Extraction and performance analysis of tidal signals from non-stationary continuous gravity data based on TVF-EMD

This is the authors' abstract. We don't add key points for this paper.

SUMMARY The precise extraction of tidal signals from non-stationary gravity observations is a central challenge in geophysics, where accuracy is often limited by mode mixing in data pre-processing algorithms. This study evaluates the performance of the Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD) method to address this issue. We employed a progressive validation pipeline: the method was first verified on simulated signals, then rigorously tested against a high-fidelity benchmark from a superconducting gravimeter, and finally applied to one month of continuous data from an Atom Gravimeter at the Yilan station. Results demonstrate that TVF-EMD dramatically suppresses mode mixing, with the energy of transient spikes in its residual being an order of magnitude lower than that from the conventional Ensemble Empirical Mode Decomposition method. The tidal signal reconstructed by TVF-EMD achieved the highest cross-correlation coefficient and the smallest root mean square error when compared to the theoretical gravity tide. Subsequent harmonic analysis confirmed that TVF-EMD yielded the lowest errors across all major tidal constituents. These findings validate TVF-EMD as a superior pre-processing framework for tidal analysis, particularly for enhancing the reliability of geophysical parameter inversion from short-duration records obtained with next-generation quantum sensors.