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Developing a Near-Field Ground Motion Model With GNSS Peak Ground Displacement

Session: Advances in Understanding Near-Field Ground Motions: Observation, Prediction and Application [Poster]

Type: Poster

Date: 4/22/2021

Presentation Time: 11:30 AM Pacific

Description: 

Earthquake ground motion models (GMM) inform a range of earth science and engineering applications, including seismic hazard evaluations, loss estimates, and seismic design standards. A typical GMM is characterized by simple metrics describing the earthquake source (e.g., magnitude, mechanism), observation distance, and site terms (e.g., Vs30). GMMs are used within U.S. Geological Survey earthquake response products such as ShakeMap, a ground shaking model that allows rapid assessment of the impact of an earthquake. Most often, GMMs are derived from broadband seismometer and strong-motion accelerometer observations, yet these traditional seismic instruments saturate during strong shaking, leading to inaccurate recordings of the low-frequency ground motions. The integration of geodetic data sources, particularly for characterizing the unsaturated ground motion of large-magnitude events, has proven valuable as a complement to traditional seismic approaches and led to the development of a GMM based on peak ground displacement (PGD) estimated from high-rate Global Navigation Satellite Systems (GNSS) data. We present an updated GMM for M6-9 earthquakes based on GNSS-estimated PGD that more effectively accounts for fault finiteness, slip heterogeneity, and observation distance. We evaluate the limitations of the currently available GNSS earthquake dataset to calibrate the GMM. In particular, the historical dataset lacks observations within 100 km of large magnitude events (>M8), inhibiting evaluation of fault dimensions for earthquakes too large to be represented as point-sources in the near-field. In response, we separately consider previously validated synthetic GNSS waveforms within 10-1000 km of M7.8-9.3 Cascadia subduction zone scenario ruptures (Melgar et al., 2016). The synthetic data highlight the importance of fault-distance metrics and improve our preparedness for large magnitude earthquakes with spatiotemporal qualities unlike those in our existing dataset.

Presenting Author: Dara E. Goldberg

Student Presenter: No

Authors