Characteristic analysis of coordinate time series of tide gauge station
-
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.
-
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 
下载: 导出CSV
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
下载: 导出CSV
-
[1] 翟国君, 黄谟涛. 海洋测量技术研究进展与展望[J]. 测绘学报, 2017, 46(10): 1752-1759. DOI: 10.11947/j.AGCS.2017.20170309 [2] 程文. 基于GPS信噪比观测值的水位反演研究[J]. 全球定位系统, 2020, 45(1): 105-109. [3] NICHOLLS R J, CAZENAVE A. Sea-level rise and its impact on coastal zones[J]. Science, 2010, 328(5985): 1517-1520. DOI: 10.1126/science.1185782 [4] 张永垂, 禹凯, 史剑, 等. 海平面年际变化研究进展[J]. 海洋预报, 2018, 35(1): 95-102. DOI: 10.11737/j.issn.1003-0239.2018.01.011 [5] 侯恺昕, 张胜军, 孔祥雪. 基于验潮站资料的HY-2A测高数据质量评定[J]. 海洋学报(中文版), 2019, 41(7): 136-142. [6] 李大炜, 李建成, 团文征. 利用卫星测高与验潮站数据监测越南近海海平面变化[J]. 测绘通报, 2017(6): 1-4. [7] 牛余朋, 郭金运, 袁佳佳, 等. 集成奇异谱分析和自回归滑动平均预测日本近海海平面变化[J]. 地球物理学报, 2020, 63(9): 3263-3274. DOI: 10.6038/cjg2020N0203 [8] BURGETTE R J, WATSON C S, CHURCH J A, et al. Characterizing and minimizing the effects of noise in tide gauge time series: relative and geocentric sea level rise around Australia[J]. Geophysical journal international, 2013, 194(2): 719-736. DOI: 10.1093/gji/ggt131 [9] NEREM R S, CHAMBERS D P, CHOE C, et al. Estimating mean sea level change from the TOPEX and Jason altimeter missions[J]. Marine geodesy, 2010, 33(S1): 435-446. DOI: 10.1080/01490419.2010.491031 [10] CHURCH J A, WHITE N J. Sea-level rise from the late 19th to the early 21st century[J]. Surveys in geophysics, 2011, 32(4): 585-602. DOI: 10.1007/ s0712-011-9119-1 [11] BOS M S, WILLIAMS S D P, ARAUJO I B, et al. The effect of temporal correlated noise on the sea level rate and acceleration uncertainty[J]. Geophysical journal international, 2014, 196(3): 1423-1430. DOI: 10.1093/gji/ggt481 [12] MONTILLET J P, MELBOURNE T I, SZELIGA W M. GPS vertical land motion corrections to sea-level rise estimates in the Pacific northwest[J]. Journal of geophysical research: oceans, 2018, 123(6): 1196-1212. DOI: 10.1002/2017JC013257 [13] 李昭, 姜卫平, 刘鸿飞, 等. 中国区域IGS基准站坐标时间序列噪声模型建立与分析[J]. 测绘学报, 2012, 41(4): 496-503. [14] HE X X, MONTILLET J P, HUA X H, et al. Noise analysis for environmental loading effect on GPS position time series[J]. Acta geodynamica et geomaterialia, 2017, 14(1): 131-142. DOI: 10.13168/Agg.2016.0034 [15] 马飞虎, 岳祥楠, 贺小星, 等. CME 对 IGS 基准站坐标序列噪声模型及速度估计影响分析[J]. 全球定位系统, 2019, 44(5): 47-54. [16] BOS M S, FERNANDES R M S, WILLIAMS S D P, et al. Fast error analysis of continuous GPS observations[J]. Journal of geodesy, 2008, 82(3): 157-166. DOI: 10.1007/s00190-007-0165-x [17] BOS M S, FERNANDES R M S, WILLIAMS S D P, et al. Fast error analysis of continuous GNSS observations with missing data[J]. Journal of geodesy, 2013, 87(4): 351-360. DOI: 10.1007/s00190-012-0605-0 [18] HOLGATE S J, MATTHEWS A, WOODWORTH P L, et al. New data systems and products at the permanent service for mean sea level[J]. Journal of coastal research, 2013, 29(3): 493-504. DOI: 10.2112/JCOASTRES-D-12-00175.1 [19] HÅKANSSON B, ALENIUS P, BRYDSTEN L. Physical environment in the Gulf of Bothnia[J]. Ambio a journal of the human environment, 1996: 5-12. [20] 刘聚, 暴景阳, 许军. 相对海平面变化时段选择效应分析[J]. 武汉大学学报(信息科学版), 2021, 46(1): 79-87. -
点击查看大图
图(5) / 表(6)
计量
- 文章访问数: 518
- HTML全文浏览量: 414
- PDF下载量: 38
- 被引次数: 0
