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Earth Gravity Tide Signal Decomposition Model Based on PCA and Geophysical Information Extraction

机译:基于PCA和地球物理信息提取的地球重力潮汐信号分解模型

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Principal Component Analysis (PCA) is a main approach based on the second order statistics. It can remove the correlation between the signal components. PCA has been widely used in blind source separation and received attention because of its potential application in signal processing. PCA's principle algorithm, its simulation steps and its application in the earth gravity tide signal are introduced. The decomposition model can help us to analyze the geophysical information in signal. The result shows that PCA is a potential method in the earth gravity tide signal. So we can extract useful geophysical information from it to understand the interior structure of the earth and earthquake precursors.
机译:主成分分析(PCA)是基于二阶统计的主要方法。它可以消除信号分量之间的相关性。 PCA已广泛应用于盲源分离并受到关注,因为其在信号处理中应用。介绍了PCA的原理算法,其仿真步骤及其在地球重力潮汐信号中的应用。分解模型可以帮助我们分析信号中的地球物理信息。结果表明,PCA是地球重力潮汐信号中的潜在方法。因此,我们可以从中提取有用的地球物理信息来理解地球和地震前体的内部结构。

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