A Regionalized Seismicity Model for Subduction Zones Based on Geodetic Strain Rates, Geomechanical Parameters, and Earthquake‐Catalog Data

José A. Bayona Viveros, Sebastian von Specht, Anne Strader, Sebastian Hainzl, Fabrice Cotton, & Danijel Schorlemmer

Published July 16, 2019, SCEC Contribution #9289

The Seismic Hazard Inferred from Tectonics based on the Global Strain Rate Map (SHIFT_GSRM) earthquake forecast was designed to provide high-resolution estimates of global shallow seismicity to be used in seismic hazard assessment. This model combines geodetic strain rates with global earthquake parameters to characterize long-term rates of seismic moment and earthquake activity. Although SHIFT_GSRM properly computes seismicity rates in seismically active continental regions, it underestimates earthquake rates in subduction zones by an average factor of approximately 3.

We present a complementary method to SHIFT_GSRM to more accurately forecast earthquake rates in 37 subduction segments, based on the conservation of moment principle and the use of regional interface seismicity parameters, such as subduction dip angles, corner magnitudes, and coupled seismogenic thicknesses. In seven progressive steps, we find that SHIFT_GSRM earthquake-rate underpredictions are mainly due to the utilization of a global probability function of seismic moment release that poorly captures the great variability among subduction megathrust interfaces.

Retrospective test results show that the forecast is consistent with the observations during the 1 January 1977 to 31 December 2014 period. Moreover, successful pseudoprospective evaluations for the 1 January 2015 to 31 December 2018 period demonstrate the power of the regionalized earthquake model to properly estimate subduction-zone seismicity.

Citation
Bayona Viveros, J. A., von Specht, S., Strader, A., Hainzl, S., Cotton, F., & Schorlemmer, D. (2019). A Regionalized Seismicity Model for Subduction Zones Based on Geodetic Strain Rates, Geomechanical Parameters, and Earthquake‐Catalog Data. Bulletin of the Seismological Society of America,. doi: 10.1785/0120190034.