Search

Abstract: Aim of this paper is to identify variations in Very-Long-Period (VLP) source associated with eruptive style changes at Stromboli volcano (Italy) and to retrieve information about the shallow plumbing system that sustains the eruptive activity. We have considered a dataset of 74493 VLP events recorded during the period from January through August 2007, when an effusive eruption occurred (February 27–April 2). We performed a polarization analysis of the entire dataset and divided the considered period into four sub-periods on the basis of polarization characteristics. We then located the events and selected a subset of these events by applying a location quality threshold. The high quality locations demonstrate that during the effusive eruption the VLP sources first moved downward and then moved southwestward. To retrieve information about the geometry of the structures where the source processes take place, we further consider a subset of events and estimate their source mechanisms by using a moment tensor source function (MTSF) inversion technique. Inversion of the waveforms of the VLP events that occurred on February 27 allows us to obtain information about the dynamics of different source centroids distributed along different portions of the shallow magmatic conduits. The structure defined by the locations and source mechanisms shows a greater complexity compared with previous studies and their time variations give an insight into the kinematics of the eruption. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]