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| Titel |
Automatic estimation of optimal autoregressive filters for the analysis of volcanic seismic activity |
| VerfasserIn |
P. Lesage |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 8, no. 2 ; Nr. 8, no. 2 (2008-04-24), S.369-376 |
| Datensatznummer |
250005416
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| Publikation (Nr.) |
 copernicus.org/nhess-8-369-2008.pdf |
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| Zusammenfassung |
| Long-period (LP) events observed on volcanoes provide important information
for volcano monitoring and for studying the physical processes in magmatic
and hydrothermal systems. Of all the methods used to analyse this kind of
seismicity, autoregressive (AR) modelling is particularly valuable, as it
produces precise estimations of the frequencies and quality factors of the
spectral peaks that are generated by resonance effects at seismic sources
and, via deconvolution of the observed record, it allows the excitation
function of the resonator to be determined. However, with AR modelling
methods it is difficult to determine the order of the AR filter that will
yield the best model of the signal. This note presents an algorithm to
overcome this problem, together with some examples of applications. The
approach described uses the kurtosis (fourth order cumulant) of the
deconvolved signal to provide an objective criterion for selecting the
filter order. This approach allows the partial automation of the AR analysis
and thus provides interesting possibilities for improving volcano monitoring
methods. |
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