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Toward quantitative characterization of simulated earthquake-cycle complexities.

Shiqi Wang1

  • 1Department of Geophysics, Stanford University, Stanford, USA. axelwang@stanford.edu.

Scientific Reports
|July 22, 2024
PubMed
Summary

Characterizing earthquake cycle simulations as nonlinear dynamical systems (NDS) reveals their complexity. Measuring correlation dimensions is an effective method for analyzing these seismic attractors, aiding in earthquake forecasting.

Keywords:
Earthquake cycle simulationsNonlinear dynamicsPhase-space attractorRate-state spring slider

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Area of Science:

  • Geophysics
  • Nonlinear Dynamics
  • Computational Seismology

Background:

  • Earthquake cycle simulations are modeled as nonlinear dynamical systems (NDS).
  • Characterizing the phase-space attractors of these NDS is crucial for understanding earthquake cycle complexity.
  • Assessing whether simulated earthquake cycles are regular or chaotic requires quantitative methods.

Purpose of the Study:

  • To quantitatively characterize attractors in earthquake cycle simulations.
  • To evaluate methods for measuring the complexity of simulated earthquake cycles.
  • To explore the implications of improved characterization for understanding seismic phenomena and forecasting.

Main Methods:

  • Revisiting the quasi-dynamic spring-slider system from an NDS perspective.
  • Evaluating Lyapunov exponents (LEs) and correlation dimensions for attractor characterization.
  • Applying Taken's theorem for attractor reconstruction in earthquake-cycle simulations.

Main Results:

  • Correlation dimension measurement is identified as an easy and effective approach for analyzing seismic attractors, even with non-uniform time sampling.
  • Lyapunov exponents were found to be inconvenient and computationally expensive.
  • Attractor reconstruction corroborated the correlation dimension results.

Conclusions:

  • Quantitative characterization of earthquake cycle simulations enhances understanding of seismic complexities.
  • This approach offers new opportunities to study exotic seismic events like slow-slip events.
  • Improved characterization facilitates more informative comparisons with real paleoseismic data, advancing earthquake forecasting.