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首页> 外文会议>International Conference on Natural Computation;ICNC '09 >Wavelet Correlation Analysis of Geodetic Signals
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Wavelet Correlation Analysis of Geodetic Signals

机译：大地信号的小波相关分析

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摘要

Classical correlation reflects linear relation between two signals completely in frequency domain, while wavelet correlation, introducing scale parameter to the classical one, is studied to realize similarity degree analysis both in time domain and frequency domain. So the latter is superior to analyzing correlation between two non-stationary geodetic signals. Wavelet correlation can detect similarity degree at different frequencies and delays and give expression to delay information while the correlation obtains maximum at a certain frequency. In order to determine the scales in which feature information lies, wavelet spectrum, combining wavelet transform and Fourier spectrum analysis, is used to explore feature information at different scales. It can be seen cycle informationȁ4; cycles of a month, a season, half of a year, and a yearȁ4;hidden in signals from stations in Shandong through wavelet spectrum analysi. Then linear relation between signals from two stations at three directions, North, East and Up, is analyzed by wavelet correlation respectively.
机译：经典相关性在频域中完全反映了两个信号之间的线性关系，而小波相关性则在经典域中引入了比例参数，以实现时域和频域的相似度分析。因此后者优于分析两个非平稳大地信号之间的相关性。小波相关可以在不同的频率和延迟下检测相似度，并在延迟在一定频率下获得最大值的同时表达延迟信息。为了确定特征信息所处的尺度，小波谱结合了小波变换和傅立叶谱分析，用于探索不同尺度下的特征信息。可以看出周期信息ȁ4;一个月，一个季节，半年，一年的周期ȁ4;通过小波频谱分析将山东站的信号隐藏起来。然后分别通过小波相关性分析来自北，东和上三个方向的两个站点的信号之间的线性关系。

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