| 变分模态分解及能量熵在地心运动降噪中的应用 |
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| 引用本文： | 王庆余,杜宁,王莉,张小东,吴磊,熬逍.变分模态分解及能量熵在地心运动降噪中的应用[J].测绘通报,2020,0(8):59-64. |
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| 作者姓名： | 王庆余 杜宁 王莉 张小东 吴磊 熬逍 |
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| 作者单位： | 贵州大学矿业学院, 贵州 贵阳 550025 |
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| 基金项目： | 贵州省科技计划（黔科合基础〔2017〕1026） |
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| 摘 要： | 针对地心运动时间序列噪声种类复杂，随机性强，信号与噪声难以有效分离等问题，本文采用网平移法对IGS站周解进行解算，得到2012-2018年的地心运动时间序列，并提出了一种基于变分模态分解（VMD）及能量熵的地心运动时间序列降噪方法。首先，对各方向时间序列进行VMD分解，获得各方向高频依次到低频的时间序列分量；然后，计算每个变分模态分量的能量熵，辨识出噪声与信号的分界，并将信号分量进行重构，得到降噪后的地心运动时间序列；最后，通过与基于EMD和EEMD的降噪方法对比，从相关系数、信噪比、剩余能量百分比、方差贡献率等参数评价指标上定量说明该方法对地心运动时间序列降噪表现出更好的降噪效果。
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| 关 键 词： | 网平移法 降噪 变分模态分解 能量熵 信噪比 |
| 收稿时间： | 2019-12-27 |
| 修稿时间： | 2020-03-05 |
Application of variational mode decomposition and energy entropy in denoising of geocentric motion |
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| WANG Qingyu,DU Ning,WANG Li,ZHANG Xiaodong,WU Lei,AO Xiao.Application of variational mode decomposition and energy entropy in denoising of geocentric motion[J].Bulletin of Surveying and Mapping,2020,0(8):59-64. |
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| Authors: | WANG Qingyu DU Ning WANG Li ZHANG Xiaodong WU Lei AO Xiao |
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| Institution: | College of Mining, Guizhou University, Guiyang 550025, China |
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| Abstract: | In order to solve the problems about the noise of geocenter motion time seris, such as the complex types, strong randomness, and difficult to effectively separate from the signal and so on, the net translation method is used to process the weekly solution data from IGS station, which acquires the time series of geocenter motion from 2012 to 2018, and proposing a noise reduction method based on VMD and energy entropy. Firstly, the time series components from high frequency to low frequency in each direction are obtained by decomposing the time series of geocenter motion. Secondly, the energy entropy of each variational modal component is calculated to obtain the boundary between noise and signal. Then the signal component is reconstructed to obtain the time series of geocenter motion after noise reduction.Finally, comparing EMD and EEMD methods, it is shown that the method used in this paper has better effect on noise reduction from correlation coefficient, signal-to-noise ratio, residual energy percentage, variance contribution rate and other parameter evaluation indexes. |
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| Keywords: | geocentric motion time series noise reduction variational mode decomposition energy entropy signal-to-noise ratio |
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