Bayesian inference of empirical ground motion models to pseudo-spectral accelerations of south Iceland seismic zone earthquakes based on informative priors

2020 ◽  
Vol 132 ◽  
pp. 106075 ◽  
Author(s):  
Milad Kowsari ◽  
Tim Sonnemann ◽  
Benedikt Halldorsson ◽  
Birgir Hrafnkelsson ◽  
Jónas Þ. Snæbjörnsson ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Ground Motion ◽  
Seismic Zone ◽  
Informative Priors ◽  
Motion Models ◽  
Empirical Ground ◽  
Spectral Accelerations ◽  
Ground Motion Models ◽  
South Iceland Seismic Zone ◽  
Pseudo Spectral

Relational database used for ground-motion model development in the NGA-sub project

2021 ◽  
pp. 875529302110552
Author(s):  
Silvia Mazzoni ◽  
Tadahiro Kishida ◽  
Jonathan P Stewart ◽  
Victor Contreras ◽  
Robert B Darragh ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Data Storage ◽  
Relational Database ◽  
Model Development ◽  
Intensity Measures ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Models ◽  
Pseudo Spectral

The Next-Generation Attenuation for subduction zone regions project (NGA-Sub) has developed data resources and ground motion models for global subduction zone regions. Here we describe the NGA-Sub database. To optimize the efficiency of data storage, access, and updating, data resources for the NGA-Sub project are organized into a relational database consisting of 20 tables containing data, metadata, and computed quantities (e.g. intensity measures, distances). A database schema relates fields in tables to each other through a series of primary and foreign keys. Model developers and other users mostly interact with the data through a flatfile generated as a time-stamped output of the database. We describe the structure of the relational database, the ground motions compiled for the project, and the means by which the data can be accessed. The database contains 71,340 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Provided ground motion intensity measures include peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations for a range of oscillator periods.

A Bayesian model for truncated regression for the estimation of empirical ground-motion models

2020 ◽  
Vol 18 (14) ◽  
pp. 6149-6179
Author(s):  
Nicolas Martin Kuehn ◽  
Tadahiro Kishida ◽  
Mohammad AlHamaydeh ◽  
Grigorios Lavrentiadis ◽  
Yousef Bozorgnia
Keyword(s):  
Ground Motion ◽  
Bayesian Model ◽  
Truncated Regression ◽  
Motion Models ◽  
Empirical Ground ◽  
Ground Motion Models
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