2021/04/01 by Анна Морозова, Morozova, Anna, Rania Rebbah +1
Earth and Planetary Sciences · Physics and Astronomy · #Earthquake Detection and Analysis #Ionosphere and magnetosphere dynamics #Solar and Space Plasma Dynamics
paper · pdf · doi:10.48550/arxiv.2104.00391
Here we present the results of the comparative analysis of the regular daily\nvariations of the geomagnetic field, Sq and SD, obtained from the observations\nwith two methods: (1) using the so-called geomagnetically quiet day (QD) to\ncalculate mean daily variations and (2) using the principal component analysis\n(PCA) to decompose original series into main variability modes. The original\ngeomagnetic field series were obtained at the Coimbra Magnetic Observatory\n(COI, Portugal) in 01.01.2007-31.12.2017. The data were analyzed separately for\neach of 12 months. The geomagnetic field components X, Y and Z were analyzed\nindependently. The Sq and SD variations obtained with QD were compared with the\nprincipal components from PCA (PCs, daily variation of the geomagnetic field of\ndifferent types) using the correlation analysis. In this study only the PCA\nability to extract daily variations similar to ones acquired using the standard\napproach with QD was analyzed. The results for X, and Y and Z are essentially\ndifferent. The Sq variation is always filtered to the 1st PCA mode for Y and Z,\nwhereas the SD variation is filtered either to the 2nd or 3rd mode. For X the\nSq variation tends to be filtered to the 2nd mode, while the SD variation is\nmore often filtered to the 1st mode. The number of the PCs classified either as\nSq or SD follows seasonal and decadal variations of the geomagnetic activity.\nThe analysis showed that PCA can automatically extract the Sq variation from\nthe observations of Y and Z; however complementary analysis (for example, a\ncomparison to a reference curve) is needed to identify a PC corresponding to\nthe SD variation. For X component, the automatic extraction of the Sq or SD\nvariations is not possible, and the complementary analysis is always needed.\nThe dataset used in this paper is described in detail in a companion paper by\nMorozova et al. (2021a).\n