Bayesian analysisof a probability distribution for local intensity attenuation

Intensity attenuation and its variation as a function of the distance and earthquake size is still a critical issue in evaluating seismic hazard. We present a method that allows us to incorporate additional information from the historical earthquake felt reports in the probability estimation of local intensity attenuation. The approach is based on two ideas: a) standard intensity versus epicentral distance relationships constitute an unnecessary filter between observations and estimates; and b) the intensity decay process is affected by many, scarcely known elements (the physical parameters of the source, propagation path effects, building vulnerability, the semi-qualitative character of macroseismic scales, etc.). Hence intensity decay should be treated as a random variable as is the macroseismic intensity. We assume here that decay, defined on the set {0,1, ..., I0}, follows a binomial distribution with parameters (I0, p); p depends on the distance from the epicenter and is related to the probability of null decay at that distance. According to the Bayesian approach this p parameter is, in turn, a Beta random variable. The observations related to earthquakes with their epicenter outside the area concerned, but belonging to homogeneous zones, are used as prior knowledge of the phenomenon, while the data points of events inside the area are used to update the estimates through the posterior means of the quantities involved. Our methodology is described in detail in an application to the Umbria-Marche area in Central Italy. The data sets examined are the macroseismic intensity database DOM4.1 and the zonation ZS.4, both compiled by the Italian Group for Defence against Earthquakes (GNDT). The method is validated by comparing the observed and the estimated intensity data points of the Camerino (28/07/1799) and of the Colfiorito (26/09/1997) earthquakes

Applying the Disruption Index procedure to evaluate the urban seismic risk in the Mt. Etna area (Italy)

The Disruption Index is used here for the assessment of urban disruption in the Mt. Etna area after a natural disaster. The first element of the procedure is the definition of the seismic input, which is based on information about the historical seismicity and seismogenic faults. The second element is the computation of the seismic impact on the building stock and infrastructure in the region considered. Information on urban-scale vulnerability was collected and a geographic information system was used to organize the data relating to buildings and network systems (e. g., typologies, schools, strategic structures, lifelines). The central idea underlying the definition of the Disruption Index is the identification and evaluation of the impacts on a target community, considering the physical elements that contribute most to the severe disruption. The results of this study are therefore very useful for earthquake preparedness planning and for the development of strategies to minimize the risks from earthquakes. This study is a product of the European “Urban Disaster Prevention Strategies using Macroseismic Fields and Fault Sources” project (UPStrat-MAFA European project 2013).PublishedTorino, Italy3T. Pericolosità sismica e contributo alla definizione del rischioope

On assessing importance of components in dysfunction urban systems given an earthquake: the case of Mt. Etna region

Mt Etna region (Sicily, Italy) is one of the test areas studied in the European Project “Urban disaster Prevention Strategies using MAcroseismic fields and FAult sources” ( UPStrat-MAFA) to which the methodology of Disruption Index (hereafter DI), recently developed to evaluate the dysfunction of urban systems caused by earthquakes (Ferreira et al., 2014), has been applied on a trial basis

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Detecting clusters in spatially correlated waveforms

Seismic networks often record signals characterized by similar shapes that provide important information according to their geographic positions. We propose an approach to identify homogeneous clusters of seismic waves, combining analysis of waveforms with metadata and spectrogram information. In waveforms clustering, cross-correlation measures between signals may presents some limitations, so we refer to more recent contributes relating data-depth based clustering analysis. The mechanism for alignment is also an important topic of the analysis: warping (or aligning) procedures identify nuisance effects in phase variation, that, if ignored, may result in a possible loss of information and the immediate consequence is that the underlying pattern could not be retained. The effectiveness of the approach is investigated by mean of real data. The data consist of a set of recordings of 21 earthquakes in the Centre of Italy with magnitude more than 5.5 mw, provided by the seismic network RAN (Rete Accelerometrica Nazionale) managed by the Italian Department of Civil Protection, are obtained from ESM/ITACA database (esm.mi.ing.it; itaca.mi.ingv.it).The signals were recorded by stations, whose distances from the epicenter are in the range from 50 to 100 km. The goal is dividing the spatial domain into homogeneous clusters and extracting information from the shapes of the underlying curves. This work is supported by National grant MIUR, PRIN-2015 program, Prot.20157PRZC4: Complex space-time modeling and functional analysis for probabilistic forecast of seismic events

Bayesian Analysis of a Probability Distribution for the Regional Intensity Attenuation

The regional intensity attenuation and its variation as a function of the distance and earthquake size is still a critical problem in the seismic hazard evaluations. We present a method that allows us to incorporate additional information of the historical earthquake felt reports in the probability estimation of the regional intensity attenuation. The approach is based on two ideas: a) standard intensity versus epicentral distance relationships constituite an unnecessary filter between observations and estimates; b) the intensity decay process is affected by many, not well known elements (physical parameters of the source, propagation path effects, building conditions, semi-qualitative character of macroseismic scales, etc.) and hence it should be treated as a random variable like the macroseismic intensity. We assume that the decay, defined on the set {0, 1,...,Io}, follows a binomial distribution with parameters (Io , p); p depends on the distance from the epicenter and is related to the probability of having null decay at that distance. According to the Bayesian approach this p parameter is, in its turn, a Beta random variable. The observations related to earthquakes with epicenter outside the area in exam, but belonging to homogeneous zones, are used as prior knowledge on the phenomenon, while the data points of the events inside the area are used to update the estimates through the posterior means of the quantities in interest. A detailed description of this methodology is given by applying it to the Umbria-Marche area, Central Italy. The data sets examined are the macroseismic intensity database DOM4.1 and the zonation ZS.4, both compiled by the Italian Group for the Defence against Earthquakes (GNDT). Moreover the method is validated comparing the observated and the estimated intensity data points of the Camerino (28/07/1799) and of the Colfiorito (26/09/1997) earthquake

How to estimate anisotropic attenuation exploiting prior isotropic knowledge

The pattern of the highest intensities in macroseismic fields of volcanic areas is strongly anisotropic because of the linear extension of the fault. In the isotropic approach to the estimation of the probability distribution of the site intensity the analysis starts considering the sites inside circular bins, with fixed width, around the epicentre. To consider the source effect it seems natural to shift epicentre to the rupture length and circular bins to elliptical ones. To exploit prior information on the attenuation trend in Italian seismological and volcanic areas we transform the plane so that an ellipse becomes a circle with diameter equal to its minor axis, and then estimate the probability distribution of the site intensity applying the method proposed in Zonno et al. (2009) to the transformed data points

Is space-time interaction real or apparent in seismic activity?

It is widely shared opinion that not only secondary (aftershocks) but also main earthquakes have the tendency to occur in space-time clusters. This assumption has affected the preferential choice of stochastic models in the studies on seismic hazard, like self-exciting (epidemic) models which imply the abrupt increase of the occurrence probability after a shock and the subsequent exponential decrease without the desirable increase before a forthcoming event. The importance of this assumption requires the application of statistical tools to evaluate objectively its coherence with the reality at different scale of magnitude-space-time. To this end we consider the earthquakes drawn from the historical Italian catalogue CPTI04 that geologists have associated with each of the eight tectonically homogeneous regions in which Italian territory is divided. Fixing different magnitude thresholds we perform statistical tests based on the space-time distance between pairs of earthquakes under the null hypothesis of uniform distribution in time and space and evaluate the significance of the possible clusters. Monte Carlo hypothesis testing is also used to obtain the null distribution and the simulated p-value