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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/5830

Authors: Galli, L.*
Castellani, C.*
Pace, G.*
Saccorotti, G.*
Editors: Bean, C. J.; chool of Geological Sciences, University College Dublin, Ireland
Braiden, A. K.; School of Geological Sciences, University College Dublin, Ireland
Lokmer, I.; School of Geological Sciences, University College Dublin, Ireland
Martini, F.; School of Geological Sciences, University College Dublin, Ireland
O’Brien, G. S.; School of Geological Sciences, University College Dublin, Ireland
Title: Wavelet decomposition and advanced denoising techniquesn for analysis and classification of seismic signals
Issue Date: 2009
ISBN: 978-1-905254-39-2
Keywords: Wavelet transform
Signal classification
Denoising techniques
Abstract: This work describes an automatic classification procedure for seismic signals suitable for the analysis of complex, broad-band waveforms commonly associated with fluid-rock interaction in volcanic and hydrothermal systems. Based on Discrete Wavelet Transform, a set of significant seismic signal features that characterize the type of event is identified (e.g. noise, volcano tectonic, long period). These features are initially assessed for events whose category (class) can be previously determined by an expert analyst. A Bayesian Pattern Recognition supervised technique based on these features is adopted to classify a new ‘unlabelled pattern’, whose class is unknown. In this way values computed for known events are used to classify events of unknown identity ('supervised classification'). A test was performed on seismological data recorded at Campi Flegrei (Italy), which was divided into three classes. Automatic classification accuracy ranges from 82% to 100% over a broad range of datasets.
Appears in Collections:05.01.01. Data processing
Book chapters
05.01.99. General or miscellaneous
04.06.10. Instruments and techniques

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