一种考虑PMX与PMY之间相关性的极移预报新方法

发布时间：2018-04-16 09:23

本文选题：LS模型 + AR模型　；参考：《大地测量与地球动力学》2017年11期

【摘要】：提出采用多元回归模型(MAR)与最小二乘(LS)组合进行极移预报。该模型考虑极移PMX和PMY的LS拟合残差之间的相关性,采用PMX残差和PMY残差一起构建预报模型进行残差预报。通过与LS+AR预报结果的对比表明,LS+MAR模型的预报结果更优。此外,通过与EOP_PCC预报结果的对比也说明,LS+MAR模型的短期极移预报精度能够达到国际先进水平。
[Abstract]:A multivariate regression model (Mar) combined with least squares (LSs) is proposed for polar motion prediction.The model considers the correlation between LS fitting residuals of pole shift PMX and PMY, and uses PMX residuals and PMY residuals to build prediction models for residual prediction.The comparison with the results of LS AR model shows that the prediction results of the MAR model are better than that of the LS-AR model.In addition, the comparison with the EOP_PCC prediction results also shows that the short-term polar shift prediction accuracy of the LLS MAR model can reach the international advanced level.
【作者单位】：中国矿业大学环境与测绘学院;
【基金】：国家自然科学基金(41404033) 国家科技部科技基础性工作专项(2015FY310200)~~
【分类号】：P22

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