On the Consistency of Instrumental, Macroseismic Intensity and Remote Sensing Data: Lessons From the 2019 Ridgecrest, California Earthquake Sequence

Session: Observations From the 2019 Ridgecrest Earthquake Sequence [Poster]
Type: Poster
Date: 4/28/2020
Time: 08:00 AM
Room: Ballroom
Description: 

Shaking from the 6 July 2019 M7.1 Ridgecrest, California, mainshock was strongly felt through the local area, but generated only minimal structural damage. We consider the extent to which a damage-proxy map (DPM) generated from pre- and post-event SAR images is able to detect relatively minor damage throughout the town of Ridgecrest. The DPM, which has a ~30-m pixel size, does not, as expected, detect all minor structural damage to individual small structures such as typical single-family houses. The DPM does, however, confirm many instances of minor structural damage to larger structures and groups of smaller structures and confirms that structural damage in Ridgecrest was generally low. In some instances, the DPM suggests minor structural damage that is not apparent upon visual inspection. We further use both instrumental and intensity data to map out the distribution of near-field ground motions over the frequency range of engineering concern. We show that peak ground accelerations and peak ground velocities estimated from “Did You Feel It?” (DYFI) intensity data using the Worden et al. (2012) ground motion intensity conversion equations (GMICE) are highly consistent with recorded instrumental data. Both instrumental and estimated mainshock peak accelerations from the mainshock are further consistent with predictions from both the Boore et al. (2014) ground motion prediction equation (GMPE), but lower than predicted by the Atkinson and Wald (2007) and Atkinson et al. (2014) intensity prediction equations (IPE). Of note, the GMPE includes non-linear terms for strong ground motions at soft-sediment sites, while the IPEs do not. The results suggest that it might be fruitful to develop an IPE for large (M7+) earthquakes in California based solely on ground motion prediction equations derived from the large NGA (Next Generation Attenuation relationship) data set, together with a well-constrained GMICE.

Presenting Author: Susan E. Hough

Authors