2021/12/03 by Анна Морозова, Morozova, Anna, Rania Rebbah +1
Computer Science · Earth and Planetary Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Earthquake Detection and Analysis #FOS: Physical sciences #Geochemistry and Geologic Mapping #Geophysics (physics.geo-ph)
paper · pdf · doi:10.48550/arxiv.2112.01827
openalex publication_date 2021/12/03 · openalex created_date 2022/07/15 · openalex updated_date 2026/07/28
In this paper, we analyze the applicability of the principal component\nanalysis (PCA) as a tool to extract the Sq variation of the geomagnetic field.\nWe tested different geomagnetic field components and used data measured at\ndifferent levels of the solar and geomagnetic activity and during different\nmonths. Geomagnetic field variations obtained with PCA were classified as SqPCA\nusing two types of reference series: SqIQD series calculated using\ngeomagnetically quiet days and simulations of the ionospheric field with\nmodels. The results for the X and Y and Z components are essentially different.\nThe Sq variation is always filtered to the first PCA mode for the Y and Z\ncomponents. Thus, PCA can automatically extract the Sq variation from the\nobservations of the Y and Z components of the geomagnetic field. For the X\ncomponent, the automatic extraction of the Sq variation is not possible, and a\ncomplimentary analysis, like a comparison to a reference series, is always\nneeded. We tested two types of reference series: the mean SqIQD and the outputs\nof the CM5 and DIFI3 models. Our results show that both the data-based and\nmodel-based reference series can be used but the DIFI3 model performs better.\nWe also recommend estimating the similarity of the series not with the\ncorrelation analysis but using metrics that account for possible local\nstretching/compressing of the compared series, for example, the dynamic time\nwarping (DTW) distance.\n