Search

We describe the main structure and outcomes of the new probabilistic seismic hazard model for Italy, MPS19 [Modello di Pericolosità Sismica, 2019]. Besides to outline the probabilistic framework adopted, the multitude of new data that have been made available after the preparation of the previous MPS04, and the set of earthquake rate and ground motion models used, we give particular emphasis to the main novelties of the modeling and the MPS19 outcomes. Specifically, we (i) introduce a novel approach to estimate and to visualize the epistemic uncertainty over the whole country; (ii) assign weights to each model components (earthquake rate and ground motion models) according to a quantitative testing phase and structured experts’ elicitation sessions; (iii) test (retrospectively) the MPS19 outcomes with the horizontal peak ground acceleration observed in the last decades, and the macroseismic intensities of the last centuries; (iv) introduce a pioneering approach to build MPS19_cluster, which accounts for the effect of earthquakes that have been removed by declustering. Finally, to make the interpretation of MPS19 outcomes easier for a wide range of possible stakeholders, we represent the final result also in terms of probability to exceed 0.15 g in 50 years., peer-reviewed

Many complex phenomena concerning extreme events causing natural disasters exhibit power-law behavior, reflecting a hierarchical or fractal structure. These phenomena seem to be susceptible to description using approaches inspired by statistical mechanics, particularly approaches involving generalizations of the classical concept of entropy. In this framework we consider the nonextensive Tsallis entropy; by its constrained optimization we derive the expression of the q- exponential distribution which controls the behavior of variables that, in geophysics, can be for instance seismic moment, length of faults, and inter-event times. We estimate the model parameters in the Bayesian perspective and apply this approach to the identification of different dynamical regimes in seismic sequences.

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.

This paper presents the ongoing activities for the assessment of the urban seismic risk at Mt. Etna volcano using the “Disruption Index” approach. We use updated information on the historic main seismicity, seismogenic faults and intensity attenuation that in a recent research project produced probabilistic seismic hazard maps and scenarios expressed in terms of macroseismic intensity. To apply the Disruption Index at Etna, we consider a probabilistic approach for seismic hazard evaluation based jointly on macroseismic fields and fault parameters. For information on the urban scale vulnerability, we use a GIS to organise data relating to buildings and network systems (e.g. typologies, schools, strategic structures, lifelines) related to the municipalities more exposed to seismic risk. The convolution of ground motion and vulnerability/ impact is based on a Monte Carlo simulation. We present here some preliminary results on the identification of nodes that are responsible for major disruption in urban systems., peer-reviewed

This contribution presents the general framework of the European project UPStrat-MAFA "Urban disaster Prevention Strategies using MAcroseismic Fields and FAult Sources" and its ongoing activities. A unique probabilistic procedure is being used for seismic hazard evaluation, using both macroseismic fields and characteristics of fault sources for the analysis of data from volcanic and tectonic areas: Mt. Etna, Mt. Vesuvius and Campi Flegrei (Italy), Azores Islands (Portugal), South Iceland (Iceland), Alicante-Murcia (Spain), and mainland and offshore Portugal. An improvement of urban scale vulnerability information on building and network systems (typologies, schools, strategic buildings, lifelines, and others) is proposed in the form of a global Disruption Index, with the objective to provide a systematic way of measuring earthquake impact in urbanized areas considered as complex networks. Disaster prevention strategies are considered based on an education information system, another effective component of the disaster risk reduction given by long-term activities., peer-reviewed

In this work we aim at two objects: quantifying, by a binomial-beta probabilistic model, the uncertainty involved in the assessment of the intensity decay, an ordinal quantity often incorrectly treated as real variable, and, given the finite dimension of the fault, modelling non-symmetric decays but exploiting information collected from previous studies on symmetric cases. To this end we transform the plane so that the ellipse having the fault length as maximum axis is changed into a circle with fixed diameter. We start from an explorative analysis of a set of macroseismic fields representative of the Italian seismicity among which we identify three different decay trends by applying a hierarchical clustering method. Then we focus on the exam of the seismogenic area of Etna volcano where some fault structures are well recognizable as well as the anisotropic trend of the attenuation. As in volcanic zones the seismic attenuation is much quicker than in other zones, we first shrink and then transform the plane so that the decay becomes again symmetric. Following the Bayesian paradigm we update the model parameters and associate the estimated values of the intensity at site with the corresponding locations in the original plane. Backward validation and comparison with the deterministic law are also presented., This work was funded by the Italian Dipartimento della Protezione Civile in the frame of the 2007-2009 Agreement with Istituto Nazionale di Geofisica e Vulcanologia (INGV) - Project V4 FLANK Volcanology., peer-reviewed

The historical seismic data are suitably modelled by the self-correcting point processes whose conditional intensity functions give the instantaneous occurrence probability of at least one event. These models assume that the level of some physical quantity in a region is revealing of the proneness to generate earthquakes in the immediate future. Four versions of this self-correcting point process are defined through four different proposals for the quantity accumulated in the level of the process. Bayesian inference is performed.

A doubly stochastic Poisson process is proposed in order to study seismic sequences. It is a state-space model where the state process jumps among three possible states, each of them characterised by a different point process constituting the observed process. Hence we observe series of subsequent realizations of different point processes and we aim to estimate which state is active at any time t. To better model the real phenomenon we decided to enrich the models previously analysed by considering a non-stationary state process. The non-stationarity arises from the assumption that the current state depends on both the past observations and the time of the last state jump. A sequential Monte Carlo method is applied to approximate the likelihood function and Markov Chain Monte Carlo methods are used for the parameter estimation.

In this article the seismic hazard assessment is the final goal of a multi-step procedure in which the reliability of the final result depends strongly on the answers given to the intermediate issues. The first problem consists in analyzing the completeness of the sequences of earthquakes drawn from the Italian catalog dating back several centuries. The issue is tackled as a problem of identification, in the Bayesian framework, of the change-point between two Poisson processes. The part considered as complete of the data set is then analyzed through a new self-correcting-type point process that compares the expected and the observed displacement obtained respectively by the unknown slip rate and by the sum of the slips caused by every earthquake. The difference D(t) between these displacements turns out the level of the physical process controlling the rate of the point process proposed, having a stepwise exponentially increasing intensity function. Estimation is performed through MCMC methods.