Marco Aliotta

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

Interplay between Tectonics and Mount Etna’s Volcanism: Insights into the Geometry of the Plumbing System

Volcanoes are geologic manifestations of highly dynamic and complexly coupled physical and chemical processes in the interior of the Earth. Most volcanism on Earth occurs at plate boundaries in places where tectonic plates move apart (e.g. Iceland) and in places where tectonic plates come together with one plate plunging (subducting) below the other into the mantle (e.g. Pacific ring of fire). Conversely, intraplate volcanism is a type of volcanism occurring far from plate boundaries and whose origins are rather controversial. To know the working mode of a volcano in a given region it is necessary to understand the interplay between tectonics, deformation processes and magma transport through the lithosphere (e.g. Vigneresse, 1999; Petford et al., 2000). Deformation-induced fault-fracture networks have been regarded as efficient pathways through which magma is transported, stored and eventually erupted at the Earth’s surface (e.g. Clemens and Mawer, 1992; Petford et al., 2000). At active volcanoes, magmas rise toward the surface and can stagnate at different levels in the lithosphere, giving rise to magma bodies of different shape and size (Marsh, 2000). Nearly all volcanic eruptions are supplied with magma through dykes and inclined sheets whose initiation and eventual propagation to the surface or, alternatively, arrest at some depth in the volcano, depend on the stress state in the volcano (Gudmundsson, 2006). At the surface of active volcanic edifices, the majority of eruptive fissures have a radial configuration and tangential or oblique fissures are rare. However, within many eroded volcanic edifices, dykes and dyke-fed eruptive fissures commonly have more complex patterns, resulting from regional stresses, magmatic reservoirs, anisotropies or variations in topography (Acocella et al., 2009). Geophysics can provide information on the geometry of plumbing system and magma chambers, as well as on the mechanisms of emplacement of dykes. Among the different branches of geophysics, seismology is the most powerful tool to obtain information about

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.