| 利用小波分析重力的长期变化 |
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| 引用本文： | 徐华君,柳林涛,许厚泽,孙和平,胡小刚.利用小波分析重力的长期变化[J].地球物理学报,2008,51(3):735-742. |
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| 作者姓名： | 徐华君 柳林涛 许厚泽 孙和平 胡小刚 |
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| 作者单位： | 1.中国科学院测量与地球物理研究所，武汉 430077;2.中国科学院研究生院，北京 100049 |
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| 基金项目： | 国家自然科学基金，中国科学院"百人计划" |
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| 摘 要： | 运用小波滤波方法估算Chandler和周年项的潮汐因子.本文分析了四个台站(Brussels, Boulder, Membach以及Strasbourg)的观测记录，运用合成潮方法得到重力残差后，用Daubechies小波带通滤波器滤波残差，得到256～512 d时间尺度上的序列，根据标准差最小原则确定观测极潮周年和Chandler项的周期，然后利用最小二乘法估算它们的潮汐因子，同时给出未经模型改正的周年重力.由于高阶Daubechies小波构造的滤波器具有良好的频率响应，且能压制信号中的高阶异常成分，使滤波的信号更加光滑，因此计算结果具有更小的均方差，更加可靠.
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| 关 键 词： | 小波分析 重力长期变化 极潮 重力残差 |
| 文章编号： | 0001-5733(2008)03-0735-08 |
| 收稿时间： | 2007-3-30 |
| 修稿时间： | 2007年3月30日 |
Wavelet approach to study the secular gravity variation |
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| XU Hua-Jun,LIU Lin-Tao,Xu Hou-Ze,SUN He-Ping,HU Xiao-Gang.Wavelet approach to study the secular gravity variation[J].Chinese Journal of Geophysics,2008,51(3):735-742. |
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| Authors: | XU Hua-Jun LIU Lin-Tao Xu Hou-Ze SUN He-Ping HU Xiao-Gang |
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| Institution: | 1.Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;2.Graduate School of the Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Wavelet filtering analysis is used to improve the estimation of gravity variations induced by Chandler and Annual wobble. This method eliminates noise in superconducting gravimeter (SG) records with band pass filters derived from Daubechies wavelet. The SG records at four European stations (Brussels, Boulder, Membach and Strasbourg) are analysed in this study. First, the earth tidal constituents are removed from the observed data by using synthetic tides, and then the gravity residuals are filtered into a narrow period band of 256~512 days by a wavelet band pass filter (periods of Chandler and Annual terms could be estimated by minimum standard deviation (MinSD)). These data are submitted to Least-Square method for estimating the gravimetric factor of the Chandler and Annual wobble. After processing by wavelet filtering, SG records can provide amplitude factors δ and phase lags κ of the two wobbles with much smaller mean square deviation (MSD) than those provided by former studies. It is mainly because the wavelet method can effectively eliminate instrumental drift and provide smoothed data series for the regression analysis. |
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| Keywords: | Wavelet filter residual gravity Secular gravity variations Polar tide Residual gravity |
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