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

摘要: The Gravity Recovery and Climate Experiment (GRACE) products provide valuable information about total water storage variations over the whole globe. Since GRACE detects mass integrated vertical columns, it is desirable to separate its anomalies into their original sources. Among statistical approaches, principal component analysis (PCA) method extensions have been frequently proposed decompose space time components. However, these methods only search for decorrelated components that on one hand are not always interpretable other often contain a superposition of independent source signals. In contrast, (ICA) represents technique separates based assumed independence using higher-order information. If assumes physical processes generate statistically signal added up in observations, separating them by ICA reliable strategy identify processes. this paper, performance conventional PCA, rotated extension investigated when applied GRACE-derived variations. These analyses tested both synthetic example real level-2 monthly solutions derived from GeoForschungsZentrum Potsdam (GFZ RL04) Bonn University (ITG2010). Within example, we can show how imposing framework improves extraction ‘original’ signals GRACE-type super-position. We therefore confident also case algorithm, without making prior assumptions long-term behaviour or frequencies contained signal, PCA separation periodical