This study summarizes the results obtained by using a processing method based on wavelet
transform for noise-filtering of continuous gravity data. Continuous gravity recordings in vol-
canic area could play a fundamental role in the monitoring of active volcanoes and in the
prediction of eruptive events too. This geophysical methodology is used, on active volcanoes,
in order to detect mass changes linked to magma transfer processes and, thus, to recognize
forerunners to paroxysmal volcanic events. Spring gravimeters are still the most utilized in-
struments for this type of microgravity studies. Unfortunately, spring gravity meters show a
strong influence of meteorological parameters, especially in the adverse environmental condi-
tions usually encountered at such places. As the gravity changes due to the volcanic activity are
very small compared to other geophysical or instrumental effects, we need a new mathematical
tool to get reliable gravity residuals susceptible to reflect the volcanic effect. The aim of the
present work is to get a first evaluation about the comparison between the traditional filtering
methodology and the wavelet transform. The overall results show that the performance of the
wavelet-based filter seems better than the Fourier one. Moreover, the possibility of getting a
multi-resolution analysis and study local features of the signal in the time domain makes the
proposed methodology a valuable tool for gravity data processing
