Investigation of scattered coda correlation functions from noise correlation functions, in retrieving optimized empirical Green’s functionsin Azerbaijan Region, Iran

Authors

1 M.Sc., Department of Earth Physics, Institute of Geophysics, University of Tehran, Iran

2 Assistant Professor, Department of Physics, Islamic Azad university Damavand branch, Damavand, Iran

Abstract

There has been wide interest in ambient seismic noise studies for determining earth’ internal structures in the recent years. Ambient seismic noise contains waves with random amplitudes and phases which propagate in all directions (Van-Tighelen, 2003; Gorin et al., 2006). Therefore determining information of waves propagations is possible by extracting coherence signal. This information of propagation path is equal to Green’s function (Shapiro et al.,2005; Roux et al., 2005; Sabra et al., 2005). Ambient seismic noise method is applied in various researches such as acoustic, helioseismology, seismology, etc (Duvall et al., 1993;Rickett and Claerbout, 1999; Malcolm et al., 2004; Roux et al., 2004).
The isotropic and random noise source distribution is the basic assumption underlying retrieving empirical Green’s functions (hereafter EGFs) using this method (Weaver and Lobkis, 2001; Gouédard et al., 2008). Recent studies surrounding noise sources demonstrate the dominant presence of noise sources in oceanic regions (Stutzmann et al., 2009; Landes et al., 2010). Ambient seismic noise spectra contains two broad spectral peaks, one at the period of 17 s (the primary microseism), and the other at the period of 7 s (the secondary microseism) (e.g., Gutenberg, 1936; Berger et al., 2004).
Regarding the dominant presence of noise sources in oceanic regions and also sharp seasonal variations, noise sources distribution is non isotropic and directive (Stehly et al., 2008). Nevertheless, distribution of noise sources homogenizes when considered over long times (Snieder, 2004).
The randomization of the wavefield is enhanced by the scattering of the seismic waves on the small scale heterogeneity within the Earth (Shapiro and Campillo, 2004). Scattered coda waves, sampled randomly and repeatedly parts of wave propagations, similar to ambient seismic noise (Yao et al., 2006). Therefore scattered coda waves, contain valuable information about propagation properties of the media. Additionally these waves are also independent from distribution of noise sources (Stehly et al., 2008; Froment et al., 2011). Scattered coda waves energy flux, is equiparitioning of ambient seismic noise and are independence from distribution of noise sources (Shapiro et al., 2000; Margerin et al., 2009). Stehly et al. (2008) studies, illustrate that retrieving EGFs is possible from scattered coda waves part of noise correlation functions (hereafter NCFs), which was assigned as C3 method in brief. The C3 method is an efficient way, facing poorly oriented station pairs with directional energy flux of ambient seismic noise. Therefore the accuracy of estimating arrival times of the different parts of EGFs is improved by C3 method in the presence of inhomogeneous noise source distribution (Garnier and Papanicolaou, 2009; Froment at al.,2011).
The purpose of this study is retrieving EGFs by C3 method in the period bands of 1-3 and 3-10 s in Azerbaijan region. We processed vertical component recording of continuous data from 7 stations which are equipped with short period sensor (Kinemetrics SS-1) in Azerbaijan region (Figure 1). We use 1 year (Dec. 2011-Dec. 2012) of recording at these stations which are operated by the Iranian Seismological Center (IRSC) of the University of Tehran. NCFs were determined by preparation of raw data (i.e. removing the mean and trend, decimation, segmenting, time and frequency domain normalization). Rms-stacking method (see Shirzad and Shomali, 2013) was applied for all NCFs calculated for retrieving daily and total EGFs from ambient seismic noise method (C1). In this study, we investigate three types of NCFs including: (a) a coda wave signal window selected from NCFs which was calculated from raw data (b) a coda wave window identified from the subset of NCFs, which contributed to the rms-stacking method (c) a coda wave signal window selected from the subset of NCFs, which was subsequently used in daily EGFs from C1 method, in retrieving optimized EGFs by C3 method. We compared two parameters (including correlation coefficients and arrival time of Rayleigh waves fundamental mode) between extracted EGFs from C1 and C3 methods. Table 2 shows the results of this investigation. Analysis of this table shows that the standard deviation of the arrival time Rayleigh waves and correlation coefficients are 0.21, 0.98 in positive lag-time and 0.35, 0.96 in negative lag-time respectively. The results showed that all extracted EGFs using three types of coda wave signal windows were significantly similar in character. However, to save time and reduce the amount of calculations, we selected the first case i.e. using NCFs which was calculated from raw data for further processing (see table 1). In the similar way with C1 method, coda wave windows were stacked with rms-stacking method in monthly and yearly time intervals. Figure 8 shows, the monthly EGFs retrieved by C3 method which illustrate negligible (no) directionality in the region of study. Yearly (total) EGFs versus interstation distances in the period bands of 1-3 and 3-10 s, were depicted in Figure 9. Arrival time of Rayleigh waves fundamental mode is equal (to 2.09±0.04 (km/s) in the region of study.

Keywords

Main Subjects

References

صفرخانی، م. ، 1394، برآورد توابع گرین بهینه و بررسی جهت‌یافتگی آن‌ها در گستره آذربایجان، ایران با استفاده از نوفه‌های لرزه‌ای محیطی، پایان‌نامه کارشناسی ارشد در رشته زلزله‌شناسی، موسسه ژئوفیزیک دانشگاه تهران، 81-71.
Aki, K. and Richards, P. G., 1980, Quantitative seismology: theory and methods, W. H. Freeman, San Francisco.
Bensen, G. D., Ritzwoller, M. H., Barmin, M. P., Levshin, A. L., Lin, F., Moschetti, M. P., Shapiro, N. M. and Yang, Y., 2007, Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements, Geophys. J. Int., 169, 1239–1260.
Berger, J., Davis, P. and Ekström, G., 2004, Ambient Earth noise: A survey of the Global Seismographic Network, J. Geophys. Res., 109, B11307, doi:10.1029/2004JB003408.
Boué, P., Poli, P., Compillo, M. and Roux, P., 2014, Reverberations, coda wave sand ambient noise: Correlations at the global scale and retrieval of the deep phases, Phys. Earth Planet. In., 391, 137–145.
Campillo, M. and Paul, A., 2003, Long-range correlations in the diffuse seismic coda, Science, 299(5606), 547–549.
Cho, K. H., Herrmann, R. B., Ammon, C. J. and Lee, K., 2007, Imaging the upper crust of the Korean peninsula by surface-wave tomography, Bull. Seismol. Soc. Am., 97, 198-207.
Duvall, T. L., Jefferies, S. M., Harvey, J. W. and Pomerantz, M. A., 1993, Time distance helioseismology, Nature, 362, 430–432.
Froment, B., Campillo, M. and Roux, P., 2011, Reconstructing the Green’s function through iteration of correlations C. R. Geoscience this issue; DOI:10.1016/j.crte.2011.03.001.
Garnier, J., Papanicolaou, G., 2009, Passive sensor imaging using cross-correlations of noisy signals in a scattering medium, SIAM Journal on Imaging Sciences, 2(2), 396-437.
Gorin, T., Seligman, T. H. and Wear, R. L., 2006, Scattering fidelity in elastodynamics, Physical Rev. E., 73, 015202.
Gouédard, P., Stehly, L., Brenguier, F., Campillo, M., Colin de Verdière, Y., Larose, E., Margerin, L., Roux, P., S´anchez-Sesma, F. J., Shapiro, N. M. and Weaver, R. L., 2008, Cross-correlation of random fields: mathematical approach and applications, Geophys. Prospect., 56(3), 375–393.
Gutenberg, B., 1936, On microseisms, Bull. Seism. Soc. Am., 26, 111-117.
Hasselmann, K., 1963, A statistical analysis of the generation of microseisms, Rev. Geophys., 1(2):177–210.
Landes, M., Hubans, F., Shapiro, N. M., Paul, A. and Campillo, M., 2010, Origin of deep ocean microseisms by using teleseismic body waves, J. Geophys. Res., 115, B05302, doi:10.1029/2009JB006918.
Lobkis, O. I. and Weaver, R. L., 2001, On the emergence of the Green’s function in the correlations of a diffuse field, J. Acoust. Soc. Am., 110, 3011–3017.
Longuet-Higgins, M. S., 1950, A Theory of the Origin of Microseisms, Philos. Trans. R. Soc. Lond., 243, 1–35.
Malcolm, A. E., Scales, J. and van Tiggelen, B. A., 2004, Extracting the Green function from diffuse, equipartitioned waves, Phys. Rev. E, 70, 015601.
Margerin, L., Campillo, M., van Tiggelen, B. A. and Hennino, R., 2009, Energy partition of seismic coda waves in layered media: theory and application to Pinyon Flats Observatory, Geophys. J. Int., 177, 571–585.
Mirzaei, N., Gao, M. and Chen, Y. T., 1998, Seismic source Regionalization for seismiczoning of Iran: Major seismotectonicprovinces, Journal of Earthquake PredictionRes., 7, 465-495.
Obermann, A., Planès, T., Larose, E. and Campillo, M., 2013, Imaging presumptive and corruptive structural and mechanical changes of a volcano with ambient seismic noise, J. Geophys. Res. Solid Earth.,118, 6285–6294.
Peterson, J., 1993, Observations and modeling of seismic background noise. Open File Report 93-322, U.S. Geological Survey.
Picozzi, M., Parolai, S., Bindi, D. and Strollo, A., 2009, Characterization of shallow geology by high-frequency seismic noise tomography: Geophys. J. Int., 176, 164-174, doi: 10.1111/j.1365-246X.2008.03966.x.
Poli, P., Campillo, M., Pedersen, H. and the Lapnet Working Group, 2012, Body wave imaging of the Earth’s mantle discontinuities from ambient seismic noise, Science,338, 1063–1065.
Rickett, J. and Claerbout, J., 1999, Acoustic daylight imaging via spectral factorization: Helioseismology and reservoir monitoring, Leading Edge, 18, 957–960.
Ritzwoller, M. H., Lin, F. C. and Shen, W., 2011, Ambient noise tomography with a large seismic array, Comptes Rendus Geoscience, 343(8), 558–570.
Roux, P., Kuperman, W. A. and the NPAL Group, 2004,Extracting coherent wavefronts from acoustic ambient noise in the ocean, J. Acoust. Soc. Am., 116, 1995–2003.
Roux, P., Sabra, K. G., Kuperman, W. A. and Roux, A., 2005, Ambient noise cross correlation in free space: theoretical approach, J. Acoust. Soc. Am., 117 (1), 79–84.
Sabra, K. G., Gerstoft, P., Roux, P., Kuperman, W. A. and Fehler, M. C., 2005, Extracting time-domain Green's function estimates from ambient seismic noise, Geophys. Res. Lett., 32, L03310, doi:10.1029/2004GL021862.
Shapiro, N. M. and Campillo, M., 2004, Emergence of broadband Rayleigh wavesfrom correlations of the ambient seismicnoise, Geophys. Res. Lett., 31, L07614, doi:10.1029/2004GL019491.
Shapiro, N. M., Campillo, M., Margerin, L., Singh, S. K., Kostoglodov, V. and Pacheco, J., 2000, The energy partitioning and the diffusive character of the seismic coda, Bull. Seismol. Soc. Am., 90, 655–665.
Shapiro, N. M., Campillo, M., Stehly, L. and Ritzwoller, M. H., 2005, High-resolutionsurface-wave tomography from ambientseismic noise, Science, 307, 1615–1618.
Shirzad, T. and Shomali, Z. H., 2013, Shallow crustal structures of the Tehran basin in Iran resolved by ambient noise tomography, Geophys. J. Int., 196, 1162–1176.
Shirzad, T. and Shomali, Z. H., 2015, Extracting seismic body and Rayleigh waves from the ambient seismic noise using the rms-Stacking Method, Seismol. Res. Lett., 86(1), 173-180.
Shirzad, T. and Shomali, Z. H., 2016, Short note Extracting stable seismic core phases from ambient seismic noise, Bull. Seismol. Soc. Am., 106(1), doi:10.1785/0120150031.
Shirzad, T., Shomali, Z. H. and Riahi, M. A., 2013, An application of ambient noise and earthquake tomography in the Rigan area, southeast of Iran, Seismol. Res. Lett., 84, no. 6, 1014–1020.
Shomali, Z. H. and Shirzad, T., 2015, Crustal structure of Damavand volcano, Iran, from ambient noise and earthquake tomography, J. Seismol., 19(1), 191-200.
Snieder, R., 2004, Extracting the Green’s function from the correlation of coda waves: A derivation based on stationary phase, Phys. Rev. E, 69, 046610, doi:10.1103/PhysRevE.69.046610.
Stehly, L., Campillo, M. and Shapiro, N. M., 2006, A study of the seismic noise from its long- range correlation properties, J. Geophys, Res., 111,B10306, doi:10.1029/2005JB004237.
Stehly, L., Campillo, M., Froment, B. and Weaver, R. L., 2008, Reconstructing Green’s function by correlation of the coda of thecorrelation (C3) of ambient seismic noise, J. Geophys. Res., 113, B11306, doi: 10.1029/2008JB005693.
Stutzmann, E., Schimmel, M., Patau, G. and Maggi, A., 2009, Global climate imprint on seismic noise, Geochem. Geophys. Geosyst.,10, Q11004, doi:10.1029/2009GC002619.
Van Tighelen, B. A., 2003, Green function retrieval and time-reversal in a disordered world, Physical Rev. Lett., 91, 243904.
Wapenaar, C. P. A., 2004, Retrieving theelastodynamic Green’s function of an arbitraryinhomogeneous medium by cross correlation, Phys. Rev. Lett., 95, 254-301.
Weaver, R., Froment, B. and Campillo, M., 2009,On the correlation of non-isotropically distributed ballistic scalar diffuse waves,J. Acoust. Soc. Am., 126(4), 1817-1826.
Weaver, R. L. and Lobkis, O. I., 2001, Ultrasonics without a source: thermal fluctuation correlations at MHz frequencies, Phys. Rev. Lett., 87, 134-301, doi:10.1103/PhysRevLett.87.134301.
Wessel, P. and Smith, W. H. F.,1998, New, improved version of the Generic Mapping Tools released, Eos Trans. AGU 79, 579.
Yao, H., van der Hilst, R. D. and Van de Hoop, M., 2006, Surface-wave array tomography in SE Tibet from ambient seismic noise and two-station analysis—I. Phase velocity maps, Geophys. J. Int., 166, 732–744.
Journal of the Earth and Space Physics
Volume 43, Issue 2
July 2017
Pages 323-337

Files

Share

How to cite

Statistics