Earthquake forecasting based on data assimilation: sequential Monte Carlo methods for renewal point processes

M. J. Werner*, K. Ide, D. Sornette

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

17 Citations (Scopus)

Abstract

Data assimilation is routinely employed in meteorology, engineering and computer sciences to optimally combine noisy observations with prior model information for obtaining better estimates of a state, and thus better forecasts, than achieved by ignoring data uncertainties. Earthquake forecasting, too, suffers from measurement errors and partial model information and may thus gain significantly from data assimilation. We present perhaps the first fully implementable data assimilation method for earthquake forecasts generated by a point-process model of seismicity. We test the method on a synthetic and pedagogical example of a renewal process observed in noise, which is relevant for the seismic gap hypothesis, models of characteristic earthquakes and recurrence statistics of large quakes inferred from paleoseismic data records. To address the non-Gaussian statistics of earthquakes, we use sequential Monte Carlo methods, a set of flexible simulation-based methods for recursively estimating arbitrary posterior distributions. We perform extensive numerical simulations to demonstrate the feasibility and benefits of forecasting earthquakes based on data assimilation.

Original languageEnglish
Pages (from-to)49-70
Number of pages22
JournalNonlinear Processes in Geophysics
Volume18
Issue number1
DOIs
Publication statusPublished - 2011

Keywords

  • PATTERN SEARCH ALGORITHMS
  • SEISMIC GAP HYPOTHESIS
  • STOCHASTIC-MODELS
  • ENTROPY SCORE
  • PREDICTION
  • PREDICTABILITY
  • STATISTICS
  • CALIFORNIA
  • SYSTEMS
  • HAZARD

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