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|Authors: ||Esposito, A. M.*|
M. G. Martini, M. G.*
|Title: ||Unsupervised Neural Analysis of Very-Long-Period Events at Stromboli Volcano Using the Self-Organizing Maps|
|Title of journal: ||Bulletin of the Seismological Society of America|
|Series/Report no.: ||5/98(2008)|
|Publisher: ||Seismological Society of America|
|Issue Date: ||2008|
|Abstract: ||We have implemented a method based on an unsupervised neural network
to cluster the waveforms of very-long-period (VLP) events associated with
explosive activity at the Stromboli volcano (southern Italy). Stromboli has several
active vents in the summit area producing together more than 200 explosions=day.
We applied this method to investigate the relationship between each vent and its associated
VLP explosive waveform.
We selected 147 VLP events recorded between November and December 2005,
when digital infrared camera recordings were available. From a visual inspection of
the infrared camera images, we classified the VLPs on the basis of which vent produced
each explosion. We then applied the self-organizing map (SOM), an unsupervised
neural technique widely applied in data exploratory analysis, to cluster the VLPs
on the basis of their waveform similarity.
Our analysis demonstrates that the most recurrent VLP waveforms are usually
generated by the same vent. Some exceptions occurred, however, in which different
waveforms are associated with the same vent, as well as different vents generating
similar waveforms. This suggests that the geometry of the upper conduit-vent system
plays a role in shaping the recurring VLP events, whereas occasional modest changes
in the source process dynamics produce the observed exceptions.|
|Appears in Collections:||05.01.99. General or miscellaneous|
04.06.08. Volcano seismology
Papers Published / Papers in press
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