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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 stochastic‐based approaches. [ABSTRACT FROM AUTHOR]

This work offers a novel methodological framework to address the problem of generating data-driven earthquake shaking fields at different vibration periods, which are key to support decision making and civil protection planning. We propose to analyse the entire profiles of spectral accelerations and project their information content to unsampled locations in the system, based on the theory of Object Oriented Spatial Statistics. The proposed methodology combines a non-ergodic ground motion model with a fully functional model for the residual term, the latter consisting of (i) the spatially-varying systematic effects due to source, site and path, and (ii) the remaining aleatory error. The proposed methodology allows to generate multiple shaking scenarios conditioned on the data, jointly and consistently for all the vibration periods, overcoming the intrinsic limitations of existing multivariate approaches to the problem. The approach is tested on a vast dataset of ground motion records collected in the study-area of the Po Plain (Northern Italy), for which a region-specific fully non-ergodic GMM was previously calibrated. Our validation tests demonstrate the potentiality of the approach, which is capable to effectively simulate spectral acceleration profiles, while keeping the ability to capture the main physical features of ground motion patterns in the region. [ABSTRACT FROM AUTHOR]

Near-source effects can amplify seismic ground motion, causing large demand to structures and thus their identification and characterization is fundamental for engineering applications. Among the most relevant features, forward-directivity effects may generate near-fault records characterized by a large velocity pulse and unusual response spectral shape amplified in a narrow frequency-band. In this paper, we explore the main statistical features of acceleration and displacement response spectra of a suite of 230 pulse-like signals (impulsive waveforms) contained in the NESS1 (NEar Source Strong-motion) flat-file. These collected pulse-like signals are analyzed in terms of pulse period and pulse azimuthal orientation. We highlight the most relevant differences of the pulse-like spectra compared to the ordinary (i.e., no-pulse) ones, and quantify the contribution of the pulse through a corrective factor of the spectral ordinates. Results show that the proposed empirical factors are able to capture the amplification effect induced by near-fault directivity, and thus they could be usefully included in the framework of probabilistic seismic hazard analysis to adjust ground-motion model (GMM) predictions. [ABSTRACT FROM AUTHOR]