Spatiotemporal forecast of extreme events in a chaotic model of slow slip events
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1.
California Institute of Technology
Abstract
Seismic and aseismic slip events result from episodic slips on faults and are often chaotic due to stress heterogeneity. Their predictability in nature is a widely open question. In this study, we forecast extreme events in a numerical model. The model, which consists of a single fault governed by rate-and-state friction, produces realistic sequences of slow events with a wide range of magnitudes and interevent times. The complex dynamics of this system arise from partial ruptures. As the system self-organizes, the state of the system is confined to a chaotic attractor of a relatively small dimension. We identify the instability regions within this attractor where large events initiate. These regions correspond to the particular stress distributions that are favourable for near complete ruptures of the fault. We show that large events can be forecasted in time and space based on the determination of these instability regions in a low-dimensional space and the knowledge of the current slip rate on the fault.
Copyright and License
Acknowledgement
The authors HK and J-PA express their sincere gratitude to the National Science Foundation (NSF) for their financial support of this research project, through the Industry-University Collaborative Research Center Geomechanics and Mitigation of Geohazards (award #1822214). Author AMS is grateful to DoD for support as a Vannevar Bush Faculty Fellow. Additionally, the authors are grateful for the valuable input and discussion provided by Nadia Lapusta, Brendan Meade, Themis Sapsis, Adriano Gualandi, Alba Rodriguez, Camilla Cattania, Elif Oral, Mateo Acosta, Kelian Dascher-Cousineau, Zachary R Ross, Jan Dirk Jansen, Kyungjae Im. The authors also extend their appreciation to Eric Dunham and the other anonymous reviewer for their constructive feedback and insightful comments.
Data Availability
We used a model of a 2-D thrust fault in a 3-D medium governed by rate-and-state friction with ageing law for the evolution of state variable (θ). The model parameters are summarized in Table 1. To simulate the forward model, we use the QDYN software,2 which is an open-source code to simulate earthquake cycles (Luo et al. 2017). We use the POD technique to reduce the dimensionality of the problem. This method is reviewed in Appendix A. To solve the optimization problem we use the Bayesian optimization method (Brochu et al. 2010 ; Blanchard & Sapsis 2021) that is reviewed in Appendix B. We used the open source code available on GitHub3 for solving the optimization problem.
Supplemental Material
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Additional details
- National Science Foundation
- RISE-1822214
- United States Department of Defense
- Vannevar Bush Faculty Fellowship
- Accepted
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2024-11-11Accepted
- Available
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2024-11-20Published
- Available
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2024-12-11Corrected and typeset
- Caltech groups
- Division of Geological and Planetary Sciences, Center for Geomechanics and Mitigation of Geohazards (GMG), Seismological Laboratory
- Publication Status
- Published