Oral Presentation

Connecting observational data and high-performance cyberinfrastructure will allow for next-generation earthquake modeling. The lack of quantitative data on timescales capturing multiple large earthquake cycles is a fundamental impediment to progress in the field. Physics-based simulations provide the only path for overcoming the lack of data and elucidating multi-scale dynamics and spatiotemporal patterns that extend the knowledge beyond sporadic case studies and regional statistical laws. However, despite increasingly high-quality geodetic, seismic, and field observations, data-driven earthquake imaging yields stark differences, and rapid physics-based models exploring all observed dynamic complexities are often elusive or costly.

The destructive 2023 Mw 7.8-7.7 earthquake doublet is a prime example of how complexities across different time and space scales call for the reassessment of common assumptions on earthquake physics. I will showcase how we integrate multi-scale seismic and space-geodetic observations with multi-fault kinematic inversions and dynamic rupture modeling to unravel the events’ complex rupture history and stress-mediated fault interactions. Mechanically consistent dynamic models accounting for fault interactions can explain the unexpected rupture paths and require heterogeneous background stress. Our results highlight the importance of combining near- and far-field observations with data-driven and physics-based models for seismic hazard assessment.

As we transition SCEC's focus to encompass the broader transform plate boundary, this talk aims to stimulate discussions on the importance of bridging the gap between data-driven observations and physics-based understanding utilizing cyberinfrastructure. By embracing the complexities of earthquakes and utilizing HPC modeling and data science capacities, we can pave the way for future - large-scale and physics-based - seismic hazard mitigation. Next-generation earthquake simulation output has additional significant practical relevance: for example, the results of data-intensive computations can be used to construct reduced-order models which enable the evaluation of new EQ scenarios instantaneously and without resorting to HPC infrastructure. It will be crucial to integrate the tools for developing advanced physics-based earthquake simulations and big observational data sets while ensuring accessibility of the results of such models to broad communities of users.