Abstract
Time-varying gravity signals, with their nonlinear, non-stationary and multi-scale characteristics, record the physical responses of various geodynamic processes and consist of a blend of signals with various periods and amplitudes, corresponding to numerous phenomena. Superconducting gravimeter (SG) records are processed in this study using a multi-scale analytical method and corrected for known effects to reduce noise, to study geodynamic phenomena using their gravimetric signatures. Continuous SG (GWR-C032) gravity and barometric data are decomposed into a series of intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD) method, which is proposed to alleviate some unresolved issues (the mode mixing problem and the end effect) of the empirical mode decomposition (EMD). Further analysis of the variously scaled signals is based on a dyadic filter bank of the IMFs. The results indicate that removing the high-frequency IMFs can reduce the natural and man-made noise in the data, which are caused by electronic device noise, Earth background noise and the residual effects of pre-processing. The atmospheric admittances based on frequency changes are estimated from the gravity and the atmospheric pressure IMFs in various frequency bands. These time-and frequency-dependent admittance values can be used effectively to improve the atmospheric correction. Using the EEMD method as a filter, the long-period IMFs are extracted from the SG time-varying gravity signals spanning 7 years. The resulting gravity residuals are well correlated with the gravity effect caused by the Earth's polar motion after correcting for atmospheric effects.
Highlights
Continuous observations of superconducting gravimeters (SGs) deployed by the Global Geodynamics Project (GGP) provide valuable information on the environmental changes and geodynamic processes
In analysing the long-period gravity signals caused by polar motion and surface loading, we considered 7 years high-quality gravity and barometric records from the Wuhan Superconducting gravimeter (SG) station to confirm that the ensemble empirical mode decomposition (EEMD) method is suitable for extracting the long-period gravity and pressure components of SG signals
EEMD was used in this study to identify and analyse surface SG gravimetric and atmospheric pressure time series data
Summary
Continuous observations of superconducting gravimeters (SGs) deployed by the Global Geodynamics Project (GGP) provide valuable information on the environmental changes and geodynamic processes. A global network of SGs. A global network of SGs This makes SGs extremely useful, but requires much care during processing, in order to be able to separate the numerous contributions. The systematic study of one or several signal components in observed gravity time series requires the subtraction of contributions that can be calculated using the theoretical model. Subjecting the residual signals to more subsequent detailed refinement or interpretation is required to provide better analysis of the various contributions in terms of amplitude (nm s-2) versus period. The multi-scale analysis method is helpful in distinguishing the separate gravimetric signals of various periods
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