Seismic quiescence and activation anomalies from 2005 to 2008 beneath the Kanto district, central Honshu, 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. 2013
Received: 30 October 2012
Accepted: 21 June 2013
Published: 6 December 2013
In the present study, an earthquake catalog is used that lists 1,197 earthquakes with M ≥ 3.9. All of the earthquake waveforms were recorded by the Earthquake Research Institute, University of Tokyo. These waveforms have been manually re-examined, and hypocenters and magnitudes re-calculated. A detailed analysis of the re-determined earthquake catalog between 1996 and 2007, using a gridding technique (ZMAP), shows a pair of seismic quiescence and activation anomalies that start around the middle of 2005, and last about 30 months. The pair of quiescence and activation anomalies are located very close to each other, and the Z-values are +5.0 and −3.8 for a time window of T w = 1.5 years, using a sample size of N = 100 earthquakes. The anomaly pair is not a coincidence as is confirmed by a numerical simulation with the assumption of random seismicity. One possible hypothesis is presented to explain the seismicity anomaly: a long-term slow slip event (LSSE) occurs on the upper boundary of the subducting Pacific plate, and the seismic quiescence and activation anomalies are caused by the Coulomb failure stress change associated with the LSSE.
A slow slip event (SSE) is a very slow faulting without the generation of high-frequency seismic waves. In general, the slip velocity is ~1 m/s for ordinary earthquakes and ranges from ~1 cm/day to ~1 cm/year for SSEs. In this study, we define a short-term SSE (SSSE) as an event with a slip velocity of 1 to 10 cm/day, and a long-term SSE (LSSE) as an event with a slip velocity of 1 to 10 cm/year. SSSEs with a duration time of several days are usually detected by a dense Global Positioning System (GPS) network: the Nankai subduction zone (Obara et al., 2004), the Boso (Ozawa et al., 2007), the Cascadia subduction zone (Dragert et al., 2001), the Cocos-Caribbean subduction zone (Outerbridge et al., 2010), and Kilauea volcano (Cervelli et al., 2002). Some SSSEs are accompanied by earthquake swarms. For example, Hirose et al. (2012) showed clear evidence of swarm activities near Boso Peninsula, central Japan, likely induced by stress perturbations caused by SSEs in the region. Moreover, these authors also demonstrated that the SSEs themselves could be triggered by static stress transfer from large earthquakes. On the other hand, LSSEs with a duration time of several years are also detected by a dense GPS network: the Bungo channel (Hirose and Obara, 2005) and the Tokai region (Ozawa et al., 2002) in the Nankai subduction zone of Japan, the south central Alaska subduction zone (Ohta et al., 2006), the Guerrero seismic gap of the Mexican subduction zone (Yoshioka et al., 2004), and the Manawatu region of North Island, New Zealand (Wallace and Beavan, 2006).
Not only GPS data but also a temporal change in seis-micity rate might enable us to detect SSEs. For example, seismicity rate changes were observed associated with a large LSSE from 2000 to 2005 in the Tokai district (Matsumura, 2006; Kobayashi and Hashimoto, 2007). Based on a rate- and state-dependent friction law, Dieterich et al. (2000) developed a method to calculate the stressing rate by using earthquake rate changes. Toda and Matsumura (2006) applied Dieterich’s method to the seismicity data in the Tokai district, and found that the seismicity rate changes were well explained by the stress perturbation caused by the Tokai LSSE. Llenos and McGuire (2011) also presented an inversion method to detect the variation in stressing rate by using the earthquake occurrence rate.
However, there are important shortcomings for investigating precise and reliable temporal changes in long-term seismicity. The homogeneity of an earthquake catalog is critical for the analysis and, in general, most catalogs are not homogeneous temporally and spatially (Habermann, 1987, 1991). For this reason, a homogeneous earthquake catalog needs to be constructed prior to such an analysis. Apparent changes in seismicity rates are easily produced by artificial effects, including the deployment of new seismograph stations, the closing of old seismograph stations, and changes to the seismograph, waveform-recording system, and magnitude estimation algorithm (Habermann and Creamer, 1994; Zuniga and Wiemer, 1999).
The purpose of this study is to detect seismicity rate changes in the Kanto district, central Honshu, Japan, from an analysis of earthquake catalog data, and to propose a slow-slip model to explain these seismicity changes. First, all waveform data are carefully re-examined and hypocen-ters and magnitudes are re-determined in order to obtain a reliable earthquake catalog that is spatially and temporally homogeneous. Second, the temporal change in seismicity is then defined quantitatively using an algorithm in the program ZMAP (Wiemer and Wyss, 1994), and, finally, we present a discussion regarding an LSSE, which seems to be a possible model to explain the seismicity rate changes detected in this study.
The most important requirement for an earthquake rate analysis is the use of a homogeneous earthquake catalog. Recently, many new seismographic stations have been installed in the Kanto region. At present, the network maintained by the Earthquake Research Institute, University of Tokyo (ERI), consists of about 660 short-period seismograph stations, including Hi-net (the high-sensitivity seismographic network) and stations maintained by JMA. All the waveform data are converted to a digital form at 100 Hz, sent to the ERI via a communication satellite, exclusive telephone lines, or an internet connection, and stored on both hard disks and 8-mm video tapes.
In the present study, earthquakes were selected from the ERI catalog that satisfied the following conditions: (1) they occurred between 1 January, 1996, and 31 December, 2007; (2) they were located in the study area shown in Fig. 1; and (3) they had magnitudes of M ≥ 3.3. Approximately 160,000 events satisfied conditions (1) and (2). Of these, 3,138 events satisfied the condition (3), and the waveforms associated with these events were re-examined.
Characteristic parameters for ZMAP in this study.
Time interval of earthquake catalog
1 January 1996–31 December 2007
Time length of earthquake catalog (days)
M ≥ 3.9
Depth range of hypocenters (km)
The total number of earthquakes
0.05± × 0.05±
Radius of resolution circles (km)
r ≤ 60
The number of effective grids
Length of bin (days)
Time step of T s (years)
The number of time steps
The number of earthquakes for each grid
T w (years)
Small and large Z-values obtained by ZMAP analysisa.
Parameters of seismic quiescence and activation anomalies associated with an LSSE.
Radius of resolution circle r (km)
Sample size N
Window length T w (years)
Relative significance Z
Start date T s (decimal years)
3.2 False alarms
Another method of displaying Z-value anomalies that could be false alarms is by the use of alarm cubes (Wiemer, 1996; Wyss et al., 1996; Wyss and Martirosyan, 1998). In these three-dimensional figures (Fig. 6(b)), the horizontal axes are the spatial coordinates in the study area and the vertical axis is time. Anomalies are defined as instances of Z-values larger than +5.0, or smaller than −3.8, at any node and at any time. Figure 6(b) illustrates visually that the results are reliable. As listed in Table 2, the alarm cube includes three outstanding anomalies starting around 2006. Anomaly 1 is the only anomaly with a Z-value smaller than −3.8, all time period in this catalog. On the other hand, there are some nodes with a Z-value larger than +5.0 between 1996 and 2001, suggesting that similar anomalies with Z-values larger than +5.0 occur frequently. As is mentioned in Section 3.5, however, the probability that Anomalies 1 to 3 occur at close locations in space and time, as a coincidence, is very low.
3.3 Anomaly volumes
The earthquake catalog we have analyzed includes clustered events (e.g., aftershocks, earthquake swarms), thus the increase in the seismicity rate in the area of Anomaly 1 might be caused by clustered events. An earthquake with M 6.0 occurred on 23 July, 2005 (Fig. 7(a)), which is the largest event around Anomaly 1 between 2005 and 2008. The main shock was followed by four aftershocks with M 3.7−4.6 within three days. Furthermore, no earthquake swarm was observed in and around Anomaly 1, and no earthquake with M 7.0 or larger occurred between 2004 and 2008 within 200 km from Anomaly 1. Therefore, we conclude that it is unlikely that clustered events are the main reason for the increase in seismicity rate which continued for more than one year.
Epicenters within Anomaly 3 are located near the asperity ruptured by the 2008 Ibaraki-oki main shock. Epicenters within Anomalies 1 and 2 are located west of the focal area of the 2008 Ibaraki-oki main shock (Fig. 7(a)). The depth of hypocenters is systematically increasing westward from Anomalies 3 to 1 along the subducting Pacific plate (Fig. 7(b)).
3.4 Apparent seismicity changes due to the magnitude shift
3.5 Statistical significance of the results
In order to estimate the statistical significance of the Z-value anomalies detected in this study, synthetic seismicity data is generated, and Z-value matrices and the distributions of maximum Z-values, Zmax, and minimum Z-values, Zmin, are determined. The method of the numerical simulation has been explained by Katsumata (2011) in detail. In this study, the procedure is repeated 12,500 times and Zmax and Zmin are evaluated for each repetition.
However, it may still be considered unlikely that three such anomalies would occur at almost the same time and location. In particular, the onset times of Anomalies 1 and 2 are 2006.0 and 2005.6, respectively, which is a difference in time of dt = 0.4 years. The location of Anomalies 1 and 2 are (36.05±N, 140.35E) and (35.90±N, 140.90E), respectively, which is a difference of latitude dlat = 0.15± and a difference of longitude dlon = 0.55±. We find that the probability that two such anomalies, as closely located in time and space, would occur by chance is smaller than 0.008%; that is, there is no pair of quiescence and activation that simultaneously satisfies the three conditions, dt < 0.4 years, dlat ≤ 0.15±, and dlon ≤ 0.55±, in 12,500 times. Therefore, we conclude that the pair of Anomalies 1 and 2 is significant statistically.
4.1 Effect of a declustering process
In this study, clustered events such as aftershocks and earthquake swarms are not removed. In this section, we apply a declustering process to the earthquake catalog used in this study, and show that this has no effect on the results obtained. First, the study area is divided into six subareas: (Area name, latitude range, longitude range, the number of earthquakes in the area) = (Area 1, 34.5–36°N, 139–140°E, 97), (Area 2, 36–37.5°N, 139–140°E, 101), (Area 3, 34.5–36°N, 140–141°E, 302), (Area 4, 36–37.5°N, 140–141°E, 232), (Area 5, 34.5–36°N, 141–142.5°E, 113), and (Area 6, 36–37.5°N, 141–142.5°E, 352). To test the hypothesis that the seismicity in each of the subareas is stationary Poissonian associated with time, we quantitatively evaluate the discrepancies between the observed frequency distribution and the expected Poissonian distribution by the ×2-test (e.g., Gardner and Knopoff, 1974; Wyss and Toya, 2000). Based on the results of the χ2-test for goodness of fit, we remove earthquake clusters so that the seismicity fulfills the hypothesis.
The results of the χ2-test in other areas are as follows: χ2 = 1.654 with 3 degrees of freedom in Area 1, χ2 = 1.402 with 3 degrees of freedom in Area 2, χ2 = 0.001 with 2 degrees of freedom in Area 4, χ2 = 1795205.845 with 6 degrees of freedom in Area 5, and χ2 = 38046.991 with 7 degrees of freedom in Area 6. Declustering is not needed in Areas 1, 2, and 4. On the other hand, some clustered events should be removed in Areas 5 and 6 so that the seismicity fulfills the Poisonnian hypothesis. After the declustering process, the χ2 statistic decreases to χ2 = 0.010 with 1 degree of freedom for Area 5, and χ2 = 0.060 with 3 degrees of freedom for Area 6.
As a result of applying the declustering process, 39 earthquakes are removed in the whole area, which is only 3% of the non-declustered catalog. We find that the Anomalies 1 to 3 are clearly imaged even if the declustered catalog is used. Therefore, we conclude that the declustering process has no effect on the results obtained in this study.
4.2 A long-term slow slip event (LSSE) model
Whereas the seismicity rate changes we observe may be explained by various models, our preferred explanation is that it is caused by a long-term slow slip event (LSSE) rather than, for example, a dilatancy model or an earthquake interaction model. For an interpretation of the foreshock sequence (Hirose et al., 2011) prior to the 2011 Tohoku earthquake (M 9.0), there are two conflicting hypotheses: a slow slip event model (Ando and Imanishi, 2011; Kato et al., 2012) and an earthquake interaction model (Marsan and Enescu, 2012; Gusman et al., 2013). The earthquake interaction model does not need to invoke aseismic transients for explaining the foreshock sequence.
On the assumption that an LSSE causes between the PA plate and the overriding plate, we assume a simple rectangular fault model. The fault size of the LSSE is 66 km × 41 km so that it includes the epicenters within the Anomaly 2 volume. The strike, the dip, and the rake angles are assumed to be 200°, 18°, and +123°, respectively, which are the same as those of the subducting PA plate in this region. The slip is assumed to be 9.5 cm in the period between 1 July, 2005, and 31 December, 2007, for 2.5 years. Thus, if the rigidity is 3.0 × 1010 N/m2, the seismic moment is M0 = 7.7 × 1018 N m (Mw = 6.5). The fault motion, 9.5 cm per 2.5 years, is assumed so that GEONET is not able to detect the surface deformation.
4.3 ΔCFS caused by the LSSE model
The ΔCFS estimations depend on the assumptions made for the position and setting of the “source” and “receiver” faults, as well as the effective friction coefficient (μ) value. Reliable estimations require some quantification of the uncertainties involved by these various assumptions (e.g., Parsons et al, 2008; Aoi et al., 2010). We have discussed above the stress change variations due to changes in the “receiver” focal mechanism. In addition, since μ = 0.4 is assumed for the main computations, we have also checked the stress change dependence on the μ coefficient. We found that the absolute values of ΔCFS vary by a factor of 3 to 4 if μ changes from 0.0 to 1.0. Thus, even if μ changes from 0.0 to 1.0 in this study, the absolute value of ΔCFS does not vary by an order. In the areas of positive ΔCFS, the values are ~0.0005 MPa if μ = 0.0, ~0.002 MPa if μ = 1.0. In the areas of negative ΔCFS, the value is in the order of 0.01 MPa even if μ changes from 0.0 to 1.0.
4.4 Does the small positive ΔCFS trigger earthquakes?
As described in Section 4.3, the increase in ΔCFS in the activation area is in the order of 0.001 MPa, which is approximately 10 times smaller than the smallest stress level (0.01 MPa) that has been reported for the triggering of nearby earthquakes (Anderson and Johnson, 1999). The shear stress in the order of 0.001 MPa is almost the same as that produced by the Earth’s tide. Since there is no clear correlation between tidal stress and seismicity (Emter, 1997), many researchers believe that static stress changes of the same magnitude are not expected to trigger earthquakes. On the other hand, Ziv and Rubin (2000) reported that, in central California, static stress changes in the order of 0.001 MPa have a noticeable triggering effect. While tidal stresses are periodic, stress changes due to the LSSE continues for a long time. As the duration over which a stress change acts increases, its effect on the time of a future earthquake becomes larger (Dieterich, 1994). This is a candidate to explain why ΔCFS as small as the Earth’s tide causes an increase in seismicity.
There is another possibility. Tidal stress perturbations can trigger earthquakes only when a region reaches a critical state for failure in the case of large/great earthquakes. Tidal triggering was detected only in several to ten years preceding the 2004 Sumatra earthquake (Mw 9.1) and vanished afterwards (Tanaka, 2010). The same observation was reported prior to the 2011 Tohoku-oki earthquake (Mw 9.0) (Tanaka, 2012). Therefore, if the activation area detected in the present study had been close to failure, small ΔCFS can trigger some earthquakes and, thus, the seismicity increases. Nakajima and Hasegawa (2010) obtained detailed images of P- and S-wave velocity structures beneath the Kanto district, and found the serpentine boundary which is the western boundary of the serpentinized mantle in the PH plate, and P- and S-wave velocities varied by 15–20% across it over a short distance of ~10 km. They presented a hypothesis that the PH plate is divided into eastern and western parts by the serpentine boundary: the eastern part is the metamorphosed (serpentinized) mantle and the western part is the unmetamorphosed (dry) mantle, and the relative movement between the two parts is a right-lateral motion. In fact, at least two large earthquakes with M ~ 7 occurred along the serpentine boundary, the 1921 Ibaraki earthquake (M 7.0) and the 1987 Chiba earthquake (M 6.7), which probably ruptured asperities along the serpentine boundary. Nakajima and Hasegawa (2010) suggested that taking the deformation rate into account, a 1921-type earthquake would be expected to occur in the near future along the serpentine boundary. We find that the hypocenters within the volume of the activation area (Anomaly 1) appear to be located along the serpentine boundary (Fig. 11(b)). If this area corresponds to an asperity that reaches a critical state for failure, small positive ΔCFS can cause an increase in long-term seismicity.
The earthquake catalog used in this study is of unusually high quality from the point of view of homogeneous reporting in the Kanto area (Fig. 1). In general, earthquake catalogs are not homogeneous temporally and spatially (Habermann, 1987, 1991). Therefore, as described in Section 2, the catalog are carefully re-examined before performing a statistical analysis of long-term seismicity changes. As a result, we have found a pair of the seismic quiescence and activation anomalies beneath the Kanto district, and have confirmed that they are significant anomalies statistically. A possible physical model to explain the seismic quiescence and activation simultaneously might be an LSSE model on the upper boundary of the subducting PA plate. We show that the spatial pattern of ΔCFS caused by the LSSE model matches qualitatively with that of the seismic anomalies. The LSSE fault plain is located at a high Poisson’s ratio area, indicating that the dehydration reaction occurs in the oceanic crust of the subducting PA plate. This fact suggests that the LSSE seems to be a plausible model in the present study. For future studies, it is worth noting that a seismicity rate change might be an indicator of an LSSE, and an LSSE with a small displacement can be detected by a careful investigation of the seismicity rate.
We thank Stefan Wiemer for providing the ZMAP software, Junichi Nakajima for providing the tomography data, and two anonymous reviewers for valuable comments. GMT-SYSTEM (Wessel and Smith, 1991) was used for data mapping. MICAP-G (Naito and Yoshikawa, 1999) was used to calculate changes in the Coulomb failure stress. Naoto Wada helped to produce the homogeneous earthquake catalog.
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