Characteristic analysis of coordinate time series of tide gauge station
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摘要: 文中以298个验潮站作为研究对象，采用广义高斯-马尔科夫模型(GGM)、自回归滑动平均模型(ARMA)以及分形自回归聚合滑动平均模型(ARFIMA)三种模型，对验潮站坐标时间序列噪声模型特性及海平面变化趋势进行估计分析，并探讨了时间跨度对验潮站速度估计的影响. 实验结果表明：验潮站坐标时间序列主要呈现为ARFIMA(1,0)、ARFIMA(2,2)、ARMA (1,0) 噪声特性；验潮站速度估计结果表明64.77%的站点速度值所处区间为0～4 mm/a，平均海平面速度为1.25 mm/a，整体处于上升趋势. 随着时间跨度的增加，验潮站坐标序列速度不确定度逐渐由发散趋于收敛，大于110 a的时间跨度有助于获取稳健的验潮站速度估计值.
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关键词:
- 验潮站 /
- 时间序列 /
- 自回归滑动平均模型(ARMA) /
- 分形自回归聚合滑动平均模型(ARFIMA) /
- 时间跨度 /
- 速度
Abstract: In this paper, 298 tide gauge stations are used as the research object, and three models of generalized gauss markov (GGM) noise model, autoregressive moving average (ARMA) noise model and Autoregressive fractionally integrated moving average (ARFIMA) noise model are used to estimate the noise model characteristics of the coordinate time series of tide gauge stations and the trend of sea level changes. The influence of time span on the velocity estimation of tide gauge stations analyzed and discussed. The experimental results show that the noise characteristics of coordinate time series of tide gauge stations are mainly ARFIMA(1,0), ARFIMA(2,2), ARMA (1,0); the velocity estimation results of tide gauge stations show that 64.77% of the station velocity values are in the interval of 0 mm/a to 4 mm/a, and the average sea level velocity is 1.25 mm/a, which is on an upward trend. As the time span increases, the velocity uncertainty of the coordinate sequence, of the tide gauge stations, gradually tends to converge from divergence, and a time span more than 110 a helps to obtain a robust estimate of the tide gauge station velocity. -
表 2 不同阶数ARFIMA模型估计结果
噪声模型 站点数 噪声模型 站点数 ARFIMA(1,0) 72 ARFIMA(3,3) 7 ARFIMA(1,1) 9 ARFIMA(3,4) 2 ARFIMA(1,2) 1 ARFIMA(3,5) 1 ARFIMA(1,3) 0 ARFIMA(4,0) 0 ARFIMA(1,4) 0 ARFIMA(4,1) 0 ARFIMA(1,5) 0 ARFIMA(4,2) 6 ARFIMA(2,0) 0 ARFIMA(4,3) 0 ARFIMA(2,1) 0 ARFIMA(4,4) 0 ARFIMA(2,2) 58 ARFIMA(4,5) 1 ARFIMA(2,3) 21 ARFIMA(5,0) 0 ARFIMA(2,4) 5 ARFIMA(5,1) 0 ARFIMA(2,5) 0 ARFIMA(5,2) 4 ARFIMA(3,0) 3 ARFIMA(5,3) 0 ARFIMA(3,1) 0 ARFIMA(5,4) 0 ARFIMA(3,2) 0 ARFIMA(5,5) 0 
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表 3 不同阶数ARMA模型估计结果
噪声模型 站点数 噪声模型 站点数 ARMA(1,0) 44 ARMA(3,3) 0 ARMA(1,1) 9 ARMA(3,4) 3 ARMA(1,2) 4 ARMA(3,5) 0 ARMA(1,3) 0 ARMA(4,0) 2 ARMA(1,4) 0 ARMA(4,1) 0 ARMA(1,5) 0 ARMA(4,2) 0 ARMA(2,0) 5 ARMA(4,3) 3 ARMA(2,1) 0 ARMA(4,4) 1 ARMA(2,2) 1 ARMA(4,5) 0 ARMA(2,3) 5 ARMA(5,0) 0 ARMA(2,4) 3 ARMA(5,1) 0 ARMA(2,5) 2 ARMA(5,2) 2 ARMA(3,0) 1 ARMA(5,3) 0 ARMA(3,1) 0 ARMA(5,4) 0 ARMA(3,2) 19 ARMA(5,5) 0
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