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Automatic Discrimination among Landslide, Explosion-Quake, and Microtremor Seismic Signals at Stromboli Volcano Using Neural Networks

¹ Istituto Nazionale di Geofisica e Vulcanologia
Sezione di Napoli (Osservatorio Vesuviano)
Napoli, 80124
Italy
 (A.M.E., F.G., L.D’A., M.M.)
² Dip. di Fisica "E.R. Caianiello"
Univ. di Salerno
Baronissi (SA), 84081
Italy
 (A.M.E., S.S., M.M.)
³ INFM Sez. di Salerno and INFN Gruppo Coll. di Salerno
Baronissi (SA), 84081
Italy
 (S.S., M.M.)
⁴ Istituto Internazionale per gli Alti Studi Scientifici (IIASS)
Vietri sul Mare (SA), 84019
Italy
 (M.M.)

In 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 to obtain 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 parametrization 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 parametrization technique characterizes the different classes of data appropriately.

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