Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/6793
AuthorsEsposito, A.* 
Giudicepietro, F.* 
D’Auria, L.* 
Peluso, R.* 
Martini, M.* 
TitleA Neural-based Algorithm for Landslide Detection at Stromboli Volcano: Preliminary Results.
Issue Date27-May-2011
URIhttp://hdl.handle.net/2122/6793
KeywordsLandslides
detection
neural network,
seismic signals
Subject Classification04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring 
05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks 
AbstractThis study presents a neural-based algorithm for the automatic detection of landslides on Stromboli volcano (Italy). It has been shown that landslides are an important short-term precursor of effusive eruptions of Stromboli. In particular, an increase in the occurrence rate of landslides was observed a few hours before the beginning of the February 2007 effusive eruption. Automating the process of detection of these signals will help analysts and represents a useful tool for the monitoring of the stability of the Sciara del Fuoco flank of Stromboli volcano. A multi-layer perceptron neural network is here applied to continuously discriminate landslides from other signals recorded at Stromboli (e.g., explosion quakes, tremor signals), and its output is used by an automatic system for the detection task. To correctly represent the seismic data, coefficients are extracted from both the frequency domain, using the linear predictive coding technique, and the time domain, using temporal waveform parameterization. The network training and testing was carried out using a dataset of 537 signals, from 267 landslides and 270 records that included explosion quakes and tremor signals. The classification results were 99.5% predictive for the best net performance, and 98.7% when the performance was averaged over the different net configurations. Thus, this detection system was effective when tested on the 2007 effusive eruption period. However, continuing investigations into different time intervals are needed, to further define and optimize the algorithm.
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