Near-Source Ground Motions and Their Variability Derived from Dynamic Rupture Simulations Constrained by NGA-West2 GMPEs

Empirical ground-motion prediction equations (GMPEs) lack a sufficient number of measurements at near-source distances. Seismologists strive to supplement the missing data by physics-based strong ground-motion modeling. Here, we build a database of similar to 3000 dynamic rupture scenarios, assuming a vertical strike-slip fault of 36 x 20 km embedded in a 1D layered elastic medium and linear slip-weakening friction with heterogeneous parameters along the fault. The database is built by a Monte Carlo procedure to follow median and variability of Next Generation Attenuation-West2 Project GMPEs by Boore et al. (2014) at Joyner-Boore distances 10-80 km. The synthetic events span a magnitude range of 5.8-6.8 and have static stress drops between 5 and 40 MPa. These events are used to simulate ground motions at near-source stations within 5 km from the fault. The synthetic ground motions saturate at the near-source distances, and their variability increases at the near stations compared to the distant ones. In the synthetic database, the within event and between-event variability are extracted for the near and distant stations employing a mixed-effect model. The within-event variability is lower than its empirical value, only weakly dependent on period, and generally larger for the near stations than for the distant ones. The between-event variability is by 1/4 lower than its empirical value at periods > 1 s. We show that this can be reconciled by considering epistemic error in M-w when determining GMPEs, which is not present in the synthetic data.

Automated summary

Seismologists aim to supplement the lack of near-source data in GMPEs by building a database of dynamic rupture scenarios. Synthetic events are used to simulate ground motions at near-source stations, revealing greater variability at near stations compared to distant ones. Within-event variability is lower for near stations than for distant ones.