Near-surface velocity structure study using surface waves and first breaks in the middle segment of the Bangong-Nujiang suture zone, Tibetan Plateau

Author(s):  
Hui Zhang ◽  
Rizheng He ◽  
Zhiwei Liu

<p>The Bangong-Nujiang suture zone, located in the central Tibet, is one of several important geological boundaries in Qinghai-Tibet plateau. Abundant researches have been performed and most of them focused on deep tectonic structure and its dynamic mechanism through recent geophysical projects such as INDEPTH-III, Hi-CLIMB, ANTILOPE, SinoProbe, etc. (Zhao Wenjin et al., 2008; N´abelek et al. 2009; Gao Rui, et al., 2013;Zhao Junmeng et al. 2014; He Rizheng et al., 2014; Xu Qiang et al., 2017; Shang Xuefeng et al., 2017; Davlatkhudzha et al.,2018). Near-surface velocity study can not only obtain the physical parameters such as Vp and Vs in the area, but also improve seismic image quality of deep structure (Zhao Lingzhi et al., 2018). However, the velocity information obtained from passive seismic stations using either receiver function or ambient noise tomography is not enough to elaborate the near surface velocity structure of the Bangong-Nujiang suture zone. Besides, the active-source seismic reflection data usually doesn’t have sufficient offset density at near surface which poses a challenge to conventional near-surface velocity analysis methods.</p><p>This study makes full use of surface waves and first breaks to obtain near-surface P- and S-wave velocities based on a 2D deep seismic reflection survey data which was acquired by SinoProbe project in 2009 . We adopt the method of superposition of surface waves in common receiver domain to generate high quality F-K spectrum which enables us to obtain fundamental-order and high-order dispersion curves. First, a 2D layered model with an irregular topography was built and the 2D elastic finite difference modeling was executed to generate 161 synthetic seismic shot gathers which mimicking the actual acquisition geometry. These gathers contain surface waves, refractions, reflections and multiples energy, and the maximum offset is about 18 km. It is shown that the F-K spectrum quality has been improved for each receiver station using superposition of surface waves in the F-K domain by adding more shots. The S-wave velocity inverted from dispersion curves showed good agreement with the synthetic model. Second, high quality F-K spectrum generated from the above method enabled us to pick both fundamental and 1<sup>st</sup> order dispersion curves from the SinoProbe field data. The S-wave velocity was generated using three methods: 1) empirical equations based on dispersion curves; 2) fundamental order dispersion curves inversion; and 3) both fundamental and 1<sup>st </sup>order dispersion curves inversion. Results show that using higher order dispersion curves can generate a more reliable near-surface model. Third, first breaks were picked up to 18 km offset and diving wave tomography was applied to derive near-surface P-wave velocity from abundant first break information. It is shown that there is an excellent correlation between P- and S-wave velocities, the bottom of basin is clearly revealed, and over-thrusts are identified accordingly which is consistent with field geological survey in the middle segment of Bangong-Nujiang suture zone.</p><p>This study was financially supported by the CAGS Research Fund (grant YWF201907), and the National Natural Science Foundation of China (grant 41761134094). Data sources: SinoProbe-02 Project.</p>

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. EN95-EN105 ◽  
Author(s):  
Tatsunori Ikeda ◽  
Takeshi Tsuji ◽  
Toshifumi Matsuoka

CMP crosscorrelation (CMPCC) analysis of surface waves enhances lateral resolution of surface wave analyses. We found the technique of window-controlled CMPCC analysis, which applies two kinds of spatial windows to further improve the lateral resolution of CMPCC analysis. First, a spatial weighting function given by the number of crosscorrelation pairs is applied to CMPCC gathers. Because the number of crosscorrelation pairs is concentrated near the CMP, the lateral resolution in extracting dispersion curves on CMPs can be improved. Second, crosscorrelation pairs with longer receiver spacing are excluded to further improve lateral resolution. Although removing crosscorrelation pairs generally decreases the accuracy of phase velocity estimations, the required accuracy to estimate phase velocities is maintained by considering the wavenumber resolution defined for given receiver configurations. When applied to a synthetic data set simulating a laterally heterogeneous structure, window-controlled CMPCC analysis improved the retrieval of the lateral variation in local dispersion curves beneath each CMP. We also applied the method to field seismic data across a major fault. The window-controlled CMPCC analysis improved lateral variations of the inverted S-wave velocity structure without degrading the accuracy of S-wave velocity estimations. We discovered that window-controlled CMPCC analysis is effective in improving lateral resolution of dispersion curve estimations with respect to the original CMPCC analysis.


Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. EN105-EN117
Author(s):  
Kai Zhang ◽  
Hongyi Li ◽  
Xiaojiang Wang ◽  
Kai Wang

In urban subsurface exploration, seismic surveys are mostly conducted along roads where seismic vibrators can be extensively used to generate strong seismic energy due to economic and environmental constraints. Generally, Rayleigh waves also are excited by the compressional wave profiling process. Shear-wave (S-wave) velocities can be inferred using Rayleigh waves to complement near-surface characterization. Most vibrators cannot excite seismic energy at lower frequencies (<5 Hz) to map greater depths during surface-wave analysis in areas with low S-wave velocities, but low-frequency surface waves ([Formula: see text]) can be extracted from traffic-induced noise, which can be easily obtained at marginal additional cost. We have implemented synthetic tests to evaluate the velocity deviation caused by offline sources, finding a reasonably small relative bias of surface-wave dispersion curves due to vehicle sources on roads. Using a 2D reflection survey and traffic-induced noise from the central North China Plain, we apply seismic interferometry to a series of 10.0 s segments of passive data. Then, each segment is selectively stacked on the acausal-to-causal ratio of the mean signal-to-noise ratio to generate virtual shot gathers with better dispersion energy images. We next use the dispersion curves derived by combining controlled source surveying with vehicle noise to retrieve the shallow S-wave velocity structure. A maximum exploration depth of 90 m is achieved, and the inverted S-wave profile and interval S-wave velocity model obtained from reflection processing appear consistent. The data set demonstrates that using surface waves derived from seismic reflection surveying and traffic-induced noise provides an efficient supplementary technique for delineating shallow structures in areas featuring thick Quaternary overburden. Additionally, the field test indicates that traffic noise can be created using vehicles or vibrators to capture surface waves within a reliable frequency band of 2–25 Hz if no vehicles are moving along the survey line.


Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. R1-R14 ◽  
Author(s):  
Yudi Pan ◽  
Jianghai Xia ◽  
Yixian Xu ◽  
Lingli Gao ◽  
Zongbo Xu

High-frequency surface-wave techniques are widely used to estimate S-wave velocity of near-surface materials. Surface-wave methods based on inversions of dispersion curves are only suitable to laterally homogeneous or smoothly laterally varying heterogeneous earth models due to the layered-model assumption during calculation of dispersion curves. Waveform inversion directly fits the waveform of observed data, and it can be applied to any kinds of earth models. We have used the Love-wave waveform inversion in the time domain to estimate near-surface S-wave velocity. We used the finite-difference method as the forward modeling method. The source effect was removed by the deconvolution technique, which made our method independent of the source wavelet. We defined the difference between the deconvolved observed and calculated waveform as the misfit function. We divided the model into different sizes of blocks depending on the resolution of the Love waves, and we updated the S-wave velocity of each block via a conjugate gradient algorithm. We used two synthetic models to test the effectiveness of our method. A real-world case verified the validity of our method.


Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. EN1-EN11 ◽  
Author(s):  
Tatsunori Ikeda ◽  
Toshifumi Matsuoka ◽  
Takeshi Tsuji ◽  
Toru Nakayama

In surface-wave analysis, S-wave velocity estimations can be improved by the use of higher modes of the surface waves. The vertical component of P-SV waves is commonly used to estimate multimode Rayleigh waves, although Rayleigh waves are also included in horizontal components of P-SV waves. To demonstrate the advantages of using the horizontal components of multimode Rayleigh waves, we investigated the characteristics of the horizontal and vertical components of Rayleigh waves. We conducted numerical modeling and field data analyses rather than a theoretical study for both components of Rayleigh waves. As a result of a simulation study, we found that the estimated higher modes have larger relative amplitudes in the vertical and horizontal components as the source depth increases. In particular, higher-order modes were observed in the horizontal component data for an explosive source located at a greater depth. Similar phenomena were observed in the field data acquired by using a dynamite source at 15-m depth. Sensitivity analyses of dispersion curves to S-wave velocity changes revealed that dispersion curves additionally estimated from the horizontal components can potentially improve S-wave velocity estimations. These results revealed that when the explosive source was buried at a greater depth, the horizontal components can complement Rayleigh waves estimated from the vertical components. Therefore, the combined use of the horizontal component data with the vertical component data would contribute to improving S-wave velocity estimations, especially in the case of buried explosive source signal.


2020 ◽  
Vol 17 (6) ◽  
pp. 940-955
Author(s):  
Zhiwei You ◽  
Peifen Xu ◽  
Suqun Ling ◽  
Yanan Du ◽  
Ruohan Zhang ◽  
...  

Abstract Due to its efficiency, convenience, non-destructive nature and strong anti-interference capability, the microtremor survey method (MSM) has found wide applications in urban geological surveys. The spatial autocorrelation method is diffusely applied to extract the dispersion curves from microtremor signals, which needs to satisfy the assumption that the energy of the fundamental Rayleigh wave is dominant. However, for layered media containing a layer with a significant low- or high-velocity contrast, this assumption is distinctly incorrect for certain frequency ranges. We present a processing methodology comprising the extraction and inversion of the apparent dispersion curves based on extended spatial autocorrelation method and fast simulated-annealing algorithm. We analyse synthetic microtremor signals for three selected geological models, and then compare the S-wave velocity structures estimated from their inversions with the actual models. Subsequently, a filed data example is given to detect the shallow stratigraphic structures in Guangzhou city, China, in which the new MSM was used. The estimated two-dimensional S-wave velocity model provided an accurate description of the thickness and depth of the strata in the study area, based on a priori information. Moreover, the S-wave velocity structures estimated from the MSM and the results from the drilling match very well, indicating that MSM is a reliable geophysical technique in urban geological surveys. Combined with available borehole information, MSM can be a very robust and effective method for detecting the shallow three-dimensional velocity structures in an urban area.


2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  

SUMMARY The analysis of surface wave dispersion curves (DCs) is widely used for near-surface S-wave velocity (VS) reconstruction. However, a comprehensive characterization of the near-surface requires also the estimation of P-wave velocity (VP). We focus on the estimation of both VS and VP models from surface waves using a direct data transform approach. We estimate a relationship between the wavelength of the fundamental mode of surface waves and the investigation depth and we use it to directly transform the DCs into VS and VP models in laterally varying sites. We apply the workflow to a real data set acquired on a known test site. The accuracy of such reconstruction is validated by a waveform comparison between field data and synthetic data obtained by performing elastic numerical simulations on the estimated VP and VS models. The uncertainties on the estimated velocity models are also computed.


Author(s):  
Zhanbo Ji ◽  
Baoshan Wang ◽  
Wei Yang ◽  
Weitao Wang ◽  
Jinbo Su ◽  
...  

ABSTRACT Basins with thick sediments can amplify and prolong the incoming seismic waves, which may cause serious damage to surface facilities. The amplification of seismic energy depends on the shear-wave velocity of the uppermost layers, which is generally estimated through surface wave analysis. Surface waves may propagate in different modes, and the mechanism of the mode development is not well understood. Exploiting a recently deployed permanent airgun source in the Hutubi basin, Xinjiang, northwest China, we conducted a field experiment to investigate the development of multimode surface waves. We observed surface waves at the frequency of 0.3–5.0 Hz with apparent group velocities of 200–900  m/s, and identified five modes of surface waves (three Rayleigh-wave modes and two Love-wave modes) through time–frequency and particle-motion analyses. We then measured 125 group velocity dispersion curves of the fundamental- and higher-mode surface waves, and further inverted the 1D S-wave velocity structure of the Hutubi basin. The S-wave velocity increases abruptly from 238  m/s at the surface to 643  m/s at 300 m depth. Synthetic seismograms with the inverted velocity structure capture the main features of the surface waves of the different modes. Synthetic tests suggest that the low velocity, high velocity gradient, and shallow source depth are likely the dominant contributing factors in the development of higher-mode surface waves.


Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. EN57-EN65 ◽  
Author(s):  
Zhen-Dong Zhang ◽  
Tariq Alkhalifah

Recorded surface waves often provide reasonable estimates of the S-wave velocity in the near surface. However, existing algorithms are mainly based on the 1D layered-model assumption and require picking the dispersion curves either automatically or manually. We have developed a wave-equation-based inversion algorithm that inverts for S-wave velocities using fundamental and higher mode Rayleigh waves without picking an explicit dispersion curve. Our method aims to maximize the similarity of the phase velocity spectrum ([Formula: see text]) of the observed and predicted surface waves with all Rayleigh-wave modes (if they exist) included in the inversion. The [Formula: see text] spectrum is calculated using the linear Radon transform applied to a local similarity-based objective function; thus, we do not need to pick velocities in spectrum plots. As a result, the best match between the predicted and observed [Formula: see text] spectrum provides the optimal estimation of the S-wave velocity. We derive S-wave velocity updates using the adjoint-state method and solve the optimization problem using a limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Our method excels in cases in which the S-wave velocity has vertical reversals and lateral variations because we used all-modes dispersion, and it can suppress the local minimum problem often associated with full-waveform inversion applications. Synthetic and field examples are used to verify the effectiveness of our method.


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