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  • 1
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    Unsupervised Neural Analysis of Very-Long-Period Events at Stromboli Volcano Using the Self-Organizing Maps (2008)
    Seismological Society of America
    Details
    Publication Date: 2017-04-04
    Description: 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.
    Description: Published
    Description: 2449–2459
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: JCR Journal
    Description: reserved
    Keywords: Stromboli ; Maps ; 04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology ; 05. General::05.01. Computational geophysics::05.01.99. General or miscellaneous
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
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    Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy, Using Neural Networks (2005)
    Details
    Publication Date: 2017-04-04
    Description: 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.
    Description: Published
    Description: 185-196
    Description: open
    Keywords: Seismic signals ; Vesuvius ; Automatic classification ; Volcano-tectonic earthquakes ; 04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Format: 469 bytes
    Format: 1473591 bytes
    Format: text/html
    Format: application/pdf
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