Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/6373
AuthorsNapoli, R.* 
Pistorio, A.* 
Scandura, D.* 
Currenti, G.* 
Greco, F.* 
Del Negro, C.* 
TitleVectorial magnetometers for noise reduction in volcanomagnetic monitoring at Mt Etna
Issue Date2010
URIhttp://hdl.handle.net/2122/6373
Keywordsvectorial magnetometer
volcanomagnetic monitoring
Subject Classification04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring 
AbstractThe volcanomagnetic monitoring is critically dependent on the ability to detect and isolate magnetic variations related to volcanic activity. Accurate detection of volcanomagnetic anomalies attributable to the volcano’s dynamics requires removing 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 partially successful. Variations in the difference fields arise principally from contrasting electromagnetic properties at magnetometer sites. To improve the noise reduction of geomagnetic data from 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 disturbance field. The filter is able to estimate and rectify the model parameters continuously by means of new observations, so that predictions match the observed data. The error of state estimation has been decreased and the filtering accuracy improved. Experimental data collected on Mt Etna during 2010 are analyzed 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.
Appears in Collections:Manuscripts

Files in This Item:
File Description SizeFormat 
s1-ln857452595844769-1939656818Hwf2071127690IdV-12609763298574525PDF_HI0001.pdf1.45 MBAdobe PDFView/Open
Show full item record

Page view(s)

64
checked on Apr 27, 2017

Download(s)

159
checked on Apr 27, 2017

Google ScholarTM

Check