The application of a denoising method aimed at reducing continuous gravity data

Abstract

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.