Modelling background seismicity components identified by nearest neighbour and stochastic declustering approaches: the case of Northeastern Italy

Stochastic Environmental Research and Risk Assessment - An adequate characterization of the temporal features of background seismicity, namely after removal of temporally and spatially clustered...     

Probabilistic modelling of macroseismic attenuation and forecast of damage scenarios

According to the idea now widespread that macroseismic intensity should be expressed in probabilistic terms, a beta-binomial model has been proposed in the literature to estimate the probability of the intensity at site in the Bayesian framework and a clustering procedure has been adopted to define learning sets of macroseismic fields required to assign prior distributions of the model parameters. This article presents the results concerning the learning sets obtained by exploiting the large Italian macroseismic database DBM1I11 (Locati et al. in DBMI11, the 2011 version of the Italian Macroseismic Database, 2011. http://emidius.mi.ingv.it/DBMI11/) and discusses the problems related to their use in probabilistic modelling of the attenuation in seismic regions of the European countries partners of the UPStrat-MAFA project (2012), namely South Iceland, Portugal, SE Spain and Mt Etna volcano area (Italy). Anisotropy and the presence of offshore earthquakes are some of the problems faced. All the work has been carried out in the framework of the Task B of the project.     

The role of the urban system dysfunction in the assessment of seismic risk in the Mt. Etna area (Italy)

A procedure for seismic risk assessment is applied to the Mt. Etna area (eastern Sicily, Italy) through assessment of urban system dysfunction following the occurrence of an earthquake. The tool used is based on the Disruption Index as a concept implemented in Simulator QuakeIST, which defines urban disruption following a natural disaster. The first element of the procedure is the definition of the seismic input, which is based on information about historical seismicity and seismogenic faults. The second element is computation of seismic impact on the building stock and infrastructure in the area considered. Information on urban-scale vulnerability was collected and a geographic information system was used to organise the data relating to buildings and network systems (e.g., building stock, schools, strategic structures, lifelines). The central idea underlying the definition of the Disruption Index is identification and evaluation of the impact on a target community through the physical elements that most contribute to severe disruption. The procedure applied in this study (i.e., software and data) constitutes a very useful operational tool to drive the development of strategies to minimise risks from earthquakes.     

Bayesian analysis of marked stress release models for time-dependent hazard assessment in the western Gulf of Corinth

We consider point processes defined on the space–time domain which model physical processes characterized qualitatively by the gradual increase over time in some energy until a threshold is reached, after which, an event causing the loss of energy occurs. The risk function will, therefore, increase piecewise with sudden drops in correspondence to each event. This kind of behaviour is described by Reid's theory of elastic rebound in the earthquake generating process where the quantity that is accumulated is the strain energy or stress due to the relative movement of tectonic plates. The complexity and the intrinsic randomness of the phenomenon call for probabilistic models; in particular the stochastic translation of Reid's theory is given by stress release models. In this article we use such models to assess the time-dependent seismic hazard of the seismogenic zone of the Corinthos Gulf. For each event we consider the occurrence time and the magnitude, which is modelled by a probability distribution depending on the stress level present in the region at any instant. Hence we are dealing here with a marked point process. We perform the Bayesian analysis of this model by applying the stochastic simulation methods based on the generation of Markov chains, the so called Markov chain Monte Carlo (MCMC) methods, which allow one to reconcile the model's complexity with the computational burden of the inferential procedure. Stress release and Poisson models are compared on the basis of the Bayes factor.