Retrieval of shallow S-wave profiles from seismic reflection surveying and traffic-induced noise

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. EN105-EN117
Author(s):  
Kai Zhang ◽  
Hongyi Li ◽  
Xiaojiang Wang ◽  
Kai Wang
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Wave Velocity ◽  
Rayleigh Waves ◽  
Seismic Reflection ◽  
Seismic Energy ◽  
Dispersion Curves ◽  
S Wave ◽  
Near Surface ◽  
Wave Velocities

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.

Love-wave waveform inversion in time domain for shallow shear-wave velocity

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. R1-R14 ◽  
Author(s):  
Yudi Pan ◽  
Jianghai Xia ◽  
Yixian Xu ◽  
Lingli Gao ◽  
Zongbo Xu
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Time Domain ◽  
Love Wave ◽  
Waveform Inversion ◽  
Dispersion Curves ◽  
Gradient Algorithm ◽  
S Wave ◽  
Near Surface ◽  
Earth Models

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.

scholarly journals Characteristics of the horizontal component of Rayleigh waves in multimode analysis of surface waves

Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. EN1-EN11 ◽  
Author(s):  
Tatsunori Ikeda ◽  
Toshifumi Matsuoka ◽  
Takeshi Tsuji ◽  
Toru Nakayama
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Rayleigh Waves ◽  
Field Data ◽  
Vertical Component ◽  
Dispersion Curves ◽  
Horizontal Component ◽  
S Wave ◽  
Higher Modes ◽  
Explosive Source

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.

A comprehensive comparison between the refraction microtremor and seismic interferometry methods for phase-velocity estimation

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. EN99-EN108 ◽  
Author(s):  
Zongbo Xu ◽  
T. Dylan Mikesell ◽  
Jianghai Xia ◽  
Feng Cheng
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Wave Velocity ◽  
Velocity Estimation ◽  
Seismic Interferometry ◽  
S Wave ◽  
Noise Sources ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Refraction Microtremor

Passive-source seismic-noise-based surface-wave methods are now routinely used to investigate the near-surface geology in urban environments. These methods estimate the S-wave velocity of the near surface, and two methods that use linear recording arrays are seismic interferometry (SI) and refraction microtremor (ReMi). These two methods process noise data differently and thus can yield different estimates of the surface-wave dispersion, the data used to estimate the S-wave velocity. We have systematically compared these two methods using synthetic data with different noise source distributions. We arrange sensors in a linear survey grid, which is conveniently used in urban investigations (e.g., along roads). We find that both methods fail to correctly determine the low-frequency dispersion characteristics when outline noise sources become stronger than inline noise sources. We also identify an artifact in the ReMi method and theoretically explain the origin of this artifact. We determine that SI combined with array-based analysis of surface waves is the more accurate method to estimate surface-wave phase velocities because SI separates surface waves propagating in different directions. Finally, we find a solution to eliminate the ReMi artifact that involves the combination of SI and the [Formula: see text]-[Formula: see text] transform, the array processing method that underlies the ReMi method.

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

2020 ◽  
Author(s):  
Hui Zhang ◽  
Rizheng He ◽  
Zhiwei Liu
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Velocity Structure ◽  
Dispersion Curves ◽  
Suture Zone ◽  
Surface Velocity ◽  
S Wave ◽  
Middle Segment ◽  
Near Surface ◽  
Order Dispersion

&lt;p&gt;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&amp;#180;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&amp;#8217;t have sufficient offset density at near surface which poses a challenge to conventional near-surface velocity analysis methods.&lt;/p&gt;&lt;p&gt;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&amp;#160;the&amp;#160;synthetic model. Second, high quality F-K spectrum generated from the above method enabled us to pick both fundamental and 1&lt;sup&gt;st&lt;/sup&gt; 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&lt;sup&gt;st &lt;/sup&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

3D elastic full-waveform inversion of surface waves in the presence of irregular topography using an envelope-based misfit function

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Dmitry Borisov ◽  
Ryan Modrak ◽  
Fuchun Gao ◽  
Jeroen Tromp
Keyword(s):  
Surface Wave ◽  
Objective Function ◽  
Surface Waves ◽  
Large Scale ◽  
Body Wave ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
S Wave ◽  
Near Surface ◽  
Full Waveform

Full-waveform inversion (FWI) is a powerful method for estimating the earth’s material properties. We demonstrate that surface-wave-driven FWI is well-suited to recovering near-surface structures and effective at providing S-wave speed starting models for use in conventional body-wave FWI. Using a synthetic example based on the SEG Advanced Modeling phase II foothills model, we started with an envelope-based objective function to invert for shallow large-scale heterogeneities. Then we used a waveform-difference objective function to obtain a higher-resolution model. To accurately model surface waves in the presence of complex tomography, we used a spectral-element wave-propagation solver. Envelope misfit functions are found to be effective at minimizing cycle-skipping issues in surface-wave inversions, and surface waves themselves are found to be useful for constraining complex near-surface features.

Full-waveform matching of VP and VS models from surface waves

2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Real Data ◽  
Test Site ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Near Surface ◽  
Velocity Models

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.

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