- Express Letter
- Open Access
Secular and coseismic changes in S-wave velocity detected using ACROSS in the Tokai region
© The Author(s) 2018
- Received: 14 June 2018
- Accepted: 5 September 2018
- Published: 14 September 2018
- Seismic wave velocity change
- Coseismic change
- Secular change
- Artificial seismic source
- 2011 Tohoku earthquake
Temporal variation in the propagation property of seismic waves has been studied to understand the state of the subsurface medium under various tectonic circumstances. Changes in seismic velocity are basically caused by the density variation of cracks or pores in the medium and the degree of fluid saturation in those cracks and pores (Nur 1971; O’Connell and Budiansky 1974; Hadley 1976). Therefore, stress/strain, which changes crack density, and pore pressure, which changes fluid saturation, can be detected by measuring variation of seismic velocity. This is one reason why temporal variation of seismic velocity has long been an interest in relation to earthquake generation processes, in which stress/strain and pore pressure govern the triggering process of earthquakes (e.g., Terakawa 2014).
Laboratory, theoretical, and field studies have been carried out to understand the cause of seismic velocity changes attributable to various conditions in the subsurface medium. Laboratory experiments have revealed the influences of stress, cracks/pores, and pore fluid on seismic propagation (e.g., Birch 1960; Nur and Simmons 1969; Dewhurst and Siggins 2006; Bonnelye et al. 2017). Velocity increases with stress/strain and fluid saturation, and stress sensitivity depends on the aspect ratio of the crack and/or fluid saturation. Theoretical expressions for the seismic propagation properties of rock including cracks have been studied (e.g., O’Connell and Budiansky 1974; Popp and Kern 1994; Kame et al. 2014), and the effects of crack density, fluid saturation, pressure, and strength on seismic velocity have been formulated. Field experiments using artificial seismic sources have been carried out to detect a temporal change of seismic velocity. Temporal change of seismic velocity associated with earth tide and/or barometric pressure change is detected with the use of vibrators, air guns, and piezoelectric transducers (De Fazio et al. 1973; Reasenberg and Aki 1974; Leary et al. 1979; Yamamura et al. 2003; Niu et al. 2008).
Furthermore, Kumazawa and Takei (1994) proposed the idea of using a very accurate and stable artificial seismic source to detect change in seismic velocity with high sensitivity. They proposed the Accurately Controlled Routinely Operated Signal System (ACROSS) seismic source, which generates a seismic signal with the rotation of an eccentric rotor that is accurately synchronized to a GPS clock to achieve both short- and long-term stability of signal. Yamaoka et al. (2001) demonstrated variation of travel time with a resolution of 0.1 ms, and Ikuta et al. (2002) were the first to detect coseismic change of seismic velocity induced by the strong motion of earthquakes using ACROSS with a 15-month observation period. Ikuta and Yamaoka (2004) interpreted the coseismic change as being caused by a breakage of bedrock induced by an increase in pore pressure excited by strong ground motion through an analysis of anisotropic changes of seismic velocity and strain.
In recent years, field measurement of temporal change in seismic velocity has been attempted more extensively using natural seismic sources, such as ambient noise. The coda wave interference method is widely used (Sawazaki et al. 2009; Clarke et al. 2013; Brenguier et al. 2014). A sudden decrease in seismic velocity associated with earthquakes is being observed widely (e.g., Sawazaki and Snieder 2013; Hobiger et al. 2016) and is commonly being interpreted as an effect of the breakage of subsurface bedrock or sediments.
The principal advantage of ACROSS is its stability of signal, which has been utilized for monitoring the temporal variation of seismic velocity (Yamaoka et al. 2001; Saiga et al. 2006; Yamaoka et al. 2014). This study presents the results of a 10-year monitoring with an ACROSS vibrator at the Morimachi site in the Tokai region, central Japan. During the operation period, we detected a secular variation as well as a coseismic change during the 2011 Tohoku earthquake of S-wave at regional scale within a range of 30 km.
ACROSS source and transfer functions
We calculated transfer functions (Green’s functions) by deconvolution of the signal observed at each seismic station with the force excited by the ACROSS source, which is theoretically calculated using the operation parameters of the source. As the modulation period of 50 s produces spectral peaks with an interval of 0.02 Hz, 201 discrete frequencies can be used. The deconvolution produces transfer functions in the frequency domain, and those in the time domain are obtained by inverse Fourier transformation.
We synthesize linear excitation by a linear combination of the signals of clockwise and counterclockwise rotations with appropriate phase shift (Saiga et al. 2006; Yamaoka et al. 2014). For this purpose, the direction of rotation was switched every 2 h, and neighboring two transfer functions are used.
In this study, we used the daily transfer functions of about 10 years, from March 29, 2007, to October 31, 2017. The daily transfer functions were calculated from the observation data for each day with the weighted stacking method (Nagao et al. 2010), in which the reciprocal of the data variance is used for the weight. The operation of the ACROSS source sometimes stopped due to system maintenance, malfunction, or power outrage. We analyzed operation logs, in which rotational frequency and mass position are recorded every second, to identify the periods of normal operation. About 60% of the observation period was identified as being available for our analysis.
The Tokai region, where the ACROSS source is deployed, is a source region of large interplate earthquakes between the subducting Philippine Sea Plate and the crust of the Japan Archipelago. The Philippine Sea Plate pushes the Tokai region northwestward and generates compressional strain in the NW–SE direction (Sagiya et al. 2000; Henry et al. 2001; Kumar et al. 2002). We may expect to detect temporal change associated with the subduction processes by monitoring the propagation of seismic waves in this region.
Seismic stations and calibration
We used High Sensitivity Seismograph Network Japan (Hi-net) stations around the ACROSS source as receivers. Hi-net is a dense earthquake observation network operated by the National Research Institute for Earth Science and Disaster Resilience (NIED), with sampling rate of 100 Hz. We selected 13 stations based on the signal-to-noise ratio (SNR) of the signal from the ACROSS source (Fig. 1).
The use of Hi-net digital records requires attention to measure temporal change of travel time with high accuracy. Kunitomo (2014) found that the timing of digital sampling of Hi-net occasionally shifts, resulting in false temporal change of travel time. The time shift of sampling can occur when data loggers are reset for maintenance. He developed a method to correct the sampling timing using a sensor calibration signal that is synchronized to a GPS clock. In this analysis, we applied his method for correcting the sampling timing of Hi-net data (Additional file 1). A similar problem arises when Hi-net data loggers are replaced. In this case, we could not use the correction method developed by Kunitomo (2014) because different calibration timings are adopted for different types of data loggers. Therefore, we made a correction by assuming no change in travel time due to change in structure from shortly before to shortly after data logger replacement (Additional file 2). We calculated phase difference, which reflects travel time change, for all frequency signals of the ACROSS upon data logger replacement. We selected 5–20 days within one month before and after the replacement and avoided the 3–7 days after a rainfall because rainfall can change travel time (Ikuta et al. 2002).
We calculated transfer functions that were used for reference in the analysis of temporal variation by averaging all the available daily transfer functions in the operation period. The weighted stacking method (Nagao et al. 2010) was also applied using the daily noise levels which is evaluated using the spectral signal between the peaks of ACROSS signal for the weights. The signal from the ACROSS source could be identified for stations as far as 160 km from the source. However, we only used data from 13 stations, mainly due to SNR for the short time period.
Estimated travel time changes
The secular change lasting 10 years has not been reported previously, although the same order of coseismic change has been observed. The coseismic delay at the time of the Tohoku earthquake was observed widely by the coda wave interferometry method (e.g., Minato et al. 2012; Brenguier et al. 2014). In the Tokai region, − 0.02–0% velocity changes were estimated by Brenguier et al. (2014). Our analysis is consistent with their result, but it reveals smaller deviation with better resolution. In contrast to the coseismic change, little is known about the secular change of seismic velocity in terms of both observation and origin.
Distance and azimuthal dependence
Azimuthal dependences of the secular and coseismic changes are also a key to understanding the origin of the change. We plotted the rate of secular velocity change and the amount of coseismic velocity change as a function of the azimuthal angles of each station with respect to the location of the ACROSS source. As a result, the magnitudes of the changes at stations located in the NE direction were larger than those of stations in the NW direction for both changes (Fig. 5b and d). The fitting of the distributions of velocity changes with an ellipse shows that the direction of the major axis for the secular and the coseismic changes is approximately N13∘E and N23∘E, respectively.
Possible causes of the coseismic and secular changes
Coseismic delays are widely observed at the time of an earthquake and interpreted with respect to several mechanisms, pore pressure change, stress change, and breakage of rocks. For example, Ikuta and Yamaoka (2004) detected coseismic delay and gradual recovery in about one week using an ACROSS vibrator and interpreted the phenomena, by referring to a strain observation, as pore pressure increase in groundwater caused by the strong shaking of an earthquake. Olivier et al. (2015) detected permanent seismic velocity changes around an underground mine with ambient seismic noise correlations and interpreted these changes as having been caused by a stress change induced by excavation by blasting. Sawazaki and Snieder (2013) reported good a correlation between S-wave delay and maximum dynamic strain by the Tohoku earthquake and attributed it to a breakage of shallow rocks by the strong shaking of the earthquake.
Considering these results, the coseismic change detected in this study can be caused by stress change and/or rock breakage by the Tohoku earthquake. The possibility of pore pressure increase can be excluded because the coseismic step remained for a long time during the observation period. In the case of pore pressure change, short recovery of velocity is expected due to diffusion of pore pressure, as in the case of Ikuta and Yamaoka (2004). In contrast, travel time changes caused by stress change (Olivier et al. 2015) or rock breakage (Hobiger et al. 2012; Sawazaki and Snieder 2013) often remained for very long periods of time.
The secular change detected in this study may have been caused by the healing process of rock and/or by stress buildup due to subduction. Considering that the secular change continued for 10 years with the same tendency, it is difficult to interpret it by a gradual change of pore pressure. However, the healing process or stress change can explain such a continuous 10-year change. The healing process in subsurface material causes stiffness increase that leads to increase in seismic velocity. Precipitation of chemical components on crack surfaces in rock and compaction in shallow layers are examples of possible healing mechanisms. These processes can reduce crack density, resulting in increased rigidity and seismic velocity (O’Connell and Budiansky 1974). This interpretation can also explain the random distribution of the distance dependence. Increase in seismic velocity may differ depending on the original porosity or the damage caused by the last breakage, causing variation of velocity changes from place to place. Stress increase caused by subduction of the Philippine Sea Plate can also close cracks, which causes velocity increase.
The azimuthal dependence of the temporal velocity changes can reflect the anisotropic nature of crack distribution in this region. In case of isotropic distribution, NW–SE compression of strain rate that is observed in this region would induce maximum velocity change in NW–SE, which is inconsistent with our observation. On the other hand, any direction can be maximum in the velocity change for anisotropic medium, according to the preferred orientation of cracks. Hence, our observation may indicate that cracks oriented preferably in NE–SW prevail, though geological evidences supporting the crack orientation are not found yet. The anisotropic distribution of cracks can also explain negative correlation between the secular and the coseismic change if we can assume that the both changes result from closure and opening of the same cracks.
We analyzed the travel time variation of S-waves in the Tokai region, central Japan, for 10 years, from March 29, 2007, to October 31, 2017. We detected travel time variation as a secular advance and a coseismic steplike delay at the time of the 2011 Tohoku earthquake at most stations. The rates of secular changes were 0.0–1.4 ms/year with errors of 0.0–0.9 ms/year, and the coseismic delays were − 4.0 to 0 ms with errors of 0.0–6.4 ms.
The distance dependence can be explained by a combination of common bias and random dispersion for each station. This can be interpreted as spatially random distribution of velocity variation and positive and negative bias of secular and coseismic changes, respectively. The magnitude of velocity changes was larger at stations located in the NE–SW direction than at stations in the NW–SE direction for both the secular and coseismic changes, which suggests healing and breakage as a cause of the velocity change.
ST carried out the analyses and drafted the manuscript. KY supervised ST and developed the theories for model fitting. RI provided the basic idea of secular change and provided a program for transfer function. TK maintained the ACROSS source. TW maintained daily stacking, and YY and AK managed the operation of the ACROSS source. All authors read and approved the final manuscript.
We used continuous waveform data from Hi-net operated by the NIED. We used JMA travel time table and daily precipitation data.
The authors declare that they have no competing interests.
Availability of data and materials
Hi-net data are retrieved from NIED webpage (https://hinetwww11.bosai.go.jp/nied/appli/?LANG=en). JMA 2001 table is available in the JMA Web site (http://www.data.jma.go.jp/svd/eqev/data/bulletin/catalog/appendix/trtime/tjma2001.zip). Daily precipitation data are retrieved from the JMA Web site (http://www.data.jma.go.jp/gmd/risk/obsdl/index.php).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Birch F (1960) The velocity of compressional waves in rocks to 10 kilobars: 1. J Geophys Res 65:1083–1102. https://doi.org/10.1029/JZ065i004p01083 View ArticleGoogle Scholar
- Bonnelye A, Schubnel A, David C, Henry P, Guglielmi Y, Gout C, Fauchille AL, Dick P (2017) Strength anisotropy of shales deformed under uppermost crustal conditions. J Geophys Res Solid Earth 122:110–129. https://doi.org/10.1002/2016JB013040 View ArticleGoogle Scholar
- Brenguier F, Campillo M, Takeda T, Aoki Y, Shapiro NM, Briand X, Emoto K, Miyake H (2014) Mapping pressurized volcanic fluids from induced crustal seismic velocity drops. Science 345(80):80–82. https://doi.org/10.1126/science.1254073 View ArticleGoogle Scholar
- Clarke D, Brenguier F, Froger JL, Shapiro NM, Peltier A, Staudacher T (2013) Timing of a large volcanic flank movement at Piton de la Fournaise Volcano using noise-based seismic monitoring and ground deformation measurements. Geophys J Int 195:1132–1140. https://doi.org/10.1093/gji/ggt276 View ArticleGoogle Scholar
- De Fazio TL, Aki K, Alba J (1973) Solid Earth tide and observed change in the in situ seismic velocity. J Geophys Res 78:1319–1322. https://doi.org/10.1029/JB078i008p01319 View ArticleGoogle Scholar
- Dewhurst DN, Siggins AF (2006) Impact of fabric, microcracks and stress field on shale anisotropy. Geophys J Int 165:135–148. https://doi.org/10.1111/j.1365-246X.2006.02834.x View ArticleGoogle Scholar
- Hadley K (1976) Comparison of calculated and observed crack densities and seismic velocities in westerly granite. J Geophys Res 81:3484–3494. https://doi.org/10.1029/JB081i020p03484 View ArticleGoogle Scholar
- Henry P, Mazzotti S, Le Pichon X (2001) Transient and permanent deformation of central Japan estimated by GPS 1. Interseismic loading and subduction kinematics. Earth Planet Sci Lett 184:443–453. https://doi.org/10.1016/S0012-821X(00)00335-6 View ArticleGoogle Scholar
- Hobiger M, Wegler U, Shiomi K, Nakahara H (2012) Coseismic and postseismic elastic wave velocity variations caused by the 2008 Iwate–Miyagi Nairiku earthquake, Japan. J Geophys Res Solid Earth 117:1–19. https://doi.org/10.1029/2012JB009402 View ArticleGoogle Scholar
- Hobiger M, Wegler U, Shiomi K, Nakahara H (2016) Coseismic and post-seismic velocity changes detected by passive image interferometry: comparison of one great and five strong earthquakes in Japan. Geophys J Int 205:1053–1073. https://doi.org/10.1093/gji/ggw066 View ArticleGoogle Scholar
- Ikuta R, Yamaoka K (2004) Temporal variation in the shear wave anisotropy detected using the Accurately Controlled Routinely Operated Signal System (ACROSS). J Geophys Res B Solid Earth 109:1–15. https://doi.org/10.1029/2003JB002901 View ArticleGoogle Scholar
- Ikuta R, Yamaoka K, Miyakawa K, Takahiro K, Mineo K (2002) Continuous monitoring of propagation velocity of seismic wave using ACROSS. Geophys Res Lett 29:1627. https://doi.org/10.1029/2001GL013974 View ArticleGoogle Scholar
- Kame N, Nagata K, Nakatani M, Kusakabe T (2014) Feasibility of acoustic monitoring of strength drop precursory to earthquake occurrence. Earth, Planets Space 66:1–12. https://doi.org/10.1186/1880-5981-66-41 View ArticleGoogle Scholar
- Kumar KV, Miyashita K, Li J (2002) Secular crustal deformation in central Japan, based on the wavelet analysis of GPS time-series data. Earth, Planets Space 54:133–139. https://doi.org/10.1186/BF03351713 View ArticleGoogle Scholar
- Kumazawa M, Takei Y (1994) Active method of monitoring underground structures by means of Accurately Controlled Rotary Seismic Source (ACROSS). 1. Purpose and principle. Abstruct Seismol Soc Japan p 158Google Scholar
- Kunitomo T (2014) An improvement in the precision of measuring seismic travel time changes with the use of the hi-net data. Zisin (J Seismol Soc Jpn 2nd ser) 66:97–112. https://doi.org/10.4294/zisin.66.97 View ArticleGoogle Scholar
- Leary PC, Malin PE, Phinney RA, Brocher T, Voncolln R (1979) Systematic monitoring of millisecond travel time variations. J Geophys Res 84:659–666. https://doi.org/10.1029/JB084iB02p00659 View ArticleGoogle Scholar
- Minato S, Tsuji T, Ohmi S, Matsuoka T (2012) Monitoring seismic velocity change caused by the 2011 Tohoku-oki earthquake using ambient noise records. Geophys Res Lett 39:1–6. https://doi.org/10.1029/2012GL051405 View ArticleGoogle Scholar
- Nagao H, Nakajima T, Kumazawa M, Kunitomo T (2010) Stacking strategy for acquisition of an ACROSS transfer function. In: Handbook of geophysical exploration: seismic exploration. Pergamon, pp 213–227Google Scholar
- Niu F, Silver PG, Daley TM, Cheng X, Majer EL (2008) Preseismic velocity changes observed from active source monitoring at the Parkfield SAFOD drill site. Nature 454:204–208. https://doi.org/10.1038/nature07111 View ArticleGoogle Scholar
- Nur A (1971) Effects of stress on velocity anisotropy in rocks with cracks. J Geophys Res 76:2022–2034. https://doi.org/10.1029/JB076i008p02022 View ArticleGoogle Scholar
- Nur A, Simmons G (1969) The effect of saturation on velocity in low porosity rocks. Earth Planet Sci Lett 7:183–193. https://doi.org/10.1016/0012-821X(69)90035-1 View ArticleGoogle Scholar
- O’Connell RJ, Budiansky B (1974) Seismic velocities in dry and saturated cracked solids. J Geophys Res 79:5412–5426. https://doi.org/10.1029/JB079i035p05412 View ArticleGoogle Scholar
- Olivier G, Brenguier F, Campillo M, Roux P, Shapiro NM, Lynch R (2015) Investigation of coseismic and postseismic processes using in situ measurements of seismic velocity variations in an underground mine. Geophys Res Lett 42:9261–9269. https://doi.org/10.1002/2015GL065975 View ArticleGoogle Scholar
- Popp T, Kern H (1994) The influence of dry and water saturated cracks on seismic velocities of crustal rocks—a comparison of experimental data with theoretical model. Surv Geophys 15:443–465. https://doi.org/10.1007/BF00690169 View ArticleGoogle Scholar
- Reasenberg PA, Aki K (1974) A precise, continuous measurement of seismic velocity for monitoring in situ stress. J Geophys Res 79:399–406. https://doi.org/10.1029/JB079i002p00399 View ArticleGoogle Scholar
- Sagiya T, Miyazaki S, Tada T (2000) Continuous GPS array and present-day crustal deformation of Japan. Pure appl Geophys 157:2303–2322. https://doi.org/10.1007/PL00022507 View ArticleGoogle Scholar
- Saiga A, Yamaoka K, Kunitomo T, Watanabe T (2006) Continuous observation of seismic wave velocity and apparent velocity using a precise seismic array and ACROSS seismic source. Earth, Planets Space 58:993–1005. https://doi.org/10.1186/BF03352604 View ArticleGoogle Scholar
- Sawazaki K, Snieder R (2013) Time-lapse changes of P- and S-wave velocities and shear wave splitting in the first year after the 2011 Tohoku earthquake, Japan: shallow subsurface. Geophys J Int 193:238–251. https://doi.org/10.1093/gji/ggs080 View ArticleGoogle Scholar
- Sawazaki K, Sato H, Nakahara H, Nishimura T (2009) Time-lapse changes of seismic velocity in the shallow ground caused by strong ground motion shock of the 2000 Western-Tottori earthquake, Japan, as revealed from coda deconvolution analysis. Bull Seismol Soc Am 99:352–366. https://doi.org/10.1785/0120080058 View ArticleGoogle Scholar
- Terakawa T (2014) Evolution of pore fluid pressures in a stimulated geothermal reservoir inferred from earthquake focal mechanisms. Geophys Res Lett 41:7468–7476. https://doi.org/10.1002/2014GL061908 View ArticleGoogle Scholar
- Ueno H, Hatakeyama S, Aketagawa T, Funasaki J, Hamada N (2002) Improvement of hypocenter determination procedures in the japan meteorological agency. Q J Seismol 65:123–134Google Scholar
- Yamamura K, Sano O, Utada H, Takei Y, Nakao S, Fukao Y (2003) Long-term observation of in situ seismic velocity and attenuation. J Geophys Res Solid Earth 108:1–15. https://doi.org/10.1029/2002JB002005 View ArticleGoogle Scholar
- Yamaoka K, Kunitomo T, Miyakawa K, Kobayashi K, Kumazawa M (2001) A trial for monitoring temporal variation of seismic velocity using an ACROSS system. Isl Arc 10:336–347. https://doi.org/10.1111/j.1440-1738.2001.00332.x View ArticleGoogle Scholar
- Yamaoka K, Miyamachi H, Watanabe T, Kunitomo T, Michishita T, Ikuta R, Iguchi M (2014) Active monitoring at an active volcano: amplitude-distance dependence of ACROSS at Sakurajima Volcano, Japan. Earth, Planets Space 66:32. https://doi.org/10.1186/1880-5981-66-32 View ArticleGoogle Scholar