Spatiotemporal forecast of extreme events in a dynamical model of earthquake sequences

Hojjat Kaveh, Jean-Philippe Avouac, Andrew Stuart

Research output: Contribution to journalArticlepeer-review

13 Downloads (Pure)

Abstract

Seismic (‘earthquakes’) and aseismic (‘slow earthquakes’) 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. Here, we forecast extreme events in a numerical model of a single fault governed by rate-and-state friction, which produces realistic sequences of slow events with a wide range of magnitudes and inter-event times. The complex dynamics of this system arise from partial ruptures. As the system self-organizes, prestress is confined to a chaotic attractor of a relatively small dimension. We identify the instability regions (corresponding to particular stress distributions) within this attractor which are precursors of large events. 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.
Original languageUndefined
Pages (from-to)870-885
Number of pages16
JournalGeophysical Journal International
Volume240
Issue number2
DOIs
Publication statusPublished (in print/issue) - 20 Nov 2024

Data Access Statement

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 (⁠
⁠). 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.

Keywords

  • Seismic cycle
  • Self-organization
  • Earthquake interaction
  • forecasting
  • prediction –Numerical modelling

Cite this