Placido Montalto

Lava lake surface characterization by thermal imaging: Erta 'Ale volcano (Ethiopia)

The project ‘‘Sviluppo di sistemi di monitoraggio’’ (Dipartimento di Protezione Civile di Regione Sicilia, INGV Catania Section, Italy), the Leverhulme Trust, the BBC, and the Ethiopian Air Force.

Monitoring Seismo-volcanic and Infrasonic Signals at Volcanoes: Mt. Etna Case Study

Volcanoes generate a broad range of seismo-volcanic and infrasonic signals, whose features and variations are often closely related to volcanic activity. The study of these signals is hence very useful in the monitoring and investigation of volcano dynamics. The analysis of seismo-volcanic and infrasonic signals requires specifically developed techniques due to their unique characteristics, which are generally quite distinct compared with tectonic and volcano-tectonic earthquakes. In this work, we describe analysis methods used to detect and locate seismo-volcanic and infrasonic signals at Mt. Etna. Volcanic tremor sources are located using a method based on spatial seismic amplitude distribution, assuming propagation in a homogeneous medium. The tremor source is found by calculating the goodness of the linear regression fit (R2) of the log-linearized equation of the seismic amplitude decay with distance. The location method for long-period events is based on the joint computation of semblance and R2 values, and the location method of very long-period events is based on the application of radial semblance. Infrasonic events and tremor are located by semblance–brightness- and semblance-based methods, respectively. The techniques described here can also be applied to other volcanoes and do not require particular network geometries (such as arrays) but rather simple sparse networks. Using the source locations of all the considered signals, we were able to reconstruct the shallow plumbing system (above sea level) during 2011.

Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

© 2012 Cassisi et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

Insights into Mt. Etna’s Shallow Plumbing System from the Analysis of Infrasound Signals, August 2007–December 2009

Previous studies performed on Mt. Etna on short and discontinuous time intervals indicate the North East Crater (NEC) as the most active source of infrasound. The source mechanism of NEC infrasound events was modeled as a double resonance. This lead to infer the connection between the NEC and both the southeast crater (SEC) and the eruptive fissure (EF), that opened at the beginning of the 2008–2009 eruption. Nevertheless, there are still several open questions that need to be addressed. For instance, the steadiness of NEC event features should be studied, as well as the orderliness of spectral changes of NEC events time-related to eruptive activity of other vents. The investigation of such topics is strongly enhanced by the possibility of analysing infrasound signals during year-long time periods. With this aim about 40,000 infrasound events, recorded at Mt. Etna from August 2007 to December 2009 were analysed by using spectral and location techniques. It was noted in particular that the NEC events featured periods with very steady waveforms and spectral characteristics lasting from days to months with slow or sudden variations. The most important eruptive episodes occurring at the SEC or the EF were accompanied by significant spectral changes in NEC events. In light of such systematic behaviour the connection between the NEC and the SEC/EF plumbing systems was not considered temporary but rather stable even during a relatively long time interval (2006–2009). Moreover, study of NEC event spectral features and their changes over multiple years supports the double resonance source model. Such a model, together with the inferred connections between NEC and SEC/EF feeding systems, implies that level fluctuations of a magma column inside the NEC conduit correspond to magmastatic pressure decrease/increase inside the main plumbing system. These findings open up new and interesting possibilities for monitoring magma pressure changes inside the Mt. Etna plumbing system.

Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna

From January 2011 to December 2015, Mt. Etna was mainly characterized by a cyclic eruptive behavior with more than 40 lava fountains from New South-East Crater. Using the RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area, an automatic recognition of the different states of volcanic activity (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN) has been applied for monitoring purposes. Since values of the RMS time series calculated on the seismic signal are generated from a stochastic process, we can try to model the system generating its sampled values, assumed to be a Markov process, using Hidden Markov Models (HMMs). HMMs analysis seeks to recover the sequence of hidden states from the observations. In our framework, observations are characters generated by the Symbolic Aggregate approXimation (SAX) technique, which maps RMS time series values with symbols of a pre-defined alphabet. The main advantages of the proposed framework, based on HMMs and SAX, with respect to other automatic systems applied on seismic signals at Mt. Etna, are the use of multiple stations and static thresholds to well characterize the volcano states. Its application on a wide seismic dataset of Etna volcano shows the possibility to guess the volcano states. The experimental results show that, in most of the cases, we detected lava fountains in advance.

Motif Discovery on Seismic Amplitude Time Series: The Case Study of Mt Etna 2011 Eruptive Activity

Algorithms searching for similar patterns are widely used in seismology both when the waveforms of the events of interest are known and when there is no a priori-knowledge. Such methods usually make use of the cross-correlation coefficient as a measure of similarity; if there is no a-priori knowledge, they behave as brute-force searching algorithms. The disadvantage of these methods, preventing or limiting their application to very large datasets, is computational complexity. The Mueen–Keogh (MK) algorithm overcomes this limitation by means of two optimization techniques—the early abandoning concept and space indexing. Here, we apply the MK algorithm to amplitude time series retrieved from seismic signals recorded during episodic eruptive activity of Mt Etna in 2011. By adequately tuning the input to the MK algorithm we found eight motif groups characterized by distinct seismic amplitude trends, each related to a different phenomenon. In particular, we observed that earthquakes are accompanied by sharp increases and decreases in seismic amplitude whereas lava fountains are accompanied by slower changes. These results demonstrate that the MK algorithm, because of its particular features, may have wide applicability in seismology.

DBStrata: a system for density-based clustering and outlier detection based on stratification

Clustering is a widely used unsupervised data mining technique. In density-based clustering, a cluster is defined as a connected dense component and grows in the direction set by the density. In this paper we present a software system called DBStrata that implements the density-based clustering architecture together with several extensions able to boost the clustering performances and to efficiently identify outliers.