Correlation between Coulomb stress changes imparted by large historical strike-slip earthquakes and current seismicity in Japan
© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences; TERRAPUB. 2011
Received: 25 June 2010
Accepted: 27 January 2011
Published: 4 March 2011
To determine whether current seismicity continues to be affected by large historical earthquakes, we investigated the correlation between current seismicity in Japan and the static stress changes in the Coulomb Failure Function (ΔCFF) due to eight large historical earthquakes (since 1923, magnitude ≥ 6.5) with a strike-slip mechanism. The ΔCFF was calculated for two types of receiver faults: the mainshock and the focal mechanisms of recent moderate earthquakes. We found that recent seismicity for the mainshock receiver faults is concentrated in the positive ΔCFF regions of four earthquakes (the 1927 Tango, 1943 Tottori, 1948 Fukui, and 2000 Tottori-Ken Seibu earthquakes), while no such correlations are recognizable for the other four earthquakes (the 1931 Nishi-Saitama, 1963 Wakasa Bay, 1969 Gifu-Ken Chubu, and 1984 Nagano-Ken Seibu earthquakes). The probability distribution of the ΔCFF calculated for the recent focal mechanisms clearly indicates that recent earthquakes concentrate in positive ΔCFF regions, suggesting that the current seismicity may be affected by a number of large historical earthquakes. The proposed correlation between the ΔCFF and recent seismicity may be affected by multiple factors controlling aftershock activity or decay time.
In Japan, small-magnitude earthquakes have been detected by a recently developed dense seismic network of high-sensitivity seismographs. Source processes of large historical earthquakes during the past century have been obtained from various seismological and/or geological datasets. In this study, we investigate the correlation between the ΔCFF due to large historical earthquakes and recent seismicity. Our results suggest that the recent earthquake catalog possibly includes numerous aftershocks of some large historical earthquakes.
We also use focal mechanism solutions of moderate earthquakes (M ≤ 2.5) from a catalog of the National Research Institute for Earth Science and Disaster Prevention (NIED) from October 1997 to May 2010. This catalog is based on the waveform data obtained by the Full-Range Seismograph Network (F-net) (Fig. 1(b); Fukuyama etal., 1998). There are 2,271 focal mechanisms in Fig. 1(b). Hereafter, we refer to these focal mechanism solutions as F-net solutions.
In our calculation of the ΔCFF associated with large historical earthquakes, we assume an elastic half-space, an apparent coefficient of friction of 0.4, a shear modulus of 32 GPa, and a Poisson’s ratio of 0.25. Multiple fault models have been proposed for some earthquakes; for these earthquakes, we investigate how uncertainty due to different fault parameters affects the results.
We calculate ΔCFF for two types of receiver faults: the mainshock solution and the F-net solutions. For the mainshock focal mechanism receiver fault, the calculation depth is basically set at the center of the fault plane. The calculated ΔCFF is compared with the distribution of recent earthquakes to determine if a spatial correlation exists. Strike-slip faults are basically nearly vertical, and the depth changes in the ΔCFF are small; hence, the ΔCFF distribution is robust. Spatial heterogeneity of earthquake detection capability can be neglected because of the relatively small target region; hence, we use all of the earthquakes in the catalog without setting a magnitude threshold. We have verified that the conclusion does not change whether the completeness magnitude is considered or not.
Calculation of the ΔCFF for assumed receiver faults may generate large errors under a complex regional stress field in which various types of earthquakes occur, and this uncertainty can be substantially reduced by using focal mechanisms as receiver faults (e.g., Toda, 2008). Thus, we also calculate the ΔCFF associated with large historical earthquakes for receiver faults of the F-net solutions. For earthquakes that occurred prior to the 2000 Tottori-Ken Seibu earthquake, we do not include the ΔCFF due to this earthquake.
We set the lowest and highest ΔCFF thresholds. For low absolute ΔCFF values, the sign can easily reverse (e.g., because of errors in hypocentral location). Furthermore, the number of earthquakes with low absolute ΔCFF values depends on the spatial extent of the study area. If we adopt a very broad region, the ΔCFF values of almost all of the earthquakes are nearly zero, leading to the conclusion that recent seismicity is not correlated with the ΔCFF associated with large historical earthquakes. Thus, we omit earthquakes with absolute ΔCFF values of <0.1 bars, which is the minimum threshold commonly associated with static stress triggering (e.g., Reasenberg and Simpson, 1992; Hardebeck et al., 1998; Harris, 1998). However, significantly high absolute ΔCFF values, which can be observed to be very close to rupture faults, may have large uncertainties, perhaps because of simplified source geometry and slip distribution. Therefore, we also omit earthquakes with absolute ΔCFF values > 15 bars and those that occurred within 5 km of the rupture source faults.
4.1 Spatial correlation between recent seismicity and ΔCFF for mainshock receiver faults
4.1.1 1927 Tango (M 73), 1943 Tottori (M7.2), and 2000 Tottori-Ken Seibu (M7.3) earthquakes
Figure 2(c) depicts the epicentral distribution of recent seismicity from the unified JMA catalog and the ΔCFF associated with these three earthquakes; recent seismicity can be seen to significantly concentrate on the positive ΔCFF regions, especially in the eastern and western extents of the 1943 Tottori earthquake source fault. The fault mechanism of the Tottori earthquake is assumed to be a receiver fault mechanism, although the result is almost the same whether the fault mechanism of the 1927 Tango or that of the 2000 Tottori-Ken Seibu earthquake is used because they occurred on conjugate fault systems. The spatial correlations between the positive ΔCFF regions and recent seismicity are also found even if other fault models (e.g., Chinnery, 1961, 1964; Kanamori, 1972, 1973; Sato, 1973) are used as a source fault. Linear seismic activity along the source faults can be recognized. The Kita-Tajima earthquake (M 6.8) occurred on May 23, 1925, between the source region of the Tango and Tottori earthquakes, and current seismicity may include the aftershocks of this earthquake. Inclusion of the ΔCFF due to this earthquake in the analysis may enable a more reliable analysis; however, the detailed rupture process has not been revealed. The 1927 Tango earthquake increased the ΔCFF at the hypocenter of both the 1943 Tottori and the 1963 Wakasa Bay earthquakes (discussed later); the 1943 Tottori earthquake increased the ΔCFF by 0.1 bars at the hypocenter of the 2000 Tottori-Ken Seibu earthquake, and thus probably accelerated the earthquake occurrences.
4.1.2 1931 Nishi-Saitama earthquake
4.1.3 1948 Fukui
Figure 4(b) depicts the epicentral distribution of recent earthquakes and the ΔCFF due to the 1927 Tango, 1948 Fukui, 1961 Kita-Mino, 1963 Wakasa Bay, and 1969 Gifu-Ken Chubu earthquakes, assuming the focal mechanism of the Fukui earthquake as a receiver fault mechanism. Around the source region of the Fukui earthquake, positive ΔCFF regions correlate well with recent seismicity, suggesting that the ΔCFF associated with the Fukui earthquake still affects recent seismicity. However, no significant correspondence between recent seismicity and positive ΔCFF regions can be observed around the source region of the 1963 Wakasa Bay earthquake, even if the focal mechanism of the Wakasa Bay earthquake is assumed to be a receiver fault mechanism. Seismic activity around the Wakasa Bay earthquake is significantly quiet compared with that of the surrounding region, indicating that a seismic gap exists and suggesting that the aftershock activity of the Wakasa Bay earthquake has returned to the background seismicity level.
4.1.4 1969 Gifu-Ken Chubu (M6.6) and 1984 Nagano-Ken Seibu (M6.8) earthquakes The Gifu-Ken Chubu earthquake occurred on September 9, 1969, and the Nagano-Ken Seibu earthquake occurred on September 14, 1984, in central Honshu. We adopt the fault models of Mikumo (1973) and Mikumo et al. (1985).
4.2 Correlation between recent seismicity and ΔCFF for F-net focal mechanism solutions
5.1 Possible factors generating uncertainties in estimating the ΔCFF
Recent seismicity correlates well with the positive ΔCFF regions associated with the four large historical earthquakes (the 1927 Tango, 1943 Tottori, 1948 Fukui, and 2000 Tottori-Ken Seibu earthquakes), but no distinct correlations are found for the other four earthquakes (the 1931 Nishi-Saitama, 1963 Wakasa Bay, 1969 Gifu-Ken Chubu, and 1984 Nagano-Ken-Seibu earthquakes) in terms of the main-shock receiver faults. Furthermore, the probability distribution of moderate earthquakes plotted against the ΔCFF clearly indicates that recent earthquakes concentrate in positive ΔCFF regions. However, a number of possible factors generate uncertainties in the correlation between the ΔCFF and recent seismicity.
One possible factor is the simplicity of the model. First, the ΔCFF associated with the 1927 Tango, 1931 Nishi-Saitama, 1963 Wakasa Bay, 1969 Gifu-Ken Chubu, and 1984 Nagano-Ken Seibu earthquakes are calculated using uniform slip models. More reliable discussion will be possible using variable slip models.
Second, the background seismicity rate is assumed to be uniform throughout the target region, in comparison with the ΔCFF and recent seismicity. The ΔCFF can be quantitatively correlated with changes from the background seismicity rate using earthquake and/or focal mechanism catalogs before and after large earthquakes (e.g., Dietrich, 1994; Aoi et al., 2010). However, estimating a spatially heterogeneous background seismicity rate is not straightforward because of inadequate knowledge on small-magnitude earthquakes and focal mechanisms in earlier catalog duration, and the short reference period. For example, the available catalog duration is only 4 years for the 1927 Tango earthquake because the JMA catalog started in 1923. Based on this scarcity of data, we are therefore not able to absolutely deny that it may be purely coincidental that positive ΔCFF regions associated with large historical earthquakes match high background seismicity rate zones.
Third, spatial heterogeneity of the receiver fault mechanism is not considered for the mainshock receiver faults. Our study findings clearly indicate that the specified receiver fault mechanism may generate large errors and sometimes fail to obtain fair conclusions in a complex regional stress field, and that this uncertainty can be substantially reduced by using F-net solutions as receiver faults.
Fourth, the temporal decay dependence on the lapse time from the mainshock is neglected. Decay of aftershock activities is known to be expressed by the Omori-Utsu law (Utsu, 1961), and the seismicity rate change after a large earthquake can be described using the rate- and state-dependent friction law (e.g., Dietrich, 1994). Aftershock activity and stress perturbation accompanying large earthquakes decay with time, and a better quantitative analysis can be performed by considering this effects (e.g., Toda and Enescu, 2011).
Other stress changes (e.g., dynamic stress changes and/or pore-pressure changes accompanying fluid migration) may also be major factors controlling seismicity rate changes. More recent large earthquakes may load on top of the ΔCFF associated with large historical earthquakes and mask it, even if changes from the background seismicity rate continue.
5.2 Effect on reliable estimation of background seismicity rate
Background seismicity rate, a fundamental parameter describing seismicity, can be used to forecast future earthquakes because anomalous seismicity (e.g., seismic quiescence or activation prior to large earthquakes) has been recognized as a deviation from background seismicity rate (e.g., Inouye, 1965; Utsu, 1968; Mogi, 1969; Kelleher and Savino, 1975; Ohtake et al., 1977; Habermann and Wyss, 1984; Wyss, 1986; Kisslinger, 1988; Taylor et al., 1991; Imoto, 1992; Miyaoka and Yoshida, 1993; Odaka and Maeda, 1994; Wiemer and Wyss, 1994; Takanami et al., 1996; Katsumata and Kasahara, 1999; Enescu and Ito, 2001; Huang et al., 2001; Huang and Nagao, 2002). Furthermore, various earthquake forecasting models based on the background seismicity rate have been proposed in the Collaboratory for the Study of Earthquake Predictability (CSEP), which was recently started in Japan (Nanjo et al., 2011; Tsuruoka et al., 2011). Reliable estimation of the background seismicity rate is essential for detecting such anomalous seismicity and for testing such forecast models.
However, the definition and the measurement of background seismicity rate are still controversial, and different approaches are used in the literature (e.g., Hainzl and Ogata, 2005). Cocco et al. (2010) recently redefined background seismicity rate as a time-independent smoothed seismicity rate estimated in a prescribed time window using a declustered catalog, and reference seismicity rate as a time-independent smoothed seismicity rate estimated using an undeclustered catalog. These authors investigated the effects of the two approaches on seismicity forecasts.
Reliable and unbiased estimations of background seismicity rate are sometimes accompanied by various difficulties. For example, earthquake catalogs may include artificial seismicity rate changes (e.g., installation or closure of seismic stations, changes of instrument for seismic observation, and systematic changes in the magnitudes assigned to events) (Habermann, 1981, 1982a, b, 1983, 1987, 1991; Perez and Scholtz, 1984; Wyss and Burford, 1985; Katsumata and Kasahara, 2004; Katsumata, 2006). Eneva et al. (1994) carefully investigated seismicity rate changes in the Garm district, Tajikistan, and concluded that most seismicity rate changes are artificial.
Another difficulty is the limitation of earthquake catalog duration. Large earthquakes change the stress field in the surrounding region and generate numerous smaller-magnitude earthquakes, or aftershocks. Various declustering algorithms classifying earthquakes into background or triggered seismicity (aftershocks) have been investigated (e.g., Reasenberg, 1985; Zhuang et al., 2002). Such declustering may not suffer much when the contribution of historical earthquakes is not included because many earthquakes are actually triggered by more recent smaller neighboring events. However, significant correlations between the ΔCFF and recent seismicity for some large historical earthquakes strongly suggest that the background seismicity rate estimated from earthquake catalogs is possibly affected by a number of large earthquakes that occurred prior to the start of the catalog. This is consistent with the findings of Helmstetter and Sornette (2003), who reported that many earthquakes included in an earthquake catalog are indeed secondary or higher aftershocks. The background seismicity rate estimated from a region activated/deactivated by large historical earthquakes may produce apparent seismic quiescence/activation accompanied by temporal decay. Thus, the availability of forecast models based on background seismicity rate may be reduced.
6. Concluding Remarks
In the study reported here, we investigated the spatial correlation between recent seismicity in Japan and the ΔCFF associated with large historical earthquakes (since 1923, M ≥ 6.5) with strike-slip mechanisms. The recent epicentral distribution correlates well with the positive ΔCFF regions associated with four earthquakes (the 1927 Tango, 1943 Tottori, 1948 Fukui, and 2000 Tottori-Ken Seibu earthquakes). However, no significant correlations are observed for the other four earthquakes (the 1931 Nishi-Saitama, 1963 Wakasa Bay, 1969 Gifu-Ken-Chubu, and 1984 Nagano-Ken-Seibu earthquakes). The probability distribution of earthquakes with F-net solutions places a disproportionate emphasis on positive ΔCFF values. These results suggest that recent seismicity is possibly still affected by a number of large historical earthquakes and that the aftershock decay time strongly depends on each earthquake.
The ΔCFF calculated for a specified fault mechanism involves large uncertainty or fails to evaluate the correlation in complex stress fields (e.g., the 1984 Nagano-Ken Seibu earthquake source region). This study clearly indicates that this uncertainty could be substantially reduced by using F-net solutions of moderate earthquakes as receiver faults. The use of focal mechanism solutions of smaller earthquakes would enable more quantitative analyses; thus, a focal mechanism catalog that includes small-magnitude earthquakes is very important. Stress perturbation due to large earthquakes may shift the earthquake distribution for ΔCFF to a positive side after the mainshocks, under the temporally stable observation network system and station distribution.
The background seismicity rate estimated from a region activated by large earthquakes may produce apparent seismic quiescence accompanied by temporal decay, and vice versa. Therefore, those effects may be important for more reliable and unbiased estimates of background seismicity rate using an earthquake catalog, especially a relatively short catalog.
We thank Dr. Ross Stein and an anonymous reviewer, who gave us thoughtful and relevant comments and suggestions to improve this manuscript. We also thank Drs. Shinji Toda and Bogdan Enescu, who gave us their submitted paper (Toda and Enescu, 2011) before publication. We used the unified Japan Meteorology Agency (JMA) catalog with Hokkaido University, Hirosaki University, Tohoku University, Tokyo University, Nagoya University, Kyoto University, Kochi University, Kyushu University, Kagoshima University, Shizuoka Prefecture, Yokohama City, Tokyo Metropolis, JMA, Natural Research Institute for Earth Science and Disaster Prevention, National Institute of Advanced Industrial Science and Technology (AIST), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Hot Springs Research Institute of Kanagawa Prefecture. We also used F-net focal mechanism solutions determined by NIED, the Generic Mapping Tools (Wessel and Smith, 1991) for drawing figures, the TSEIS visualization program package (Tsuruoka, 1997, 1998) for the study of hypocenter data, and the program by Okada (1992) for calculating the ΔCFF. We thank all of these organizations and individuals. This study is supported by the Special Project for Earthquake Disaster Mitigation in the Tokyo Metropolitan Area from the Ministry of Education, Culture, Sports, Science, and Technology of Japan.
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