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Lessons learned from applying varying coefficient model to controlled simulation datasets

  • S.I. : Non-Ergodic Ground Motion Models and Hazard
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Abstract

The development of site- and path-specific (i.e., non-ergodic) ground-motion models (GMMs) can drastically improve the accuracy of probabilistic seismic hazard analyses (PSHAs). The varying coefficient model (VCM) is a novel technique for developing non-ergodic GMMs, which puts epistemic uncertainty into spatially varying coefficients. The coefficients at nearby locations are correlated by a prior distribution imposed on a Gaussian Process. The correlation structure is determined by the data, and later used to predict coefficients and their epistemic uncertainties at new locations. It is important to carefully verify the technique before its results can be accepted by the engineering community. This study used a series of simulation-based controlled ground-motion datasets from CyberShake to test a modified VCM technique, which partitions the epistemic uncertainty into spatially varying source, site, and path terms. Because the simulation parameters (inputs) are known, verification of what is recovered by the VCM from CyberShake simulations is straightforward. We found that the site effects in CyberShake datasets can be reliably estimated by the VCM. However, the densely-located self-similar events in CyberShake datasets along pre-defined faults violate the isotropic assumption underlying the VCM, thus preventing the VCM from capturing the genuine source effects. For path effects, cell-specific attenuation approaches fail to recover the anelastic attenuation pattern of the 3D velocity model, which is most likely due to other unmodeled effects and inappropriate assumption of wave-propagation path. Instead, a midpoint approach that only considers the aggregated path effects can better recover the strong anelastic attenuation within basins by fixing the correlation length of path effects. Lessons learned in this study not only provide guidance for future applications of VCM to both simulation and empirical datasets, but will also guide further development of the technique, with emphasis on the recovery of path effects.

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Acknowledgements

We would like to thank Nicolas Kuehn, Chih-Hsuan Sung, Grigorios Lavrentiadis and Kevin Milner for comments and suggestions on this article. We also thank Scott Callaghan for his help with the CyberShake data.

Funding

This work was supported by the Southern California Earthquake Center (SCEC), SCEC Contribution #11847. SCEC is funded by the National Science Foundation (NSF) and U.S. Geological Survey (USGS) through cooperative agreements with the University of Southern California (USC). Additional funding for this work was provided by the Pacific Gas and Electric Company (PG&E). This work used allocations from the Extreme Science and Engineering Discovery Environment (XSEDE) program, which is supported by National Science Foundation Grant Number ACI-1548562. Specifically, it used the Bridges and Bridges-2 system, which is supported by NSF Award Number ACI-1445606, at the Pittsburgh Supercomputing Center (PSC).

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  1. Southern California Earthquake Center, University of Southern California, Los Angeles, USA

    Xiaofeng Meng & Christine A. Goulet

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  1. Xiaofeng Meng
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  2. Christine A. Goulet
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Correspondence to Xiaofeng Meng.

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Meng, X., Goulet, C.A. Lessons learned from applying varying coefficient model to controlled simulation datasets. Bull Earthquake Eng 21, 5151–5174 (2023). https://doi.org/10.1007/s10518-022-01512-x

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