Non-ergodic Methodology and Modeling Tools

This repository contains software tools for developing Nonergodic Ground Motion Models (NGMMs) based on the varying coefficient (Landwehr et al., 2016) and cell-specific anelastic attention approach (Dawood and Rodriguez‐Marek, 2013). Developed tools are available for R using the statistical package (R-INLA, https://www.r-inla.org/) and in python using the CMDSTAN and PYSTAN interface packages for the Bayesian software (Stan, https://mc-stan.org/). Documentation and detailed instructions on the use of the developed tools are provided in Lavrentiadis et al., 2022a GIRS report. A general introduction and considerations for the development of NGMMs are presented in Lavrentiadis et al., 2022b.

Home Page

The project's home page with links to the various project deliverables is: https://www.risksciences.ucla.edu/nhr3/ngmm The project's data are accessible through DesignSafe

Folder Structure

The main folder Analyses contains all the regression, prediction, hazard implementation, testing, and library scripts. Within the Analyses folder, Data_Preparation includes preprocessing scripts to prepare the ground-motion data for the NGMM regression. Regression contains the Jupyter notebooks for running the NGMM regressions using Stan and INLA. Predictions includes the scripts for the conditional predictions for new scenarios based on the regression results. Code_Verification contains the codes associated with the verification exercise. Lastly, folders Python_lib, R_lib, and Stan_lib contain various scripts invoked in the main functions.

The main folder Data mirrors the structure of the Analyses folder and contains all the input and output files.

The Raw_files includes the files used to construct the synthetic datasets for the verification exercise.

.
|--Analyses
|     |--Data_Preparation
|     |--Regression
|     |--Predictions
|     |--Code_Verification
|     |--Python_lib
|     |--R_lib
|     |--Stan_lib
|
|--Data
|     |--Regression
|     |--Predictions
|     |--Code_Verification
|     
|--Raw_files

An example regression dataset, as well as the synthetic datasets and raw metadata, can be downloaded from DesignSafe.

Acknowledgments

Financial support by the California Department of Transportation and Pacific Gas & Electric Company is greatly appreciated.

References

Dawood, H. M., & Rodriguez‐Marek, A. (2013). A method for including path effects in ground‐motion prediction equations: An example using the M w 9.0 Tohoku earthquake aftershocks. Bulletin of the Seismological Society of America, 103(2B), 1360-1372.

Landwehr, N., Kuehn, N. M., Scheffer, T., & Abrahamson, N. (2016). A nonergodic ground‐motion model for California with spatially varying coefficients. Bulletin of the Seismological Society of America, 106(6), 2574-2583.

Lavrentiadis, G., Nicolas, K. M., Bozorgnia, Y., Seylabi, E., Meng, X., Goulet, C., & Kottke, A. (2022a) Non‐ergodic Methodology and Modeling Tools. Natural Hazards Risk and Resiliency Research Center: The Garrick Institute for the Risk Sciences, University of California, Los Angeles

Lavrentiadis, G., Abrahamson, N. A., Nicolas, K. M., Bozorgnia, Y., Goulet, C. A., Babič, A., ... & Walling, M. (2022b). Overview and Introduction to Development of Non-Ergodic Earthquake Ground-Motion Models. Bulletin of Earthquake Engineering