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Dynamics, interactions and delays of the 2019 Ridgecrest rupture sequence

Nature volume 618, pages 308–315 (2023)Cite this article

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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.

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Fig. 1: Observational constraints for 3D dynamic rupture modelling of the Ridgecrest earthquake sequence.
Fig. 2: Dynamic rupture scenario of the Searles Valley foreshock and comparison with observations.
Fig. 3: Dynamic rupture scenario of the Ridgecrest mainshock and comparison with observations.
Fig. 4: Coseismic and postseismic stress changes.
Fig. 5: Off-fault surface deformation and shallow slip deficit.

Data availability

All data required to reproduce the earthquake sequence scenarios can be downloaded from https://zenodo.org/record/6842773. We provide a detailed README file summarizing the data and data formats provided. Our 3D fault model is available at https://skfb.ly/oDVGw. The static GPS data related to the coseismic rupture of both the foreshock and mainshock are available from UNAVCO (https://www.unavco.org/highlights/2019/ridgecrest.html). The continuous GPS dataset35 is available at https://zenodo.org/record/3366342. The Instaseis Green’s function database that we use to compute teleseismic synthetics is hosted by IRIS at https://ds.iris.edu/ds/products/syngine/. Source data are provided with this paper.

Code availability

All dynamic rupture simulations were performed using SeisSol (www.seissol.org), an open-source software freely available to download from https://github.com/SeisSol/SeisSol/. The used SeisSol code branch and commit are archived at https://zenodo.org/record/7642533. The SeisSol Ridgecrest sequence branch is also available on GitHub (https://github.com/SeisSol/SeisSol/tree/2019_Ridgecrest_sequence). Instructions for downloading, installing and running the code are available in the SeisSol documentation at https://seissol.readthedocs.io/. Downloading and compiling instructions are at https://seissol.readthedocs.io/en/latest/compiling-seissol.html. Instructions for setting up and running simulations are at https://seissol.readthedocs.io/en/latest/configuration.html. Quickstart containerized installations and introductory materials are provided in the docker container and jupyter notebooks at https://github.com/SeisSol/Training. Example problems and model configuration files are provided at https://github.com/SeisSol/Examples, many of which reproduce the SCEC 3D Dynamic Rupture benchmark problems described at https://strike.scec.org/cvws/benchmark_descriptions.html. We use the software SKUA-GOCAD (https://www.aspentech.com/en/resources/brochure/aspen-skua) as the modelling environment to produce all 3D fault models and the open-source software ParaView (https://www.paraview.org/) for visualization.

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Acknowledgements

We thank M. Bader, L. Krenz, S. Wolf, R. Dorozhinskii and the group of hardware-aware algorithms and software for high-performance computing at TUM for decade-long collaboration; N. Schliwa, J. Biemiller, C. Nicholson and S. A. Wirp for discussions; and S. Antoine for sharing surface offset data. This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme (TEAR grant number 852992) and Horizon Europe (ChEESE-2P grant number 101093038, DT-GEO grant number 101058129 and Geo-INQUIRE grant number 101058518), the National Science Foundation (grant number EAR-2121666), the German Research Foundation (DFG projects GA 2465/2-1 and GA 2465/3-1) and the Southern California Earthquake Center (SCEC award 21112). We acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for providing computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de), in project pr63qo.

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Authors and Affiliations

  1. Department of Earth and Environmental Sciences, Ludwig-Maximilians-Universität München, Munich, Germany

    Taufiq Taufiqurrahman, Alice-Agnes Gabriel, Duo Li, Thomas Ulrich, Bo Li & Sara Carena

  2. Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Alice-Agnes Gabriel

  3. Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec, Canada

    Alessandro Verdecchia

  4. Institute of Geology, Mineralogy and Geophysics, Ruhr-University Bochum, Bochum, Germany

    Alessandro Verdecchia

  5. Department of Geophysics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

    František Gallovič

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Contributions

Conceptualization, formal analysis, software, visualization and writing: T.T., A.-A.G., D.L., T.U., B.L., S.C., A.V. and F.G. Funding acquisition and resources: A.-A.G..

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Correspondence to Alice-Agnes Gabriel.

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Supplementary Information

This file contains Supplementary Tables 1–4, Figs. 1–22 and legends for Supplementary Videos 1–8.

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Supplementary Video 1

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Preferred foreshock dynamic rupture scenario.

Supplementary Video 2

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Preferred mainshock dynamic rupture scenario.

Supplementary Video 3

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Alternative mainshock dynamic rupture scenario not accounting for the dynamic and static foreshock stress changes.

Supplementary Video 4

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Alternative mainshock dynamic rupture scenario not accounting for the dynamic and static foreshock stress changes and in addition omitting the effects of the long-term ΔCFS.

Supplementary Video 5

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Alternative foreshock dynamic rupture scenario omitting the long-term ΔCFS.

Supplementary Video 6

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Alternative mainshock dynamic rupture scenario omitting the long-term ΔCFS.

Supplementary Video 7

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Alternative foreshock dynamic rupture scenario loaded with an alternative community ambient stress model.

Supplementary Video 8

Evolution of absolute slip rate (m s−1) across the fault network from four perspectives. Alternative mainshock dynamic rupture scenario loaded with an alternative community ambient stress model.

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Taufiqurrahman, T., Gabriel, AA., Li, D. et al. Dynamics, interactions and delays of the 2019 Ridgecrest rupture sequence. Nature 618, 308–315 (2023). https://doi.org/10.1038/s41586-023-05985-x

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