Intermediate‐term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches

DOI Web Site Web Site 被引用文献12件 参考文献51件
  • Takahiro Omi
    Institute of Industrial Science University of Tokyo Tokyo Japan
  • Yosihiko Ogata
    Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa Japan
  • Yoshito Hirata
    Institute of Industrial Science University of Tokyo Tokyo Japan
  • Kazuyuki Aihara
    Institute of Industrial Science University of Tokyo Tokyo Japan

書誌事項

公開日
2015-04
資源種別
journal article
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1002/2014jb011456
公開者
American Geophysical Union (AGU)

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説明

<jats:title>Abstract</jats:title><jats:p>Because aftershock occurrences can cause significant seismic risks for a considerable time after the main shock, prospective forecasting of the intermediate‐term aftershock activity as soon as possible is important. The epidemic‐type aftershock sequence (ETAS) model with the maximum likelihood estimate effectively reproduces general aftershock activity including secondary or higher‐order aftershocks and can be employed for the forecasting. However, because we cannot always expect the accurate parameter estimation from incomplete early aftershock data where many events are missing, such forecasting using only a single estimated parameter set (plug‐in forecasting) can frequently perform poorly. Therefore, we here propose Bayesian forecasting that combines the forecasts by the ETAS model with various probable parameter sets given the data. By conducting forecasting tests of 1 month period aftershocks based on the first 1 day data after the main shock as an example of the early intermediate‐term forecasting, we show that the Bayesian forecasting performs better than the plug‐in forecasting on average in terms of the log‐likelihood score. Furthermore, to improve forecasting of large aftershocks, we apply a nonparametric (NP) model using magnitude data during the learning period and compare its forecasting performance with that of the Gutenberg‐Richter (G‐R) formula. We show that the NP forecast performs better than the G‐R formula in some cases but worse in other cases. Therefore, robust forecasting can be obtained by employing an ensemble forecast that combines the two complementary forecasts. Our proposed method is useful for a stable unbiased intermediate‐term assessment of aftershock probabilities.</jats:p>

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