Gaochun Wang, Xiaobo Tian, Lianglei Guo, Jiayong Yan, Qingtian Lyu (2018). High-resolution crustal velocity imaging using ambient noise recordings from a high-density seismic array: An example from the Shangrao section of the Xinjiang basin, China. Earthq Sci 31(5-6): 242-251. DOI: 10.29382/eqs-2018-0242-4
Citation:
Gaochun Wang, Xiaobo Tian, Lianglei Guo, Jiayong Yan, Qingtian Lyu (2018). High-resolution crustal velocity imaging using ambient noise recordings from a high-density seismic array: An example from the Shangrao section of the Xinjiang basin, China. Earthq Sci 31(5-6): 242-251. DOI: 10.29382/eqs-2018-0242-4
Gaochun Wang, Xiaobo Tian, Lianglei Guo, Jiayong Yan, Qingtian Lyu (2018). High-resolution crustal velocity imaging using ambient noise recordings from a high-density seismic array: An example from the Shangrao section of the Xinjiang basin, China. Earthq Sci 31(5-6): 242-251. DOI: 10.29382/eqs-2018-0242-4
Citation:
Gaochun Wang, Xiaobo Tian, Lianglei Guo, Jiayong Yan, Qingtian Lyu (2018). High-resolution crustal velocity imaging using ambient noise recordings from a high-density seismic array: An example from the Shangrao section of the Xinjiang basin, China. Earthq Sci 31(5-6): 242-251. DOI: 10.29382/eqs-2018-0242-4
High-resolution crustal velocity imaging using ambient noise recordings from a high-density seismic array: An example from the Shangrao section of the Xinjiang basin, China
State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
4.
MLR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
5.
China Deep Exploration Center, Chinese Academy of Geological Sciences, Beijing 100037, China
A profile of shallow crustal velocity structure (1–2 km) may greatly enhance interpretation of the sedimentary environment and shallow tectonic deformation. Recent advances in surface wave tomography, using ambient noise data recorded with high-density seismic arrays, have improved the understanding of regional crustal structure. As the interest in detailed shallow crustal structure imaging has increased, dense seismic array methods have become increasingly efficient. This study used a high-density seismic array deployed in the Xinjiang basin in southeastern China, to record seismic data, which was then processed with the ambient noise tomography method. The high-density seismic array contained 203 short-period seismometers, spaced at short intervals (~ 400 m). The array collected continuous records of ambient noise for 32 days. Data preprocessing, cross correlation calculation, and Rayleigh surface wave phase-velocity dispersion curve extraction, yielded more than 16,000 Rayleigh surface wave phase-velocity dispersion curves, which were then analyzed using the direct-inversion method. Checkerboard tests indicate that the shear wave velocity is recovered in the study area, at depths of 0–1.4 km, with a lateral image resolution of ~ 400 m. Model test results show that the seismic array effectively images a 50 m thick slab at a depth of 0–300 m, a 150 m thick anomalous body at a depth of 300–600 m, and a 400 m thick anomalous body at a depth of 0.6–1.4 km. The shear wave velocity profile reveals features very similar to those detected by a deep seismic reflection profile across the study area. This demonstrates that analysis of shallow crustal velocity structure provides high-resolution imaging of crustal features. Thus, ambient noise tomography with a high-density seismic array may play an important role in imaging shallow crustal structure.
A geophone array, deployed in a geometrical arrangement with signals recorded by a single channel, has been used for many years for active seismic detection (Karplus and Schmandt, 2018). However, recent advances in the technology of all-in-one seismic systems, which include one-component or three-component geophone sensors, a digitizer, and a battery system, have made it possible to use them for passive seismic detection (e.g., Schmandt and Clayton, 2013; Li et al., 2016b). With the benefit of portable seismic systems, shallow crustal structure imaging based on passive seismic studies has received increased attention (Pilz et al., 2012; Lin et al., 2013; Shirzad and Shomali, 2014). The convenience and efficiency of a short-period dense seismic array with all-in-one seismic systems makes it possible to arrange and install the detection system in a cost-effective manner, as opposed to the more difficult to use expensive broad-band seismographs. The dense seismic array, which is distinct from the traditional definition of a “geophone array,” has advanced rapidly in the last decade (Mordret et al., 2013a, b; Roux et al., 2016; Li et al ., 2016b).
However, because of the significant group velocity and phase-velocity changes in the near-surface or shallow crust, great-circle propagation is not sufficient. Methods must include frequency-dependent off-great-circle propagation. Fang et al. (2015) described a direct inversion method capable of imaging three-dimensional shallow crustal structure, based on ray tracing. High-frequency surface waves from ambient noise cross correlation have become popular for investigating shallow crustal or near-surface structure in small-scale regions (Fang et al., 2015, Li et al., 2016a, Chen et al., 2016).
In this study, we deployed a dense seismic array with three-component (3C) 2.5-Hz geophones, which can be used in an urban environment and carried in difficult-to-access areas, to investigate shallow crustal velocity structures. The resolution was tested using the ambient noise tomography method. The Qin-Hang belt (QHB) developed as the suture between the ancient Yangtze and Cathaysia plates (Figure 1a). The QHB consists of complex structural and lithological features and hosts abundant mineral resources (Zhang, 1991; Li et al., 1996; Liu, 1996; Zhang, 1997; Zhang et al., 1998; Li, 2009; Jiang et al., 2010). The complex shallow crustal structures and mineral resources of the region form an ideal environment to test the resolution of the dense seismic array and investigate complex shallow crustal structures. This study analyzed the continuous ambient noise collected from 203 three-component digital seismographs, over 32 days from October to December, 2016. The seismographs were spaced at ~ 400 m intervals across the Xinjiang basin (XJB), which is located in the northeast QHB. Ambient noise tomographic processing of high-frequency surface waves (0.2–1.7 s) revealed shallow crustal (0–1.4 km) velocity structure with high-resolution.
Figure
1.
(a) Tectonic background and topography of eastern China. The red rectangular box indicates the study area. Qin-Hang belt (QHB), Yangtze block (YB), Cathaysia block (CB), Xinjiang basin (XJB). (b) Topographic map of the XJB, west of Shangrao city. Blue triangles mark the location of the seismic array and the yellow circle shows the region with a particularly low shear wave velocity (Figure 7). Black dashed lines denote the valleys. Light blue areas represent surface water bodies. Solid lines show the profiles across the study area
This study deployed a dense seismic array, containing 203 seismographs with 3C 2.5-Hz geophones, a built-in digitizer, and a long lasting battery. The majority of the seismographs were deployed at points over 100 m from a road. The seismic array spanned an area of approximately 6 km × 8 km in the XJB, with an average interval of about 400 m (Figure 1b). The portable seismometers included integrating digitizer systems and three geophones with a ~ 2.5 Hz cut-off frequency. Operating at a 200 Hz sampling frequency, the observation system collected continuous ambient-noise data for 32 days, from October to December, 2016.
2.2
Ambient noise data processing
1) Data preprocessing
In this study, ambient noise tomography with vertical component data was used to obtain the shallow crustal shear wave velocity structure. The vertical component data were divided into hourly segments at a 40 Hz sampling rate. Instrumental response was removed along with the mean and the trend of the data. Data were then band-pass filtered in the 0.1–10 s frequency band and subsequently subjected to spectral whitening and temporal one-bit normalization (Bensen and Ritzvoller, 2007).
2) Cross-correlation calculation
The time domain cross-correlation function (CF) CAB(t) [equation (1)] processed hourly data for station pair A and B. Linear stacking of hourly CFs from each station pair gave an adequate signal-to-noise ratio (SNR) (Figure 2).
CAB≈∫τc0VA(τ)VB(t+τ)dτ
(1)
Figure
2.
Interstation cross-correlation functions (CFs) with signal-to-noise ratio (SNR) > 5 in the 0.1–10 s period band, obtained by stacking hourly data from each station pair after cross-correlation. All the CFs were stacked linearly. The map shows 1000 CFs after symmetry
The time domain empirical Green’s function (EGF) represents the time derivative of the CF (Sabra et al., 2005; Yao et al., 2006). For the calculation, interstation distance was set to a distance greater than 1.5 times the relevant seismic wavelength (Luo et al., 2015). The positive- and negative-time EGF components were linearly stacked, following the image analysis method detailed in Yao et al. (2006, 2011). These processes extracted the fundamental Rayleigh-wave group mode and phase-velocity dispersion curves, from each station pair’s EGF. We then used the Rayleigh-wave phase-velocity dispersion curves for direct inversion (Figure 3a).
Figure
3.
(a) Phase-velocity dispersion curves in the 0.2–1.7 s period band. The black solid lines represent dispersion curves. The blue circles indicate the number of phase-velocity curves at different phase-periods. (b) A few typical dispersion curves that show large phase-velocity fluctuation at relatively high frequencies (> 1 Hz)
For the ~16,000 phase-velocity curves extracted from the EGFs, phase-velocity dispersion curve periods varied (Figure 3a) from 0.2 to 1.7 s, with 0.05 s intervals. Due to the relatively short station interval, the quantity of dispersion data gradually decreased with increasing period, especially above 1.5 s. According to the dispersion curves extracted in our study area, the variation of phase-velocity was relatively large. For example, phase-velocity varied from 1.15 to 2.5 km/s, for 0.2 s periods. The blue circles (Figure 3a) show the number of the phase-velocity curves for different phase-periods. These results illustrate that the number of 0.2–1.0 s phase-velocity dispersion curves reached 3 000 or more, while there were more than 10,000 0.2–0.6 s phase-velocity dispersion curves. Figure 3b gives a few typical dispersion curves that show large phase velocity fluctuation at relatively high frequencies (>1 Hz).
2.3
Direct-inversion for 3D vS structure
In this study, direct inversion of surface wave dispersion for three-dimensional shallow crustal structure imaging (Fang et al., 2015) was used. The fundamental Rayleigh-wave phase-velocity mode is sensitive to shear wave velocity at depths of around 1/3 of its corresponding wavelength (Fang et al., 2015). For a uniform half-space Poisson solid, the phase-velocity, c, is equal to 0.92vS (shear wave velocity) (Shearer, 2009). We therefore selected an initial shear wave velocity model approximating the average measured phase-velocity times 1.1 (vS ≈ 1.1c), at depths corresponding to 1/3 of the wavelength (Figure 4) (Fang et al., 2015). All phase-velocity curves were subjected to the direct inversion method described by Fang et al. (2015).
Figure
4.
Transformation of all interstation Rayleigh wave phase-velocity dispersion curves, measured by the ambient noise cross-correlation method, into a depth-shear wave velocity approximation profile. For each Rayleigh wave phase-velocity measurement, we replaced period with 1/3 of the corresponding wavelength (phase-velocity times period) at the vertical axis and replaced phase-velocity with the approximated shear wave velocity (1.1 times phase-velocity) at the horizontal axis. The average wave velocity model from this profile (red line) was used as the initial reference shear wave velocity model in the inversion
The ray-paths obtained for different phase-velocity periods (Figure 5), offered very satisfactory coverage for the 0.02–1.0 s period. However, ray-paths for the 1.05–1.70 s periods were only sufficient in the middle of the study region. The constrained area became smaller with increasing depth, due to decreasing ray-paths. Inversion results thus constrain the shallow crustal structure up to 2 km underground.
Figure
5.
Different ray paths corresponding to different phase-velocity periods
Prior to applying the inversion method, the study region was divided into an aerial 34 by 34 grid points, with intervals of 0.0035° in latitude and longitude. The grid extended an additional 17 grid nodes into the subsurface, with depth spacing of 0.05–0.2 km. Checkerboard tests of different depths demonstrated that the anomalous velocity was adequately recovered at 0.05–1.0 km depth (Figure 6). At depths of 1.0–1.4 km, the anomaly was not well recovered at the boundary of the study area (Figure 6).
Figure
6.
Lateral shear wave velocity slices of the checkerboard test results. The input checkerboard model is shown in (a). The (b-f) slices give the output models at depths of 50 m, 400 m, 800 m, 1000 m, and 1400 m. The grid size is about 1 km × 1 km
As ray-path coverage decreases, the range of the velocity image should decrease with increasing depth (Figure 5). Figure 7 shows the distribution of shear wave velocity in the shallow crust. The black dashed line for each depth slice illustrates the reliable area of the inversion results, according to the checkerboard test results (Figure 6). Variation in shear wave velocity for the study area is thus relatively large (0.8 to 3.0 km/s) for the shallow crust. Velocity differences reach as much as 2.2 km/s. Low shear wave velocity occurs mostly in the northerly region of the study area. The region near 28.381°N, 117.810°E (Figure 1) exhibits a particularly low shear wave velocity (<1.6 km/s), extending to ~ 650 m depth. This region also appears at a distance of about 6 km along the f-f’ profile (Figure 8) and at a distance of about 2.5 km along the A-A’ profile (Figure 9). The A-A’ profile (Figure 9) demonstrates that the edge of the particularly low shear wave velocity region is consistent with a V-shaped deformation border.
Figure
7.
3D shear wave velocity structures obtained from direct inversion. The dark gray solid lines show the valleys. The black dashed lines represent the border of the shear wave velocity areas with reliable values
Figure
9.A-A’ profile for shear wave velocity structure, along the red line shown in Figure 1. The two pictures on the left show the deep seismic reflection profile and the shear wave velocity distribution, from the same profile. The picture on the right illustrates the ideal combination of the two pictures on the left. The black dashed line in the right slice shows a V-shaped edge of the deformation. The time-depth transforming velocity equals the average shear wave velocity (1.85 km/s) times the vP/vS (assumed equal to 1.73)
The deep seismic reflection profile (Figure 1) shown in Figure 9 indicates that the north part of the study area may have had more near-surface deformation than the south. The intense near-surface deformation may cause this low shear wave velocity anomaly. The northern region of the study area also has more surface water than the southern region (Figure 1). In contrast, the southern region consists of Cretaceous red sandstones at the surface, which exhibit a relatively high shear wave velocity > 1.8 km/s. Velocity maps (Figure 7) show that the north-south valleys (Figure 1) may have no relationship to the distribution of shear wave velocity. In contrast, the east-west valleys (Figure 1) trending along 28.371°N, divide the high and low shear wave velocity areas.
Figure 8 shows the shear wave velocity maps beneath the profiles (Figure 1). There are four profiles (a-a’, b-b’, c-c’, d-d’) in the left row and three profiles in the right row (e-e’, f-f’, g-g’). The left four profiles illustrate that southern regions of the study area exhibit a higher velocity than northern regions. The low velocity observed in the middle of the northern region in profile c-c’, may represent fragmented red sandstones. Velocity estimates for shallower areas (< 400 m depth) change rapidly. The considerable velocity variation exhibited by the right three profiles may indicate east-west crustal heterogeneity. There are many small reservoirs above low velocity (< 1.4 km/s) areas, in easterly regions of the profiles e-e’ and f-f’. Another low velocity (< 2.2 km/s) region may be caused by unconsolidated Quaternary sediments and intense deformation.
As shown in profile A-A’ (Figure 9), the distribution of shear wave velocities is very similar to that detected by a recent deep seismic reflection profile (Lyu et al. submitted for publication) of the same area. The results reveal an area of low shear wave velocity, which is subject to extreme deformation (Figure 9). Areas exhibiting high shear wave velocity show little deformation.
Therefore, we suggest that the distribution of shear wave velocity in the study area may be correlated with the amount of rupture and deformation in the near-surface crust.
3.2
Resolution and model testing
Checkerboard and model tests (Figures 6, 10 and 11) were used to evaluate the spatial resolution of the inversion at different depths. These were performed with different grid sizes (Figures 6 and 10) and for different anomalies, at different depths (Figure 11).
Figure
10.
Checkerboard resolution tests of the inversion results: (a) The input checkerboard model and (b-f) the recovered checkerboard models at depths of 100 m, 400 m, 700 m, 1000 m, and 1400 m. The grid size is about 400 m × 400 m
Figure
11.
Model test results for the E-W profile shown in Figure 1. The left slices show the input models and the right slices show the output models. From top to bottom, the input models have thicknesses of 50 m and 150 m
For checkerboard tests, input anomalies (Figures 6a and 10a) follow a sine function, with anomalous values varying from –15% to 15% of the average velocity at different depths. The grid sizes, in orthometric directions, were about 1 km for Figure 6 and 0.40 km for Figure 10. The slices in Figures 6 and 10 show the output results for different depths. The checkerboard test results illustrate that the shear wave velocity is recovered well in the study area at 0–1.4 km depth. These results reveal a lateral resolution of ~ 400 m in the study area. The resolution is much better at depths of <1.0 km than at depths of >1.0 km (Figures 5, 6 and 10).
The model tests were performed on anomalies set to 20% of the value of the average velocity. Results indicate that the seismic array recovers a thin slab with a thickness of 50 m at a depth of 0–300 m, an anomalous body with a thickness of 150 m at a depth of 300–600 m, and an anomalous body with a thickness of 400 m at a depth of 0.6–1.4 km.
4.
Conclusions
This experiment sought to detect the velocity structure of the shallow crust, using ambient noise tomography and a high-density seismic array. The experiment used 203 three-component PDS short-period seismometers, deployed in a 6 km × 8 km area of the XJB and spaced at average intervals of ~ 400 m. Results showed a lateral resolution of ~ 400 m. The seismic array detected a thin slab with a thickness of 50 m at a depth of 0–300 m, an anomalous body with a thickness of 150 m at a depth of 300–600 m, and an anomalous body with a thickness of 400 m at a depth of 0.6–1.4 km. At the same time, the method reliably recovered shallow crustal shear wave velocity structure at depths of 0–1.4 km in the study area.
In conclusion, ambient noise tomography by short-period, high-density seismic arrays images shallow crustal shear wave velocity structure at high-resolution. In the future, this approach will play an important role in detecting and interpreting shallow crustal structure.
Acknowledgments
The authors are grateful to Qifu Chen, Lianfeng Zhao, Jinhai Zhang, and Zhen Liu for helpful discussions that greatly improved the manuscript. We acknowledge Hongjian Fang for providing software resources for the direct-inversion method. This research was supported by the China Geological Survey Project “Deep Geological Survey of the Qin-Hang Belt” (No. DD20160082) and the National Natural Science Foundation of China (No. 41574048). Seismic instruments were provided by the Short-period Seismograph Observation Laboratory, IGGCAS. Most figures were plotted using the Generic Mapping Tools software (Wessel and Smith, 1998).
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