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Authors: GIACCO, F.*
Title: Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano
Editors: Apolloni, B.; Università degli Studi di Milano, Dipartimento di Scienze dell’Informazione,Milano, Italy
Bassis, S.; Università degli Studi di Milano, Dipartimento di Scienze dell’Informazione,Milano, Italy
Morabito, C.F.; Università di Reggio Calabria, IMET, Loc. Feo di Vito,Reggio Calabria, Italy
Issue Date: 28-May-2009
Keywords: Seismic signals discrimination,
Linear Predictive Coding,
Support Vector Machine,
Multilayer Perceptron.
Abstract: We applied and compared two supervised pattern recognition techniques, namely the Multilayer Perceptron (MLP) and Support Vector Machine (SVM), to classify seismic signals recorded on Stromboli volcano. The available data are firstly preprocessed in order to obtain a compact representation of the raw seismic signals. We extract from data spectral and temporal information so that each input vector is made up of 71 components, containing both spectral and temporal information extracted from the early signal. We implemented two classification strategies to discriminate three different seismic events: landslide, explosion-quake, and volcanic microtremor signals. The first method is a two-layer MLP network, with a Cross-Entropy error function and logistic activation function for the output units. The second method is a Support Vector Machine, whose multi-class setting is accomplished through a 1vsAll architecture with gaussian kernel. The experiments show that although the MLP produces very good results, the SVM accuracy is always higher, both in term of best performance, 99.5%, and average performance, 98.8%, obtained with different sampling permutations of training and test sets
Appears in Collections:Conference materials
05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks

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