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International Institute for Advanced Scientific Studies, Vietri sul Mare
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- PublicationRestrictedAutomatic Discrimination of Earthquakes and False Events in Seismological Recording for Volcanic Monitoring(2002)
; ; ; ; ; ;Ezin, E. C.; Institut de Math´ematiques et de Sciences Physiques ;Giudicepietro, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia ;Petrosino, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia ;Scarpetta, S.; Dipartimento di Fisica “E.R.Caianiello”, Universita di Salerno ;Vanacore, A.; International Institute for Advanced Scientific Studies (SA); ; ; ; ; ; ; ;Marinaro, M.; Dipartimento di Fisica “E.R.Caianiello”, Universita di Salerno ;Tagliaferri, R.; Università di Salerno; This paper reports on the classification of earthquakes and false events (thunders, quarry blasts and man-made undersea explosions) recorded by four seismic stations in the Vesuvius area in Naples, Italy. For each station we set up a specialized neural classifier, able to discriminate the two classes of events recordered by that station. Feature extraction is done using both the linear predictor coding technique and the waveform features of the signals. The use of properly normalized waveform features as input for the MLP network allows the network to better generalize compared to our previous strategy applied to a similar problem [2]. To train the MLP network we compare the performance of the quasi-Newton algorithm and the scaled conjugate gradient method. On one hand, we improve the strategy used in [2] and on the other hand we show that it is not specific to the discrimination task [2] but has a larger range of applicability191 25 - PublicationOpen AccessAutomatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy, Using Neural Networks(2005)
; ; ; ; ; ; ; ;Scarpetta, S.; Università di Salerno, INFM & Dipartimento di Fisica “E. R. Caianiello” ;Giudicepietro, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia ;Ezin, E. C.; International Institute for Advanced Scientific Studies, Vietri sul Mare ;Petrosino, S.; Institut de Mathematiques et de Sciences Physiques, Benin ;Del Pezzo, E.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia ;Martini, M.; Institut de Mathematiques et de Sciences Physiques, Benin ;Marinaro, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia; ; ; ; ; ; We present a new strategy for reliable automatic classification of local seismic signals and volcano-tectonic earthquakes (VT). The method is based on a supervised neural network in which a new approach for feature extraction from short period seismic signals is applied. To reduce the number of records required for the analysis we set up a specialized neural classifier, able to distinguish two classes of signals, for each of the selected stations. The neural network architecture is a multilayer perceptron (MLP) with a single hidden layer. Spectral features of the signals and the parameterized attributes of their waveform have been used as input for this network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the MLP network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude VT events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (VT versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications.266 1057