1 کارشناسی ارشد،گروه فیزیک زمین، مؤسسۀ ژئوفیزیک، دانشگاه تهران، ایران
2 استادیار، گروه فیزیک، دانشگاه آزاد اسلامی واحد دماوند، دماوند، ایران
عنوان مقاله [English]
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.