Group A, Poster #229, Ground Motions

Development and Verification of Regression and Application Software Tools for Non-ergodic Ground-Motion Models

Grigorios Lavrentiadis, Elnaz Seylabi, Nicolas M. Kuehn, Xiaofeng Meng, Albert R. Kottke, Yousef Bozorgnia, & Christine A. Goulet
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Poster Presentation

2022 SCEC Annual Meeting, Poster #229, SCEC Contribution #12299 VIEW PDF
Non-ergodic ground-motion models (NGMMs) are a promising development in probabilistic seismic hazard analysis (PSHA) as they have the potential of reducing the ground-motion aleatory variability. This reduction in aleatory variability is accompanied by epistemic uncertainty in regions with sparse recordings or a systematic shift in the median ground motion in regions with dense recordings. The use of NGMMs can have a large impact on the seismic hazard, especially at long return periods which are important for critical infrastructure.

Gaussian Process Regression (GPR) – with spatially varying coefficients for modeling the source and site systematic effects and cell-specific an...
elastic attenuation for modeling the systematic path effects – is a flexible and robust modeling technique for developing NGMMs. As part of this work, open‐source computer tools and instructions have been developed to show the steps toward developing and applying NGMMs in the GPR framework. Statistical software packages STAN and INLA are used and compared. The developed software packages were tested against synthetic data sets with known non-ergodic effects, and different implementations of the developed software were evaluated for scalability, universality, precision, and model complexity.