Design and application of an adaptive nonstationary filter for noise reduction in volcanomagnetic monitoring at Mt Etna

Abstract

Volcanomagnetic monitoring is critically dependent on the ability to detect and isolate local magnetic variations related to volcanic activity. Accurate detection of volcanomagnetic anomalies attributable to the dynamics of volcanoes requires removal from measurements of the Earth’s magnetic field, fluctuations of external origin which may be up to hundreds of nanotesla during geomagnetic storms. The commonly used method of taking simple differences of the total intensity with respect to the simultaneous value at a remote reference is only partially successful. Variations in the difference fields are thought to arise principally from contrasting electromagnetics of rock properties at magnetometer sites. With the aim of improving the noise reduction of geomagnetic time series from the magnetic network of Mt Etna, we developed an adaptive filtering. Magnetic vector data are included as input to the filter, to account for the orientation of the magnetic field. The filter is able to estimate and adapt the model parameters continuously by means of the new observations, so that the estimated signal closely matches the observed data. Therefore, the filtering accuracy is improved in order to reduce the residual components. Experimental data collected on Mt Etna during 2010 are analysed to relate the field variation at a given station to the field at other sites, filtering out undesired noise and enhancing signal-to-noise ratio.