Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/2274
AuthorsEsposito, A. M.* 
Giudicepietro, F.* 
Scarpetta, S.* 
D'Auria, L.* 
Marinaro, M.* 
Martini, M.* 
TitleAutomatic Discrimination among Landslide, Explosion-Quake, and Microtremor Seismic Signals at Stromboli Volcano using Neural Networks
Issue Date2006
Series/Report no.4/96 (2006)
DOI10.1785/0120050097
URIhttp://hdl.handle.net/2122/2274
KeywordsNONE
Subject Classification04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology 
AbstractIn this article we report on the implementation of an automatic system for discriminating landslide seismic signals on Stromboli island (southern Italy). This is a critical point for monitoring the evolution of this volcanic island, where at the end of 2002 a violent tsunami occurred, triggered by a big landslide. We have devised a supervised neural system to discriminate among landslide, explosion-quake, and volcanic microtremor signals. We first preprocess the data using a compact representation of the seismic records. Both spectral features and amplitude-versus-time information have been extracted from the data to characterize the different types of events. As a second step, we have set up a supervised classification system, trained using a subset of data (the training set) and tested on another data set (the test set) not used during the training stage. The automatic system that we have realized is able to correctly classify 99% of the events in the test set for both explosion-quake/ landslide and explosion-quake/microtremor couples of classes, 96% for landslide/ microtremor discrimination, and 97% for three-class discrimination (landslides/ explosion-quakes/microtremor). Finally, to determine the intrinsic structure of the data and to test the efficiency of our parameterization strategy, we have analyzed the preprocessed data using an unsupervised neural method. We apply this method to the entire dataset composed of landslide, microtremor, and explosion-quake signals. The unsupervised method is able to distinguish three clusters corresponding to the three classes of signals classified by the analysts, demonstrating that the parameterization technique characterizes the different classes of data appropriately.
Appears in Collections:Papers Published / Papers in press

Files in This Item:
File Description SizeFormat 
997.pdf830.3 kBAdobe PDFView/Open
Show full item record

Page view(s)

127
Last Week
0
Last month
2
checked on Aug 18, 2017

Download(s)

37
checked on Aug 18, 2017

Google ScholarTM

Check

Altmetric