BSHAP project strong ground motion database and selection of suitable ground motion models for the Western Balkan Region

The Kiban-Kyoshin network (KiK-net) database is an important resource for ground motion (GM) studies. The processing of the KiK-net records is a necessary first step to enable their use in engineering applications. In this manuscript we present a step-by-step automated protocol used to systematically process about 157,000 KiK-net strong ground motion records. The automated protocol includes the selection of the corner frequency for high-pass filtering. In addition, a comprehensive set of metadata was compiled for each record. As a part of the metadata collection, two algorithms were used to identify dependent and independent earthquakes. Earthquakes are also classified into active crustal or subduction type events; most of the GM records correspond to subduction type earthquakes. A flatfile with all the metadata and the spectral acceleration of the processed records is uploaded to NEEShub ( https://nees.org/resources/7849 , Dawood et al. 2014 ).

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Ranking and Selection of Earthquake Ground-Motion Models Using the Stochastic Area Metric

Seismological Research Letters ◽

10.1785/0220210216 ◽

2021 ◽

Author(s):

Jaleena Sunny ◽

Marco De Angelis ◽

Benjamin Edwards

Keyword(s):

Ground Motion ◽

Hazard Analysis ◽

Cumulative Distribution ◽

Earthquake Ground Motion ◽

Motion Data ◽

Motion Models ◽

Area Metric ◽

Few Data ◽

Ground Motion Models ◽

Selection Of

Abstract We introduce the cumulative-distribution-based area metric (AM)—also known as stochastic AM—as a scoring metric for earthquake ground-motion models (GMMs). The AM quantitatively informs the user of the degree to which observed or test data fit with a given model, providing a rankable absolute measure of misfit. The AM considers underlying data distributions and model uncertainties without any assumption of form. We apply this metric, along with existing testing methods, to four GMMs in order to test their performance using earthquake ground-motion data from the Preston New Road (United Kingdom) induced seismicity sequences in 2018 and 2019. An advantage of the proposed approach is its applicability to sparse datasets. We, therefore, focus on the ranking of models for discrete ranges of magnitude and distance, some of which have few data points. The variable performance of models in different ranges of the data reveals the importance of considering alternative models. We extend the ranking of GMMs through analysis of intermodel variations of the candidate models over different ranges of magnitude and distance using the AM. We find the intermodel AM can be a useful tool for selection of models for the logic-tree framework in seismic-hazard analysis. Overall, the AM is shown to be efficient and robust in the process of selection and ranking of GMMs for various applications, particularly for sparse and small-sized datasets.

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Selection of earthquake ground motion models using the deviance information criterion

Soil Dynamics and Earthquake Engineering ◽

10.1016/j.soildyn.2018.11.014 ◽

2019 ◽

Vol 117 ◽

pp. 288-299 ◽

Cited By ~ 8

Author(s):

Milad Kowsari ◽

Benedikt Halldorsson ◽

Birgir Hrafnkelsson ◽

Sigurjón Jónsson

Keyword(s):

Ground Motion ◽

Deviance Information Criterion ◽

Information Criterion ◽

Earthquake Ground Motion ◽

Motion Models ◽

Ground Motion Models ◽

Selection Of

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The Older The Better? The Strange Case of Empirical Ground Motion Models in the Near-Source of Moderate-To-Large Magnitude Earthquakes

10.21203/rs.3.rs-698487/v1 ◽

2021 ◽

Author(s):

Roberto Paolucci ◽

Angela Chiecchio ◽

Manuela Vanini

Keyword(s):

Ground Motion ◽

Seismic Design ◽

Strong Motion ◽

Seismic Hazard Assessment ◽

Large Magnitude ◽

Motion Models ◽

Empirical Ground ◽

Ground Motion Models ◽

Large Earthquakes ◽

Selection Of

Abstract This paper aims at providing a quantitative evaluation of the performance of a set of empirical ground motion models (GMMs), by testing them in a magnitude and distance range (Mw = 5.5 ÷ 7.0 and Joyner-Boore source-to-site distance Rjb ≤ 20 km) which dominates hazard in the highest seismicity areas of Italy for the return periods of upmost interest for seismic design. To this end, we made use of the very recent release of the NESS2.0 dataset (Sgobba et al., 2021), that collects worldwide near-source strong motion records with detailed metadata. After selection of an ample set of GMMs, based on either their application in past seismic hazard assessment (SHA) studies or for their recent introduction, a quantification of between- and within-event residuals of predictions with respect to records was performed, with the final aim of shedding light on the performance of existing GMMs in the near-source of moderate-to-large earthquakes, in view of a proper selection and weighting of GMMs for SHA.

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