  The presence of undesirable dominating signals in geophysical experimental
data is a challenge in many subfields. One remarkable example is surface
gravimetry, where frequencies from Earth tides correspond to time-series
fluctuations up to a thousand times larger than the phenomena of major
interest, such as hydrological gravity effects or co-seismic gravity changes.
This work discusses general methods for removal of unwanted dominating signals
by applying them to 8 long-period gravity time-series of the International
Geodynamics and Earth Tides Service, equivalent to the acquisition from 8
instruments in 5 locations representative of the network. We compare three
different conceptual approaches for tide removal: frequency filtering, physical
modelling and data-based modelling. Each approach reveals a different
limitation to be considered depending on the intended application. Vestiges of
tides remain in the residues for the modelling procedures, whereas the signal
was distorted in different ways by the filtering and data-based procedures. The
linear techniques employed were power spectral density, spectrogram,
cross-correlation and classical harmonics decomposition, while the system
dynamics was analysed by state-space reconstruction and estimation of the
largest Lyapunov exponent. Although the tides could not be completely
eliminated, they were sufficiently reduced to allow observation of geophysical
events of interest above the $10 \text{ nm s}^{-2}$ level, exemplified by a
hydrology-related event of $60 \text{ nm s}^{-2}$. The implementations adopted
for each conceptual approach are general, so that their principles could be
applied to other kinds of data affected by undesired signals composed mainly by
periodic or quasi-periodic components.
