The New ShakeMap in Italy: Progress and Advances in the Last 10 Yr

Michelini, Alberto; Faenza, Licia; Lanzano, Giovanni; Lauciani, Valentino; Jozinović, Dario; Puglia, Rodolfo; Luzi, Lucia

doi:10.1785/0220190130

Cited by 69 publications

(52 citation statements)

References 49 publications

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“…As demonstrated by several authors, the spatial modeling is crucial to generate correlated ground motion random fields, so its neglecting may cause a remarkable bias in loss estimation and seismic risk analyses. [12][13][14] A similar principle is also applied to produce the USGS ShakeMaps, [15][16][17] an online tool where shaking maps are made available in near-real time for a discrete set of spectral ordinates. However, a limited number of studies in the literature considers fully nonergodic models and spatial correlation of the repeatable terms to generate ground motion fields.…”

Section: Introductionmentioning

confidence: 99%

Empirical nonergodic shaking scenarios based on spatial correlation models: An application to central Italy

Sgobba

Lanzano

Pacor

2020

Earthq Engng Struct Dyn

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This paper provides a new methodological framework to generate empirical ground shaking scenarios, designed for engineering applications and civil protection planning. The methodology is useful both to reconstruct the ground motion pattern of past events and to generate future shaking scenarios, in regions where strong-motion datasets from multiple events and multiple stations are available. The proposed methodology combines (1) an ad-hoc nonergodic ground motion model (GMM) with (2) a spatial correlation model for the source region-, site-, and path-systematic residual terms, and (3) a model of the remaining aleatory error to take into account for directivity effects. The associated variability is a function of the type of scenario generated (bedrock or site, past or future event) and it is minimal for source areas where several events have occurred and for sites where recordings are available. In order to develop the region-specific fully nonergodic GMM and to compute robust estimation of the residual terms, the approach is calibrated on a highly dense dataset compiled for the area of central Italy. Example tests demonstrate the validity of the approach, which allows to simulate acceleration response spectra at unsampled sites, as well as to capture peculiar physical features of ground motion patterns in the region. The proposed approach could be usefully adopted for data-driven simulations of ground shaking maps, as alternative or complementary tool to physic-based and stochasticbased approaches.

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Section: Introductionmentioning

confidence: 99%

Empirical nonergodic shaking scenarios based on spatial correlation models: An application to central Italy

Sgobba

Lanzano

Pacor

2020

Earthq Engng Struct Dyn

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“…As shown, the site-to-MDP distances range from 0.013 km to 2.938 km. The V S30 values are extracted from the V S30 grid adopted by ShakeMap (Michelini et al, 2020). This figure evidences that most intensity-PGM pairs do not distance more that 1 km and are rather homogeneously distributed in terms of V S30 .…”

Section: Data and Metadatamentioning

confidence: 78%

“…-Station information: network and station code, and location of the receiver; EC8 class, measured and calculated V S30 from the ESM flatfile, and extracted V S30 from the V S30 grid adopted by ShakeMap (Michelini et al, 2020); -Distance measurements: epicentral distance, R EP I , azimuth and finite-source distance measure related to fault geometry R JB , distance between the selected MCS points and the strong motion stations. The Joyner-Boore distance is available for 60% data.…”

Section: Data and Metadatamentioning

confidence: 99%

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INGe: Intensity-ground motion dataset for Italy

Oliveti

Faenza

Michelini

2020

Preprint

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Abstract. In this paper we present an updated and homogeneous earthquake data set for Italy compiled by joining the Italian Macroseismic Database DBMI15 and the Engineering Strong-Motion (ESM) accelerometric data bank. The database has been compiled through an extensive procedure of selection and revision based on two main steps: 1) the removal of several earthquakes in DBMI15 because the data source has been considered to be largely unreliable and 2) the extraction of all the localities reporting intensity data which are located within 3 km from the accelerograph stations that recorded the data. The final data set includes 323 recordings from 65 earthquakes and 227 stations in the time span 1972–2016. The events are characterized by magnitudes in the range 4.0–6.9 and depths in the range 0.3–45.0 km. Here, we illustrate the data collection and the properties of the database in terms of recording, event and station distributions as well as Mercalli-Cancani-Sieberg (MCS) macroseismic intensity points. Furthermore, we discuss the most relevant features of engineering interest showing several statistics with reference to the most significant metadata (such as moment magnitude, several distance metrics, style of faulting etc). The data set can be downloaded from data repository Zenodo at https://doi.org/10.13127/inge.1 (Oliveti et., 2020).

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“…Herein, the ground motion prediction model proposed by Bindi et al (2011, also known as ITA10) for the peak ground acceleration (PGA, the geometric mean of two horizontal components) as the intensity measure is used. A recent work by Michelini et al (2019) ranks ITA10 quite favourably as the GMPE to use for Italian earthquakes from shallow active crustal zones. It is worth noting that there are more recent pan European BND14 (Bindi et al 2014a, b) and Italian GMPE's (ITA18, Lanzano et al 2019) available.…”

Section: Generation Of Gmpe-based Ground Shaking Fieldsmentioning

confidence: 99%

Empirical fragility assessment using conditional GMPE-based ground shaking fields: application to damage data for 2016 Amatrice Earthquake

Miano

Jalayer

Forte

et al. 2020

Bull Earthquake Eng

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Recent earthquakes have exposed the vulnerability of existing buildings; this is demonstrated by damage incurred after moderate-to-high magnitude earthquakes. This stresses the need to exploit available data from different sources to develop reliable seismic risk components. As far as it regards empirical fragility assessment, accurate estimation of ground-shaking at the location of buildings of interest is as crucial as the accurate evaluation of observed damage for these buildings. This implies that explicit consideration of the uncertainties in the prediction of ground shaking leads to more robust empirical fragility curves. In such context, the simulation-based methods can be employed to provide fragility estimates that integrate over the space of plausible ground-shaking fields. These ground-shaking fields are generated according to the joint probability distribution of ground-shaking at the location of the buildings of interest considering the spatial correlation structure in the ground motion prediction residuals and updated based on the registered ground shaking data and observed damage. As an alternative to the embedded coefficients in the ground motion prediction equations accounting for subsoil categories, stratigraphic coefficients can be applied directly to the ground motion fields at the engineering bedrock level. Empirical fragility curves obtained using the observed damage in the aftermath of Amatrice Earthquake for residential masonry buildings show that explicit consideration of the uncertainty in the prediction of ground-shaking significantly affects the results.

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The New ShakeMap in Italy: Progress and Advances in the Last 10 Yr

Cited by 69 publications

References 49 publications

Empirical nonergodic shaking scenarios based on spatial correlation models: An application to central Italy

Empirical nonergodic shaking scenarios based on spatial correlation models: An application to central Italy

INGe: Intensity-ground motion dataset for Italy

Empirical fragility assessment using conditional GMPE-based ground shaking fields: application to damage data for 2016 Amatrice Earthquake

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