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A Bayesian Assessment of Seismic Semi-Periodicity Forecasts

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Abstract

Among the schemes for earthquake forecasting, the search for semi-periodicity during large earthquakes in a given seismogenic region plays an important role. When considering earthquake forecasts based on semi-periodic sequence identification, the Bayesian formalism is a useful tool for: (1) assessing how well a given earthquake satisfies a previously made forecast; (2) re-evaluating the semi-periodic sequence probability; and (3) testing other prior estimations of the sequence probability. A comparison of Bayesian estimates with updated estimates of semi-periodic sequences that incorporate new data not used in the original estimates shows extremely good agreement, indicating that: (1) the probability that a semi-periodic sequence is not due to chance is an appropriate estimate for the prior sequence probability estimate; and (2) the Bayesian formalism does a very good job of estimating corrected semi-periodicity probabilities, using slightly less data than that used for updated estimates. The Bayesian approach is exemplified explicitly by its application to the Parkfield semi-periodic forecast, and results are given for its application to other forecasts in Japan and Venezuela.

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Acknowledgments

Our sincere thanks to two anonymous reviewers and to Pageoph guest editor Matthew Gerstenberger. This project was partially funded by CONACYT scholarship 242919 (C. Quinteros) and CONACYT grant 222795, and was partially carried out within project CGL2011-29474-C01-01.

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Authors and Affiliations

  1. CICESE, Ensenada, Baja California, Mexico

    F. Nava, C. Quinteros, E. Glowacka & J. Frez

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  1. F. Nava
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  2. C. Quinteros
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  3. E. Glowacka
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  4. J. Frez
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Correspondence to F. Nava.

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Nava, F., Quinteros, C., Glowacka, E. et al. A Bayesian Assessment of Seismic Semi-Periodicity Forecasts. Pure Appl. Geophys. 173, 197–203 (2016). https://doi.org/10.1007/s00024-015-1056-3

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  • DOI: https://doi.org/10.1007/s00024-015-1056-3

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