Dynamics, interactions and delays of the 2019 Ridgecrest rupture sequence

Taufiq Taufiqurrahman, Alice-Agnes Gabriel (), Duo Li, Thomas Ulrich, Bo Li, Sara Carena, Alessandro Verdecchia and František Gallovič
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Taufiq Taufiqurrahman: Ludwig-Maximilians-Universität München
Alice-Agnes Gabriel: Ludwig-Maximilians-Universität München
Duo Li: Ludwig-Maximilians-Universität München
Thomas Ulrich: Ludwig-Maximilians-Universität München
Bo Li: Ludwig-Maximilians-Universität München
Sara Carena: Ludwig-Maximilians-Universität München
Alessandro Verdecchia: McGill University
František Gallovič: Charles University

Nature, 2023, vol. 618, issue 7964, 308-315

Abstract: Abstract 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 (Mw) 6.4 Searles Valley and Mw 7.1 Ridgecrest sequence, which ruptured multiple segments of a non-vertical quasi-orthogonal conjugate fault system1. 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.

Date: 2023
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DOI: 10.1038/s41586-023-05985-x

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