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...
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Copernicus Publications
2008-10-01
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Series: | Nonlinear Processes in Geophysics |
Online Access: | http://www.nonlin-processes-geophys.net/15/735/2008/npg-15-735-2008.pdf |
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doaj-b76c92392ba847b393ce8d42984b16212020-11-24T23:03:25ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462008-10-01155735749Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methodsC. Del NegroF. GrecoR. NapoliG. NunnariMultivariate 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. http://www.nonlin-processes-geophys.net/15/735/2008/npg-15-735-2008.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
C. Del Negro F. Greco R. Napoli G. Nunnari |
spellingShingle |
C. Del Negro F. Greco R. Napoli G. Nunnari Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methods Nonlinear Processes in Geophysics |
author_facet |
C. Del Negro F. Greco R. Napoli G. Nunnari |
author_sort |
C. Del Negro |
title |
Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methods |
title_short |
Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methods |
title_full |
Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methods |
title_fullStr |
Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methods |
title_full_unstemmed |
Denoising gravity and geomagnetic signals from Etna volcano (Italy) using multivariate methods |
title_sort |
denoising gravity and geomagnetic signals from etna volcano (italy) using multivariate methods |
publisher |
Copernicus Publications |
series |
Nonlinear Processes in Geophysics |
issn |
1023-5809 1607-7946 |
publishDate |
2008-10-01 |
description |
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. |
url |
http://www.nonlin-processes-geophys.net/15/735/2008/npg-15-735-2008.pdf |
work_keys_str_mv |
AT cdelnegro denoisinggravityandgeomagneticsignalsfrometnavolcanoitalyusingmultivariatemethods AT fgreco denoisinggravityandgeomagneticsignalsfrometnavolcanoitalyusingmultivariatemethods AT rnapoli denoisinggravityandgeomagneticsignalsfrometnavolcanoitalyusingmultivariatemethods AT gnunnari denoisinggravityandgeomagneticsignalsfrometnavolcanoitalyusingmultivariatemethods |
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