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Denoising Gravity and Geomagnetic Signals from Etna Volcano (Italy) Using Multivariate Methods : Volume 15, Issue 5 (21/10/2008)

By Del Negro, C.

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Book Id: WPLBN0003980924
Format Type: PDF Article :
File Size: Pages 15
Reproduction Date: 2015

Title: Denoising Gravity and Geomagnetic Signals from Etna Volcano (Italy) Using Multivariate Methods : Volume 15, Issue 5 (21/10/2008)  
Author: Del Negro, C.
Volume: Vol. 15, Issue 5
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2008
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Greco, F., Nunnari, G., Napoli, R., & Negro, C. D. (2008). Denoising Gravity and Geomagnetic Signals from Etna Volcano (Italy) Using Multivariate Methods : Volume 15, Issue 5 (21/10/2008). Retrieved from http://hawaiilibrary.net/


Description
Description: Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania, Italy. 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.

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

Excerpt
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