Seismic velocity decrease and recovery related to earthquake swarms in a geothermal area
© 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. 2010
Received: 5 April 2010
Accepted: 18 August 2010
Published: 13 December 2010
We found a recurring seismic velocity decrease associated with small earthquake swarms experienced in 2007 in a geothermal area in Kyushu, southwestern Japan, by analyzing long-term changes in the autocorrelation function (ACF) of seismic noise. The seismic velocity decrease appeared just after two major periods of earthquake activity began in June and October of 2007. In both instances, conditions returned to normal within a characteristic time period of 4 months. The observed size of the velocity changes agrees well with the magnitudes of the swarms. The lag-time dependence of ACF changes can be systematically explained by seismic velocity changes induced by fluid inclusion in a small, localized area deep within the hypocenter region.
The earth changes dynamically over time. Even in extremely short time frames of less than 1 year, changes happen in its crustal structure caused by dynamic phenomena such as earthquakes and volcanic eruptions. A temporal change in seismic velocity can be detected by observing very slight differences in seismograms traveling through an affected area. In particular, a comparison of coda waves from the tail portion of the seismogram is useful since these are sensitive to small changes in seismic velocity (e.g., Snieder et al., 2002). Cross-correlation between seismograms of repeating earthquakes (Poupinet et al., 1984; Yamawaki et al., 2004) and those of man-made artificial earthquakes (Nishimura et al., 2005) has been a major method used to detect seismic velocity changes.
Seismic interferometry, a recently developed technique in seismology, enables researchers to extract Green’s functions of seismic waves between two points using correlation functions of ambient noise or coda waves (e.g., Campillo and Paul, 2003; Shapiro et al., 2005). Passive image interferometry (PII) uses this concept in detecting changes through the monitoring of auto- and/or cross-correlation of daily ambient noise (Sens-Schönfelder and Wegler, 2006; Wegler and Sens-Schönfelder, 2007), with successful applications to coseismic changes (Ohmi et al., 2008; Brenguier et al., 2008a; Wegler et al., 2009) and changes associated with volcanic eruptions (Brenguier et al., 2008b). There are two physical mechanisms that may explain postseismic changes in velocity; one is a relaxation of fault zone damage by healing (Vidale and Li, 2003); the other is a nonlinear change in very shallow subsurface regions caused by the incidence of strong motion (Sawazaki et al., 2006). For velocity changes associated with large earthquakes, it is quite difficult to distinguish which mechanism is responsible. Here, using the PII technique, we found a temporal change in the seismic velocity that recurred in association with swarms of small earthquake activity.
2. Earthquake Swarm Activity in NE Kyushu, Japan
3. Temporal Changes in Noise Autocorrelation
We investigated temporal changes in the autocorrelation function (ACF) of ambient noise at the nearest station (OITA2) operated by the Japan Meteorological Agency (JMA), from the epicenter region of the seismic swarm. An ACF for every 1-h period was calculated from a continuous record of the short-period velocity seismometer with a sampling frequency of 100 Hz and natural frequency of 1 Hz. The data was first filtered for the 1–3-Hz bandwidth, then one-bit normalization (Shapiro et al., 2005) was applied to minimize the effect of non-noise records, such as earthquakes. Averaging ACFs for 24 h gives a 1-day ACF, which is used to monitor temporal changes.
4.1 A cause of the temporal change
Such change in ACF can be explained not only by seismic velocity change but also by rainfalls (Sens-Schonfelder and Wegler, 2006) or contamination by earthquake signal. During the period when the earthquake swarm was very active (June, 2007 and October 30, 2007), the continuous record at the OITA2 mainly consisted of an earthquake signal; this is no longer a noise, which may violate the assumption of the seismic interferometry. However, the change in ACF lasted about 4 months, which is longer than the characteristic period of the earthquake swarm. We also manually confirmed that very few earthquake signals appeared in the continuous record at the frequency band of 1–3 Hz after 1 month from the first activity, and after 2 days from the second activity, respectively. Although the rainy season in this area stretches from June to July, there is no such seasonal effect in October that could cause such changes. In addition, we confirmed that no seasonal changes in ACFs occurred in other years of 2006 and 2008 at the same station. Therefore, this observation is most plausibly explained by seismic velocity decrease and recovery. The ACF of ambient noise can be interpreted as a sum of waves backscattered by random heterogeneity in the lithosphere with the wave source and receiver at the same location (Sens-Schönfelder and Wegler, 2006) based on the single scattered theory (Aki and Chouet, 1975).
4.2 Localized velocity change
The fact that the ACF phase delay was detected only at OITA2 in a limited time range suggests that the velocity change is localized near the OITA2 station. The temporal change should not be attributed to the incidence of strong motion because the earthquakes were too small to cause nonlinear effects in shallow sediments. If the velocity change occurred very close to the station, the expected ACF delay would have shorter lag times than those actually observed. From these observations, we propose that the observed ACF packet consists mainly of backscattered body or Rayleigh waves that pass through the hypocenter or epicenter area.
We observe significant phase delay of ACF continued for 1 month for the first earthquake swarm. The maximum observed ACF delay is 0.15 s at a lag time of 10 s (Fig. 3), resulting in a 1.5% velocity decrease. This determination assumes that the seismic velocity decreased homogeneously in a vast area that includes the seismic station and takes into account that ACF phase delay increases proportionally to lag time (Sens-Schonfelder and Wegler, 2006; Brenguier et al., 2008a). However, it is noteworthy that the lag-dependent ACF phase delay for both active periods is localized at similar lag times of about 6–12 s, even though the two periods had different hypocenter locations. This also suggests that the location of the changes are localized in a common area.
Since both body and Rayleigh waves may contribute to the Green function in this frequency range, there is no direct evidence to distinguish that which is dominant in the observed ACF. If we assume the backscattered Rayleigh waves to be a cause of the temporal change, a possible location of the velocity change should be at around the ground surface. Isochrones for Rayleigh waves at lag times of 6, 8, and 10 s are located at a horizontal distance of 6, 8, and 10 km from the station, respectively, assuming 2 km/s as a group velocity of the Rayleigh waves. Due to the sensitivity of Rayleigh waves in depth, the maximum depth of velocity change is limited to about 1 km in this frequency range. If the constituents of the ACF are Rayleigh waves, the non-linear effect in the sediment layer at just above of the hypocenter could be one reason for the velocity change. However, the location of velocity change is 6–10 km from the station; this may be too far from the epicenter, especially for the second activity that occurred at beneath the OITA2 station.
It is noteworthy that isochrones of the P-wave for the lag time of 6 s and those of the S-wave of 10 s sample almost same depth of just beneath of the hypocenter area. At these two lag times, we observe small and large temporal changes at these time windows. One possible interpretation is that the temporal changes occur at the common small area beneath the epicenter area at depths of ~15 km, and the change is observed by backscattered P-waves and S-waves (Fig. 5(b)). Since this area is a part of the Hohi volcanic area (Kamata, 1989), which has an extensional stress field, it is possible that the velocity change is caused by the inclusion of fluid from deep within the hypocenter area. An intermittent fluid supply may cause the earthquake swarm in association with the sudden velocity decrease. Fluid injection up into shallower regions triggered the two earthquake swarms, dominated by normal-fault type earthquakes. The north/south-oriented extensional stress field may promote crack opening, resulting in the fluid injection. Comparison of seismic activity and the magnitude of the ACF phase delay suggests that the volume of the first fluid injection was much larger than that of the second. Recovery of seismic velocity may be caused by diffusion of the fluid within the crust. That this injection occurred deep within the crust may explain why no geodetic change was associated with the swarms.
5. Concluding Remarks
Using the PII technique, we found reproducible temporal changes in seismic velocity in association with seismic swarm in NE Kyushu, Japan. By monitoring daily changes in noise ACF, a systematic phase delay lasting about 4 months was observed for two major periods of earthquake activities. From the lag-time dependence of ACF, a localized seismic velocity decrease which relates to the swarm activity is proposed as a possible model that may explain the observation.
The PII technique enables researchers to monitor the crustal structure at a temporal resolution of 1 day with small computational effort, which is quite suitable for real time processing. Even with changes in the crustal structure that are not geodetically detectable as in this study case, the noise correlation may contain information on the dynamic process of crustal structure through seismic velocity change. Standing, long-range monitoring that provides dense array data is becoming more available. The use of recently installed systems such as the Hi-net (Okada et al., 2004) and USArray (Meltzer et al., 1999), can support wider application of this new tool for the monitoring of crustal structure.
The authors would like to thank Tatsuhiko Hara, Ulrich Wegler, and an anonymous reviewer for their valuable comments. We used continuous seismic traces recorded by the Japan Meteorological Agency at the OITA2 station. A part of this research was carried out as a part of the “Research Project for Crustal Activity based on Seismic Data” conducted by the National Research Institute for Earth Science and Disaster Prevention.
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