講演情報
[S23-08]日向灘における群発地震検出とスロー地震との関係の調査
*吉村 嶺1、西川 友章2 (1. 京都大学、2. 京大防災研)
An increase in the seismicity rate without a clear main shock is called an earthquake swarm (e.g., Mogi, 1963). Earthquake swarms occur in various tectonic settings. In subduction zones, some earthquake swarms have been observed in association with slow slip events (SSEs) (e.g., Ozawa et al., 2003). Detecting earthquake swarms is important for understanding the relationship between slow earthquakes and seismic activity. In this study, we developed a new, simple method for detecting earthquake swarms based on the epidemic-type aftershock-sequence (ETAS) model (Ogata, 1988) and Akaike’s information criterion (AIC; Akaike, 1974). We conducted earthquake swarm detection in the Hyuga-nada region of the Nankai Trough subduction zone.
The ETAS model expresses the seismicity rate at time t (λ(t)) as the summation of the stationary background seismicity rate (μ) and aftershock rates (Σti<t Kexp{α(Mi-Mc)}/(t-ti+c)p) following Omori–Utsu’s aftershock law (e.g., Utsu, 1957), where ti is the origin time of the i-th earthquake, Mi is the magnitude of the i-th earthquake, and α, c, K, and p are model parameters controlling aftershock rates.
In this study, based on the ETAS model improved by Okutani & Ide (2011), we developed a new model that takes into account an earthquake swarm by considering the increment of the background seismicity rate during an earthquake swarm period (μ1(t)). μ1(t) is expressed as the product of the background event increase during a swarm period (Nsw) and the normalized Gaussian function (1/(2πTsws2)1/2exp{-(t-tswp)2/2Tsws2}, where tswp is the peak time of the background seismicity rate during the swarm period, Tsws is the standard deviation of the normalized Gaussian function. Note that Nsw, tswp, and Tsws are new model parameters.
In this study, the peak time of the background seismicity rate during an earthquake swarm period, tswp, was grid-searched with 1-day intervals, and the other seven parameters (μ, α, c, K, p, Nsw, and Tsws) were estimated via the maximum likelihood method. We extracted dates when the difference of AIC (ΔAIC) between the model considering an earthquake swarm and the original ETAS model was less than -2 and considered those dates as peak dates of the background seismicity rate during an earthquake swarm period.
We then conducted an earthquake swarm detection in the Hyuga-nada region (30.0°N to 32.3°N, 131.7°E to 133.6°E) in the 0 to 50 km depth range. The analysis period was 12 years, from January 1, 2010 to December 31, 2021. We used earthquakes of magnitude 2.0 or greater from the Japan Meteorological Agency catalog.
The results showed that there were 24 dates with a minimum ΔAIC of -2 or less. In this presentation, we plan to report the results of our investigation into the spatiotemporal relationship between the detected earthquake swarms and the slow earthquakes (e.g., very-low-frequency earthquakes and slow slip events) observed in the Hyuga-nada region (e.g., Baba et al., 2020; Okada et al., 2022).
The ETAS model expresses the seismicity rate at time t (λ(t)) as the summation of the stationary background seismicity rate (μ) and aftershock rates (Σti<t Kexp{α(Mi-Mc)}/(t-ti+c)p) following Omori–Utsu’s aftershock law (e.g., Utsu, 1957), where ti is the origin time of the i-th earthquake, Mi is the magnitude of the i-th earthquake, and α, c, K, and p are model parameters controlling aftershock rates.
In this study, based on the ETAS model improved by Okutani & Ide (2011), we developed a new model that takes into account an earthquake swarm by considering the increment of the background seismicity rate during an earthquake swarm period (μ1(t)). μ1(t) is expressed as the product of the background event increase during a swarm period (Nsw) and the normalized Gaussian function (1/(2πTsws2)1/2exp{-(t-tswp)2/2Tsws2}, where tswp is the peak time of the background seismicity rate during the swarm period, Tsws is the standard deviation of the normalized Gaussian function. Note that Nsw, tswp, and Tsws are new model parameters.
In this study, the peak time of the background seismicity rate during an earthquake swarm period, tswp, was grid-searched with 1-day intervals, and the other seven parameters (μ, α, c, K, p, Nsw, and Tsws) were estimated via the maximum likelihood method. We extracted dates when the difference of AIC (ΔAIC) between the model considering an earthquake swarm and the original ETAS model was less than -2 and considered those dates as peak dates of the background seismicity rate during an earthquake swarm period.
We then conducted an earthquake swarm detection in the Hyuga-nada region (30.0°N to 32.3°N, 131.7°E to 133.6°E) in the 0 to 50 km depth range. The analysis period was 12 years, from January 1, 2010 to December 31, 2021. We used earthquakes of magnitude 2.0 or greater from the Japan Meteorological Agency catalog.
The results showed that there were 24 dates with a minimum ΔAIC of -2 or less. In this presentation, we plan to report the results of our investigation into the spatiotemporal relationship between the detected earthquake swarms and the slow earthquakes (e.g., very-low-frequency earthquakes and slow slip events) observed in the Hyuga-nada region (e.g., Baba et al., 2020; Okada et al., 2022).