Jin Xin, Liu Xin, Guo Jinyun, Shen Yi
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China.
Sensors (Basel). 2021 Feb 17;21(4):1403. doi: 10.3390/s21041403.
Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth's mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and -0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.
地球质心是包括固体地球、海洋和大气在内的地球系统质量中心。地球质心运动（GCM）的时变特征反映了地球质量的重新分布以及固体地球与质量负荷之间的相互作用。引入多通道奇异谱分析（MSSA）来分析由空间研究中心发布的1993年1月至2017年2月卫星激光测距数据确定的GCM产品，以提取GCM的周期和长期趋势。结果表明，GCM在X、Y和Z方向上具有明显的年、半年、准0.6年和准1.5年的季节性特征，其中年特征占主导地位，其振幅分别为1.7、2.8和4.4毫米。它还显示出6.09年的长期项以及三个方向上分别为0.05、0.04和-0.10毫米/年的非线性趋势。为了获得实时GCM参数，将结合线性模型（LM）和自回归移动平均模型（ARMA）的MSSA方法应用于预测未来2年的GCM。使用均方根误差（RMSE）评估所提出模型的预测精度。结果表明，所提出的方法可以有效地预测GCM参数，三个方向上的预测精度分别为1.53、1.08和3.46毫米。
