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| Titel |
Earthquake forecasting based on data assimilation: sequential Monte Carlo methods for renewal point processes |
| VerfasserIn |
M. J. Werner, K. Ide, D. Sornette |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 1 ; Nr. 18, no. 1 (2011-02-03), S.49-70 |
| Datensatznummer |
250013867
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| Publikation (Nr.) |
 copernicus.org/npg-18-49-2011.pdf |
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| Zusammenfassung |
| 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. |
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