Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/6629
AuthorsDe Rubeis, V.* 
Vinciguerra, S.* 
Tosi, P.* 
Sbarra, P.* 
Benson, P. M.* 
TitleAcoustic Emission spectra classification from rock samples of Etna basalt in deformation-decompression laboratory experiments
Issue Date2010
URIhttp://hdl.handle.net/2122/6629
ISBN978-3-642-12299-6
Keywordsacoustic emissions
Subject Classification04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology 
04. Solid Earth::04.08. Volcanology::04.08.05. Volcanic rocks 
05. General::05.01. Computational geophysics::05.01.04. Statistical analysis 
AbstractRecent laboratory experiments on Etna basalt have permitted the generation of an extensive catalogue of acoustic emissions (AE) during two key experimental phases. Firstly, AE have been generated during triaxial compressional tests and formation of a complex fracture/damage zone. Secondly, rapid fluid decompression through the damage/shear zone after failure. We report new results from an advanced analysis method using AE spectrograms, allowing us to qualitatively identify high and low frequency events; essentially comparable to seismicity in volcanic areas. Our analysis, for the first time, quantitatively classifies ‘families’ of AE events belonging to the same experimental stage without prior knowledge. We then test the method using the AE catalogue for verification, which is not possible with field data. FFT spectra, obtained from AE, are subdivided into equal log intervals for which a local slope is calculated. Factor analysis has been then applied, in which we use a data matrix of columns representing the variables considered (frequency data averaged in bins) vs. rows indicating each AE data set. Factor analysis shows that the method is very effective and suitable for reducing data complexity, allowing distinct factors to be obtained. We conclude that most of the data variance (information content) can be well represented by three factors only, each one representing a well defined frequency range. Through the factor scores it is possible to represent data in a lower dimension factor space. Classification is then possible by identifying clusters of AE belonging to the same experimental stage. This allows us to propose a deformation/decompression interpretation based solely on the AE frequency analysis and to identify a third type of AE related to fluid movements in the deformation stage.
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