Separation of global time-variable gravity signals into maximally independent components
Separation of global time-variable gravity signals into maximally independent components
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DOI：
10.1007/s00190-011-0532-5
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发表时间：
2012-07-01
影响因子：
4.4
通讯作者：
Kusche, J.
中科院分区：
文献类型：
--
作者：
Forootan, E.;Kusche, J.
重力恢复和气候实验（GRACE）产品提供了关于整个地球仪总水储存变化的宝贵信息。由于GRACE检测到垂直柱上的质量变化，因此需要将其总水储存异常分离到其原始来源中。在统计方法中，主成分分析（PCA）方法及其扩展经常被提出来将GRACE产品分解为空间和时间分量。然而，这些方法仅搜索一方面不总是可解释的并且另一方面通常包含独立源信号的叠加的去相关分量。相反，独立分量分析（伊卡）表示一种使用高阶统计信息基于假设的统计独立性来分离分量的技术。如果假设独立的物理过程产生统计上独立的信号分量加起来的GRACE观测，分离它们的ICA是一个可靠的策略来识别这些过程。在本文中，传统的PCA，其旋转扩展和伊卡的性能进行了研究时，适用于GRACE派生的总水储量变化。这些分析已经测试了合成的例子和真实的GRACE水平-2每月的解决方案来自GeoForschungsZentrum波茨坦（GFZ RL 04）和波恩大学（ITG 2010）。在合成的例子中，我们可以展示如何在伊卡的框架中施加统计独立性，提高了从GRACE型叠加中提取“原始”信号。因此，我们有信心，也为真实的情况下的伊卡算法，没有事先假设的长期行为或包含在信号中的频率，提高了性能的PCA和其旋转扩展的周期性和长期成分的分离。
The Gravity Recovery and Climate Experiment (GRACE) products provide valuable information about total water storage variations over the whole globe. Since GRACE detects mass variations integrated over vertical columns, it is desirable to separate its total water storage anomalies into their original sources. Among the statistical approaches, the principal component analysis (PCA) method and its extensions have been frequently proposed to decompose the GRACE products into space and time components. However, these methods only search for decorrelated components that on the one hand are not always interpretable and on the other hand often contain a superposition of independent source signals. In contrast, independent component analysis (ICA) represents a technique that separates components based on assumed statistical independence using higher-order statistical information. If one assumes that independent physical processes generate statistically independent signal components added up in the GRACE observations, separating them by ICA is a reliable strategy to identify these processes. In this paper, the performance of the conventional PCA, its rotated extension and ICA are investigated when applied to the GRACE-derived total water storage variations. These analyses have been tested on both a synthetic example and on the real GRACE level-2 monthly solutions derived from GeoForschungsZentrum Potsdam (GFZ RL04) and Bonn University (ITG2010). Within the synthetic example, we can show how imposing statistical independence in the framework of ICA improves the extraction of the 'original' signals from a GRACE-type super-position. We are therefore confident that also for the real case the ICA algorithm, without making prior assumptions about the long-term behaviour or on the frequencies contained in the signal, improves over the performance of PCA and its rotated extension in the separation of periodical and long-term components.