Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methods

Multivariate methods were applied to denoise the gravity and geomagnetic signals continuously recorded by the permanent monitoring networks on the Etna volcano. Gravity and geomagnetic signals observed in volcanic areas are severely influenced by meteorological variables (i.e. pressure, temperature and humidity), whose disturbances can make the detection of volcanic source effects more difficult. For volcano monitoring it is necessary, therefore, to reduce the effects of these perturbations. To date filtering noise is a very complex problem since the spectrum of each noise component has wide intervals of superposition and, some times, traditional filtering techniques provide unsatisfactory results. We propose the application of two different approaches, the adaptive neuro-fuzzy inference system (ANFIS) and the Independent Component Analysis (ICA) to remove noise effects from gravity and geomagnetic time series. Results suggest a good efficiency of the two proposed approaches since they are capable of finding and effectively representing the underlying factors or sources, and allow local features of the signal to be detected.