Separation of global time-variable gravity signals into maximally independent components

Forootan, Ehsan ORCID: https://orcid.org/0000-0003-3055-041X and Kusche, J. 2011. Separation of global time-variable gravity signals into maximally independent components. Journal of Geodesy 86 (7) , pp. 477-497. 10.1007/s00190-011-0532-5

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Earth and Environmental Sciences
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences
Publisher:	Springer Verlag
ISSN:	0949-7714
Date of Acceptance:	2012
Last Modified:	01 Nov 2022 11:24
URI:	https://orca.cardiff.ac.uk/id/eprint/94862

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