A Rupture Directivity Adjustment Model Applicable to the NGA-West2 Ground Motion Models and Complex Fault Geometries

Session: Forthcoming Updates of the USGS NSHMs: Hawaii, Conterminous U.S. and Alaska
Type: Oral
Date: 4/28/2020
Time: 03:00 PM
Room: 230 + 235
Description: 

In their innovative paper, Somerville et al. (1997) developed an empirical model for the modification of ground motion models (GMMs) to account for spatial variations in near-source ground motion amplitude and duration, due to the effects of rupture propagation, source radiation pattern and the polarization of seismic waves. These effects, categorized jointly as rupture “directivity” effects, can have varying consequences on the ground motions in the near field, depending on the style of faulting and the source-site geometry. The effects of rupture directivity on near-fault ground motions are known to be significant and should be included to accurately estimate the hazard from long period ground motions (Abrahamson, 2000). However, these effects are not explicitly accounted for in most GMMs, and therefore not in typical PSHAs. A few directivity models have been proposed in the literature, but the primary obstacles preventing these from widespread use are the uncertainty in practice about which directivity model or models to use, how to use them and how to calculate the required directivity parameters.

In this work, we create a directivity adjustment model which supersedes our 2013 model (Spudich et al., 2013). This model is developed using the residuals of three NGA-West2 GMMs for response spectra. We supplement the data with a rich database of finite fault simulations to explore the directivity predictor variable space and to confirm that model predictions are consistent with recorded data. The upgraded functional form accommodates multisegment ruptures and ruptures with complex changes along strike, such as those in UCERF3 (Field et al, 2013) and features a narrow band adjustment which varies with magnitude. Models are provided for the adjustments to median predictions as well as to the aleatory variability.

Presenting Author: Jeff Bayless

Authors