Forecasting large aftershocks within one day after the main shock

Omi, Takahiro; Ogata, Yosihiko; Hirata, Yoshito; Aihara, Kazuyuki

doi:10.1038/srep02218

Cited by 88 publications

(94 citation statements)

References 24 publications

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“…The progress in the development of automatic processing techniques (e.g., Yoon et al 2015;Tamaribuchi et al 2016 for hypocenter determination and Omi et al 2013 for b-value estimation) will become increasingly important.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of foreshock activity inferred from the JMA earthquake catalog

Tamaribuchi

Yagi

Enescu

et al. 2018

Earth Planets Space

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We investigated the foreshock activity characteristics using the Japan Meteorological Agency Unified Earthquake Catalog for the last 20 years. Using the nearest-neighbor distance approach, we systematically and objectively classified the earthquakes into clustered and background seismicity. We further categorized the clustered events into foreshocks, mainshocks, and aftershocks and analyzed their statistical features such as the b-value of the frequency-magnitude distribution. We found that the b-values of the foreshocks are lower than those of the aftershocks. This b-value difference suggested that not only the stochastic cascade effect but also the stress changes/aseismic processes may contribute to the mainshock-triggering process. However, forecasting the mainshock based on b-value analysis may be difficult. In addition, the rate of foreshock occurrence in all clusters (with two or more events) was nearly constant (30-40%) over a wide magnitude range. The difference in the magnitude, time, and epicentral distance between the mainshock and largest foreshock followed a power law. We inferred that the distinctive characteristics of foreshocks can be better revealed using the improved catalog, which includes the micro-earthquake information.

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Section: Discussionmentioning

confidence: 99%

Characteristics of foreshock activity inferred from the JMA earthquake catalog

Tamaribuchi

Yagi

Enescu

et al. 2018

Earth Planets Space

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“…For example, the analysis of 92 years of historical data (Satake, 2015) yielded a b-value of 0.88, and the aftershocks of Tohoku-Oki yielded a b-value range of 0.9-1.2 (Omi et al, 2013;Toda and Stein, 2013). However, it is clear that b-values can vary in space and time (Wiemer and Wyss, 2002).…”

Section: B-valuementioning

confidence: 96%

“…2). For Tohoku-Oki 2011, the largest aftershock was 1.1 magnitude units below, and the b-value was 0.9-1.2 (Omi et al, 2013;Toda and Stein, 2013). For Gorkha 2015, the first aftershock sequence had the largest aftershock being 1.1 magnitude unit below the mainshock, whereas the second larger aftershock was only 0.5 magnitude units below the mainshock magnitude.…”

Section: Aftershocksmentioning

confidence: 99%

“…Japan is another well-documented area with a spread of values but a general convergence of b-values around 1 (e.g., Bird and Kagan, 2004;Grunewald and Stein, 2006;Parsons et al, 2012;Omi et al, 2013;Toda and Stein, 2013;Satake, 2015). For example, the analysis of 92 years of historical data (Satake, 2015) yielded a b-value of 0.88, and the aftershocks of Tohoku-Oki yielded a b-value range of 0.9-1.2 (Omi et al, 2013;Toda and Stein, 2013).…”

Section: B-valuementioning

confidence: 99%

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Determination of Mmax from Background Seismicity and Moment Conservation

Stevens

Avouac

2017

Bulletin of the Seismological Society of America

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We describe a simple method to determine the probability distribution function of the magnitude M max and return period T R of the maximum plausible earthquake on crustal faults. The method requires the background seismicity rate (estimated from instrumental data) and the rate of interseismic moment buildup. The method assumes that the moment released by the seismic slip is in balance with the moment deficit accumulated in between earthquakes. It also assumes that the seismicity obeys the Gutenberg-Richter (GR) law up to M max . We took into account the aftershocks of large infrequent events that were not represented in the instrumental record, so that we could estimate the average seismicity rate over the entire fault history. We extrapolated the instrumental record, using the GR law to model the frequency of larger events and their aftershocks. This increased the frequencies of smaller events on average; when these smaller events were newly extrapolated, they predicted a higher frequency of larger events. We iterated this process until stability was reached, and then we assumed moment balance when we found the maximum magnitude; we have found this method to be appropriate in applications involving examples of fault with good historical catalogs. We then showed examples of applications to faults with no historical catalogs. We present results from nine cases. For the San Andreas fault system, we find

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“…If the detection rates of aftershocks regarding magnitudes are same throughout the target period, we can make use of a homogeneous dataset under a smaller threshold magnitude instead of the dataset of completely detected threshold magnitudes to allow less-uncertain inference and prediction Katsura 1993, 2006). It is also desirable to recover information on missing aftershocks using modeling of detection rates, particularly in the early stage of aftershocks (Omi et al 2013(Omi et al , 2014a.…”

Section: Seismicity Quiescence and Empirical Medium-term Forecastingmentioning

confidence: 99%

Statistical monitoring of aftershock sequences: a case study of the 2015 Mw7.8 Gorkha, Nepal, earthquake

Ogata

Tsuruoka

2016

Earth Planets Space

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Early forecasting of aftershocks has become realistic and practical because of real-time detection of hypocenters. This study illustrates a statistical procedure for monitoring aftershock sequences to detect anomalies to increase the probability gain of a significantly large aftershock or even an earthquake larger than the main shock. In particular, a significant lowering (relative quiescence) in aftershock activity below the level predicted by the Omori-Utsu formula or the epidemic-type aftershock sequence model is sometimes followed by a large earthquake in a neighboring region. As an example, we detected significant lowering relative to the modeled rate after approximately 1.7 days after the main shock in the aftershock sequence of the Mw7.8 Gorkha, Nepal, earthquake of April 25, 2015. The relative quiescence lasted until the May 12, 2015, M7.3 Kodari earthquake that occurred at the eastern end of the primary aftershock zone. Space-time plots including the transformed time can indicate the local places where aftershock activity lowers (the seismicity shadow). Thus, the relative quiescence can be hypothesized to be related to stress shadowing caused by probable slow slips. In addition, the aftershock productivity of the M7.3 Kodari earthquake is approximately twice as large as that of the M7.8 main shock.

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Forecasting large aftershocks within one day after the main shock

Cited by 88 publications

References 24 publications

Characteristics of foreshock activity inferred from the JMA earthquake catalog

Characteristics of foreshock activity inferred from the JMA earthquake catalog

Determination of Mmax from Background Seismicity and Moment Conservation

Statistical monitoring of aftershock sequences: a case study of the 2015 Mw7.8 Gorkha, Nepal, earthquake

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