Dynamics, interactions and delays of the 2019 Ridgecrest rupture sequence

The observational difficulties and the complexity of earthquake physics have rendered seismic hazard assessment largely empirical. Despite increasingly high-quality geodetic, seismic and field observations, data-driven earthquake imaging yields stark differences and physics-based models explaining all observed dynamic complexities are elusive. Here we present data-assimilated three-dimensional dynamic rupture models of California's biggest earthquakes in more than 20 years: the moment magnitude (M-w) 6.4 Searles Valley and M-w 7.1 Ridgecrest sequence, which ruptured multiple segments of a non-vertical quasi-orthogonal conjugate fault system(1). Our models use supercomputing to find the link between the two earthquakes. We explain strong-motion, teleseismic, field mapping, high-rate global positioning system and space geodetic datasets with earthquake physics. We find that regional structure, ambient long- and short-term stress, and dynamic and static fault system interactions driven by overpressurized fluids and low dynamic friction are conjointly crucial to understand the dynamics and delays of the sequence. We demonstrate that a joint physics-based and data-driven approach can be used to determine the mechanics of complex fault systems and earthquake sequences when reconciling dense earthquake recordings, three-dimensional regional structure and stress models. We foresee that physics-based interpretation of big observational datasets will have a transformative impact on future geohazard mitigation.

Automated summary

The complexity and challenges of earthquake physics make seismic hazard assessment largely empirical. In this study, we present data-assimilated three-dimensional dynamic rupture models of California's biggest earthquakes in over 20 years. By using supercomputing, we identify the link between two earthquakes and explain various datasets through earthquake physics. Our findings highlight the importance of regional structure, long- and short-term stress, and fault system interactions in understanding earthquake dynamics and delays. We demonstrate that a joint physics-based and data-driven approach can greatly enhance geohazard mitigation in the future.