Wavelet multi-resolution analysis for the local separation of microgravity anomalies at Etna volcano
A microgravity 14-year-long data set (October 1994 – September 2007) recorded along a 24-kilometer East-West trending profile of 19 stations was analyzed to detect underground mass redistributions related to the volcanic activity involving the southern flank of Mt. Etna volcano (Italy). An important issue with the above data-set is the need of separating the useful signal (i.e. the volcano-related one) from unwanted components (instrumental, human-made, seasonal and other kinds of noise). To filter the gravity data-set from these last components we propose the wavelet multi-resolution analysis. This method provides a good separation of the long period component from the short period one, and allows exploring the local features of the signal with a detail matched to their characteristic scale. Using the discrete wavelet transform (DWT), the gravity data are decomposed into a low-resolution approximation level and several detail levels. Once the useful signal has been suitably separated from the noise, the residual space-time-image evidences, over the studied area and time-interval, (i) recurrences in both space (i.e. zones under which mass redistributions occur more frequently) and time (i.e. cyclic processes) and (ii) microgravity anomalies correlated with the ensuing volcanic activity.