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 is 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.
The project's home page with links to the various project deliverables is: https://www.risksciences.ucla.edu/nhr3/ngmm
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 can be downloaded with source ./download_exampfiles.sh
.
The syntetic datasets and raw metadata can be downloaded by running source ./download_data.sh
and source ./download_rawfiles.sh
, respectively.
Financial support by the California Department of Transportation and Pacific Gas & Electric Company is greatly appreciated.
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