Surface-wave tomography from ambient seismic noise of accelerograph networks in southern Korea

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 33,, doi: /2006gl027044, 2006 Surface-wave tomography from ambient seismic noise of accelerograph networks in southern Korea Tae-Seob Kang 1 and Jin Soo Shin 1 Received 29 May 2006; revised 14 July 2006; accepted 26 July 2006; published 6 September [1] An efficient and robust cross-correlation technique for estimation of surface wave Green s function between two locations is applied to a set of ambient noise data recorded by accelerograph networks in the southern Korea. We have estimated 1843 group velocities of Rayleigh waves in the period band of 2 4 seconds from correlation pairs of 91 accelerograph stations. Relatively dense and uniform coverage of the pairs provides us with high-resolution tomographic image of surface-wave group velocities in the region. The resulting group-velocity distribution correlates well with the characteristic features of geological map of the region, displaying low velocities for the sedimentary basins and regions of volcanic and metasedimentary rocks, and high velocities for the regions where granitoid basement rocks outcrop. The short-period information from the accelerograph network can enhance resolution of the subsurface structures at depths within a few kilometers, which may be predominantly responsible for simulation results of strong ground motions. Citation: Kang, T.-S., and J. S. Shin (2006), Surface-wave tomography from ambient seismic noise of accelerograph networks in southern Korea, Geophys. Res. Lett., 33,, doi: /2006gl Introduction [2] It has been shown both theoretically and experimentally that a random wavefield has correlations to a Green s function representing characteristics of media [Lobkis and Weaver, 2001; Weaver and Lobkis, 2001]. The Green s function extracted from the cross-correlation of the wavefield at two locations depends on constructive interference of multiply scattered waves that propagate along the path linking between observation points [Snieder, 2004]. Both observational experiment and numerical simulation by Derode et al. [2003] and Larose et al. [2005] proved the role of scatterers in the extraction of the Green s function from the random wavefield. The random inhomogeneity of the Earth s crust is observed on a broad range of scales [Sato and Fehler, 1998]. Waves through the crust undergo multiple scattering with distribution of their energy into the medium. On the other hand, Wapenaar [2004] showed that the assumption of multiple uncorrelated sources as well as random scatterers holds for derivation of the crosscorrelation method to retrieve the Green s function. [3] A random wavefield generated by scatterers in the near-surface structure of the Earth may inherently include the information on Green s function of a wave traveling 1 Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea. Copyright 2006 by the American Geophysical Union /06/2006GL between two locations. Effects of the multiple scattering are revealed in seismograms through ambient noise as well as coda waves of transient signals [Aki, 1957; Aki and Chouet, 1975]. Aki [1957] applied the spatial autocorrelation (SPAC) method to ambient noise to evaluate phase velocities of the predominant surface waves. Recent studies also showed that both the background noise and coda waves in seismograms contains significant surface-wave energy [Campillo and Paul, 2003; Shapiro and Campillo, 2004; Paul et al., 2005; Sabra et al., 2005a]. They extracted the Rayleigh wave Green s functions between pairs of seismograph stations from cross correlation of long noise sequences. There is a close relationship between the SPAC method and the temporal cross-correlation method [Chávez-García and Luzón, 2005; Sánchez-Sesma and Campillo, 2006]. Shapiro et al. [2005] and Sabra et al. [2005b] estimated surface wave velocity structure through tomography analysis using the arrival times measured from the cross-correlation results of broadband stations in California, United States. [4] Korea is located in the eastern margin of the Eurasian plate and known as a region with moderate seismicity. Since most of the seismic stations equipped with modern seismographs were set up in 1990s, only a few moderate-sized earthquakes have been recorded for a limited period [e.g., Kang and Baag, 2004a]. Therefore, it is not easy to obtain reliable surface wave dispersion data from the network and to construct a three-dimensional velocity structure. In order to estimate the surface wave velocity structure, we apply the cross-correlation technique to the ambient noise data recorded by the accelerograph network in the southern Korean Peninsula. Ray paths by relatively dense distribution of the accelerograph stations of the network including stations located in several islands surrounding the peninsula covers the whole inland of the region (Figure 1). We perform cross correlations of the data between stations in pairs, measure arrival times of the Rayleigh waves extracted from the correlations, and then invert them to obtain a surface-wave velocity distribution image for the shallow crustal structure of the region. Finally, we compare the tomographic results with the major geological units of the southern Korean Peninsula. 2. Data Processing [5] Vertical component seismograms recorded continuously at the rate of 20 samples per second (sps) during one month in August of the year 2005 are collected from 91 accelerograph stations in the southern Korean Peninsula (Figure 1). Most of the accelerograph stations are equipped with the Kinemetrics EpiSensor and operated by Korea Meteorological Administration (KMA), Korea Institute of 1of5

2 KANG AND SHIN: SURFACE WAVE TOMOGRAPHY OF KOREA digitize the data over a one bit leaving amplitude values at sampled points as either 1 or 1 [Derode et al., 1999]. Thus amplitude effects of the process are totally discarded, and only phases of them are to be correlated. Considering computational efficiency in the correlation process, we partition the continuous data with the length of one month into 31 segments with a bin of one day. Data corresponding to one day are cross-correlated at a time for each station pair, and the results from 31 days of the data are stacked together. [7] From the correlation results, we reject the traces with signal-to-noise ratio below 17 db and also having stationto-station distance shorter than the two wavelengths. The signal-to-noise ratio is defined by the ratio of the maximum amplitude of enveloped trace to the standard deviation of the entire trace. The number of resulting correlated seismograms is 1843 in total. Figure 1 shows ray paths with interstation distances from 19 km to 564 km and some of the correlated seismic traces arranged along station-to-station distances. We can see that wave packets in the positive and/ or negative time lag are propagating with an average group velocity of 2.96 km/s. In order to measure arrival times of the wave packets, we take the lag time of the signal corresponding to the maximum amplitude of envelope in each correlated seismogram. [8] Although we use the vertical component only to retrieve the Rayleigh wave Green s functions in all possible paths in this study, it is also possible to obtain all component pairs to be correlated using the three component records (Figure 2). In agreement with Campillo and Paul [2003], Figure 1. Rayleigh wave Green s functions between accelerograph stations in pairs. (A) Locations of the 91 accelerograph stations of the seismic networks in the southern Korea (solid triangles) and ray paths where Rayleigh wave group velocities are measured by crosscorrelating ambient seismic noise (straight lines). The networks are operated by Korea Meteorological Administration (KMA), Korea Institute of Geoscience and Mineral Resources (KIGAM), and Universities. The curved line connecting the Yellow Sea and the East Sea in the northern area indicates Demilitarized zone between North Korea and South Korea. The paths are constructed from the 1843 station pairs having correlated seismograms with a signalto-noise ratio above 17 db. The propagation paths are assumed to be along the great-circle path connecting two stations. (B) Traveltime-distance plot of cross-correlated traces. Each trace is normalized to have the same maximum amplitude. The group velocities are measured from the station-to-station distances divided by the arrival time of the maximum amplitude in the envelope of each trace. Geoscience and Mineral Resources (KIGAM), and Universities. The data set includes 4095 (= 91 90/2) station pairs to be correlated. [6] For each station pair, the cross correlation is performed for the period band of 2 4 seconds of the time series decimated to 1 sps. In order to avoid any influence of large events in the correlation of noise sequences, we re- Figure 2. Cross-correlation functions for all component pairs computed from the rotated three component records at stations CHJ and ULS with the inter-station distance of 190 km. The inter-station great circle path is the radial direction. Labels on each trace indicate vertical (Z), radial (R), and tangential (T) components at the station CHJ versus those at the station ULS. All of traces are normalized by the maximum amplitude of the function Z/Z. 2 of 5

3 KANG AND SHIN: SURFACE WAVE TOMOGRAPHY OF KOREA Figure 3. Result of checkerboard test with cell hit count. (A) Map showing the number of rays passing through each cell designed for inversion. A square cell has a size of 12 km in length. The hit count in each cell is indicated by the color of the circle. (B) Result of checkerboard test using a square inversion cell with a size of 12 km in length. The prescribed checkerboard model is composed of square cells of 80 km in length having differential slowness alternation of ±0.03 s/km. the large correlation between the vertical and radial parts indicates that Rayleigh wave propagation is dominant in the noise cross-correlations. The Love wave signal on the tangential part can also be weakly visible. 3. Rayleigh Wave Tomography [9] The arrival-time measurements are tomographically inverted to create Rayleigh-wave group-velocity map in the southern Korean Peninsula using the LSQR algorithm of Paige and Saunders [1982a, 1982b]. A series of tests using both real and synthetic data is performed to select the optimum cell size and damping constant for the tomography [Calvert et al., 2000]. A checkerboard model having cell size 80 km 80 km with differential slowness alternation of ±0.03 s/km is used to generate the synthetic arrival-time residuals. By varying the size of square cell for inversion from 8 km to 30 km with zero spatial damping, we check the number of cell hits and residuals from model fit in the inversion. We find that a cell size of 12 km is potentially suitable for our data set balancing the hit count and residual. For the fixed cell size of 12 km, we examine the effect of damping constants varying from 0 to 200 with an increasing step of 10. Considering both the fit to the real data and the small-scale features appearing in the inverted model, we find a proper damping constant to be 90 providing about 50% fit of the data. [10] The result of a checkerboard test with the hit count map shown in Figure 3 confirms that we have adequate ray density throughout most area of the southern Korean Peninsula except for the eastern border adjacent to the East Sea. The resolution is considered to be good for the area where the velocity alternation image is properly reconstructed, indicating that the quality of our data set is good enough for obtaining a group velocity distribution image with high resolution in cell size as small as ten-odd kilometers for the southern Korean Peninsula. [11] Figure 4 presents a tomographic image obtained using the observed arrival-time data at period band of 2 4 seconds (Figure 1). For the purpose of comparison between the spatial velocity distribution and lithologic features, a geological map is aligned with the velocity map overlying the topography. The group velocity map correlates well with surface geology. [12] A wide distribution of low surface-wave group velocities in the southeastern part of the study area corresponds to the Gyeongsang and Yeonil basins. The Gyeongsang basin is the largest Cretaceous sedimentary basin in Korea, which developed during a period of sinistral shearing tectonics in the eastern margin of the Asian continent caused by the subduction of an ancient oceanic plate [Chun and Chough, 1992; Chough et al., 2000; Jeon and Sohn, 2003]. The basin is filled with non-marine deposits and volcanics [Chough et al., 2000], constitutes of low velocity layers with thickness of 10 km, and is characterized by the lowest velocity material from the surface to a depth of 5 km [Cho et al., 2006]. Thus the striking similarity between the distribution of low group velocities and the extent of the Gyeongsang basin explains high fidelity of our tomographic results. Low velocities are also associated with the Quaternary Jeju volcanic terrain and a region consisting mainly of Proterozoic and Archean metamorphic sedimentary rocks in the western coastal area of the Gyeonggi massif (127 E, 37 N). On the other hand, a series of sinistral strike-slip movements in the late Jurassic to the Cretaceous has formed some small-scale rhomboidal inland basins trending northeast-southwest in the southwestern Korean Peninsula [Chough et al., 2000]. These elongated small-scale basins are well resolved in the group velocity map, being characterized by low velocities. [13] High velocities generally characterize regions being composed of granitic basement rocks in the Gyeonggi massif, the Yeongnam massif, and the Okcheon fold belt. The elongated pattern of high group velocities in Figure 4b 3of5

4 KANG AND SHIN: SURFACE WAVE TOMOGRAPHY OF KOREA Figure 4. Group velocity distribution in comparison with geological features in the southern Korean Peninsula. (A) Map showing geology and tectonic provinces of the study area, which is modified from the geological and tectonic maps of Korea in a scale of 1:1,000,000 [Chwae et al., 1995; Hwang et al., 2001]. The geology is classified to three types and each of them is given in chronological order in legend. Black solid lines show boundaries between tectonic provinces (PB: Pyeongnam Basin, OB: Ongjin Basin, IB: Imjingang Basin, GM: Gyeonggi Massif, OFB: Okcheon Fold Belt, YM: Yeongnam Massif, GB: Gyeongsang Basin, YB: Yeonil Basin, JVT: Jeju Volcanic Terrain). (B) Rayleigh wave group velocity map in period band of 2 4 seconds. The map is constructed by tomographic inversion of group velocities which are obtained by cross correlations of ambient seismic noise recorded during one month in August of the year 2005 at stations of accelerograph networks in the southern Korean Peninsula. is approximately laid in the NNE SSW direction reflecting the regional tectonic environment of the Mesozoic in the Korean Peninsula. The directionality of the subsurface velocity structures may have influence on efficiency of seismic energy propagation. For example, intensity distribution of the 1996, ML 4.5, Yeongwol earthquake occurred in the northeastern part of the study area shows that the trend of high intensity distribution is predominantly in the south-southwestern (SSW) direction [Jo et al., 1997] in agreement with the distribution of high group velocities in Figure 4b. This illustrates that horizontal variations of velocities in the crust within a few kilometers below the free surface could be one of the main factors determining effects of earthquake ground motions. Thus the Mesozoic granitoid rocks with high group velocities may serve as passages with relatively low attenuation for seismic energy propagation in the southern Korean Peninsula. 4. Conclusions [14] In tectonic regions with low to moderate seismicity such as Korea, it is not easy to obtain sufficient information on the subsurface structure from natural earthquakes. The technique using the ambient noise field that exists always in both space and time allows us to have an alternative approach in those environments. Thus we cross-correlated sequences of ambient seismic noise from accelerograph data set of the seismic networks in the southern Korea, and then estimated group velocities of Rayleigh waves traveling great-circle paths linking stations. A relatively dense network of permanent accelerograph stations provides us with uniform coverage across the southern Korean Peninsula. Therefore, the estimates along the paths were used to obtain high-resolution tomographic image of surface-wave group velocities for the southern Korean Peninsula. The resulting group-velocity distribution is fairly in accordance with characteristic features of the geological map of the region, displaying low velocities for the sedimentary basins and regions of volcanic and metasedimentary rocks, and high velocities for the regions where granitoid basement rocks outcrop. Surface-wave group velocities are related to the local velocity structure of body waves, and are mainly sensitive to shear-wave velocities. Thus dispersion analysis of Rayleigh wave Green s functions estimated from cross correlations in this study can provide independent constraints to construct the three-dimensional crustal structure of the southern Korean Peninsula, combining receiver function studies [e.g., Chang and Baag, 2005] with regional refraction experiments [e.g., Cho et al., 2006]. Particularly the short-period information from the accelerograph network can enhance resolution of the subsurface structures within depths of a few kilometers, which may be predominantly responsible for simulation results of strong ground motions [Kang and Baag, 2004b]. [15] Acknowledgments. We are grateful to Y. Ryoo for providing the KMA data and C.-E. Baag for the careful reading of the manuscript. Thanks are given to K. Wapenaar and an anonymous reviewer for the enlightening comments. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER References Aki, K. (1957), Space and time spectra of stationary stochastic waves with special reference to microtremors, Bull. Earthquake Res. Inst., 35, of 5

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