosteric contribution estimates and geophysical corrections to the TOPEX/Poseidon data.     

Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir

In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott(HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine(ELM) and least squares support vector machine(LS-SVM) were chosen to predict theperiodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future.