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The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is an Italian research institution with focus on earth sciences. Moreover, the INGV is the operational center for seismic surveillance and earthquake monitoring in Italy and is a part of the civil protection system as a center of expertise on seismic, volcanic, and tsunami risks.INGV operates the Italian National Seismic Network and other networks at national scale and is a primary node of the European Integrated Data Archive for archiving and distributing strong-motion and weak-motion seismic recordings. In the control room in Rome, INGV staff performs seismic surveillance and tsunami warning services; in Catania and Naples, the control rooms are devoted to volcanic surveillance. Volcano monitoring includes locating earthquakes in the regions around the Sicilian (Etna, Eolian Islands, and Pantelleria) and the Campanian (Vesuvius, Campi Fregrei, and Ischia) active volcanoes. The tsunami warning is based on earthquake location and magnitude (M) evaluation for moderate to large events in the Mediterranean region and also around the world. The technologists of the institute tuned the data acquisition system to accomplish, in near real time, automatic earthquake detection, hypocenter and magnitude determination, and evaluation of several seismological products (e.g., moment tensors and ShakeMaps). Database archiving of all parametric results is closely linked to the existing procedures of the INGV seismic surveillance environment and surveillance procedures. Earthquake information is routinely revised by the analysts of the Italian seismic bulletin. INGV provides earthquake information to the Department of Civil Protection (Dipartimento di Protezione Civile) to the scientific community and to the public through the web and social media. We aim at illustrating different aspects of earthquake monitoring at INGV: (1) network operations; (2) organizational structure and the hardware and software used; and (3) communication, including recent developments and planned improvements.

During the COVID-19 pandemic, most countries put in place social interventions, restricting the mobility of citizens, to slow the spread of the epidemic. Italy, the first European country severely impacted by the COVID-19 outbreak, applied a sequence of progressive restrictions to reduce human mobility from the end of February to mid-March 2020. Here, we analysed the seismic signatures of these lockdown measures in densely populated eastern Sicily, characterized by the presence of a permanent seismic network used for earthquake and volcanic monitoring. We emphasize how the anthropogenic seismic noise decrease is visible even at stations located in remote areas (Etna and Aeolian Islands) and that the amount of this reduction (reaching ∼ 50 %–60 %), its temporal pattern and spectral content are strongly station-dependent. Concerning the latter, we showed that on average the frequencies above 10 Hz are the most influenced by the anthropogenic seismic noise. We found similarities between the temporal patterns of anthropogenic seismic noise and human mobility, as quantified by the mobile-phone-derived data shared by Google, Facebook and Apple, as well as by ship traffic data. These results further confirm how seismic data, routinely acquired worldwide for seismic and volcanic surveillance, can be used to monitor human mobility too.

During the COVID-19 pandemic, most countries worldwide put in place social interventions, consisting of restricting the mobility of citizens, aimed at slowing and mitigating the spread of the epidemic. In particular, Italy, as the first European country violently struck by the COVID-19 outbreak, applied a sequence of progressive restrictions to reduce both human mobility and human-to-human contacts from the end of February to mid-March 2020. Here, we analysed the seismic signatures of these lockdown measures in the densely populated Eastern Sicily, characterised by the presence of a permanent seismic network used for both seismic and volcanic monitoring. We specifically emphasize how the amount of the amplitude reduction of anthropogenic seismic noise (reaching ~50–60%), its temporal pattern and spectral content are strongly station-dependent. As for the latter, we exhibited that on average the frequencies above 10 Hz are the most influenced by the anthropogenic seismic noise. Finally, we found an impressive similarity between the temporal patterns of anthropogenic seismic noise and human mobility, as quantified by the mobile phone-derived data shared by Google, Facebook and Apple. These results further confirm how seismic data, routinely acquired worldwide for seismic and volcanic surveillance, can be used to monitor human mobility too.

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.

During explosive eruptions, emergency responders and government agencies need to make fast decisions that should be based on an accurate forecast of tephra dispersal and assessment of the expected impact. Here, we propose a new operational tephra fallout monitoring and forecasting system based on quantitative volcanological observations and modelling. The new system runs at the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) and is able to provide a reliable hazard assessment to the National Department of Civil Protection (DPC) during explosive eruptions. The new operational system combines data from low-cost calibrated visible cameras and satellite images to estimate the variation of column height with time and model volcanic plume and fallout in near-real-time (NRT). The new system has three main objectives: (i) to determine column height in NRT using multiple sensors (calibrated cameras and satellite images); (ii) to compute isomass and isopleth maps of tephra deposits in NRT; (iii) to help the DPC to best select the eruption scenarios run daily by INGV-OE every three hours. A particular novel feature of the new system is the computation of an isopleth map, which helps to identify the region of sedimentation of large clasts (≥5 cm) that could cause injuries to tourists, hikers, guides, and scientists, as well as damage buildings in the proximity of the summit craters. The proposed system could be easily adapted to other volcano observatories worldwide.

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 (R 2) 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 R 2 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.

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.

In this work, we present a new catalog of focal mechanisms calculated for earthquakes recorded in Sicily and southern Calabria. It comprises about 300 solutions, for events with magnitudes ranging from 2.7 to 4.8 that occurred from 1999 to 2011. We used P-wave polarities to compute the fault-plane solutions. Two main goals are achieved. For the first, the catalog allows the stress regime and kinematics characterizing the studied area to be depicted at a regional and more local scale. In particular, moving along the tectonic lineament that extends from the Aeolian Islands to the Ionian Sea, there is a change from a regime characterized by sub-horizontal P-axes, ca. NW-SE directed, to an extensive one in the Calabro-Peloritan Arc, where T-axes striking in a NW-SE direction prevail. Our results also show that part of the seismicity is clustered along the main active seismogenic structures, of which the focal mechanisms indicate the kinematics. Finally, in the Etna volcano area, different stress fields act at different depths due to the combination of the regional tectonics, the strong pressurization of the deep magmatic system, and the dynamics of the shallower portion of the volcano. As a second goal, we highlight that the catalog also represents a valuable tool, through the data distribution on the internet, for further studies directed towards improving our understanding of the geodynamic complexity of the region, and for a better characterization of the seismogenic sources.

Clustering is a widely used unsupervised data mining technique. It allows to identify structures in collections of objects by grouping them into classes, named clusters, in such a way that similarity of objects within any cluster is maximized and similarity of objects belonging to different clusters is minimized. In density-based clustering, a cluster is defined as a connected dense component and grows in the direction driven by the density. The basic structure of density-based clustering presents some common drawbacks: (i) parameters have to be set; (ii) the behavior of the algorithm is sensitive to the density of the starting object; and (iii) adjacent clusters of different densities could not be properly identified. In this paper, we address all the above problems. Our method, based on the concept of space stratification, efficiently identifies the different densities in the dataset and, accordingly, ranks the objects of the original space. Next, it exploits such a knowledge by projecting the original data into a space with one more dimension. It performs a density based clustering taking into account the reverse-nearest-neighbor of the objects. Our method also reduces the number of input parameters by giving a guideline to set them in a suitable way. Experimental results indicate that our algorithm is able to deal with clusters of different densities and outperforms the most popular algorithms DBSCAN and OPTICS in all the standard benchmark datasets.

© 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

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

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.