Computing near-surface velocity models for S-wave static corrections using raypath interferometry

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. U23-U34
Author(s):  
Raul Cova ◽  
David Henley ◽  
Kristopher A. Innanen
Keyword(s):  
Wave Velocity ◽  
Velocity Model ◽  
P Wave ◽  
Surface Velocity ◽  
S Wave ◽  
Converted Wave ◽  
Near Surface ◽  
Velocity Models ◽  
Consistent Solution ◽  
Static Corrections

A near-surface velocity model is one of the typical products generated when computing static corrections, particularly in the processing of PP data. Critically refracted waves are the input usually needed for this process. In addition, for the converted PS mode, S-wave near-surface corrections must be applied at the receiver locations. In this case, however, critically refracted S-waves are difficult to identify when using P-wave energy sources. We use the [Formula: see text]-[Formula: see text] representation of the converted-wave data to capture the intercept-time differences between receiver locations. These [Formula: see text]-differences are then used in the inversion of a near-surface S-wave velocity model. Our processing workflow provides not only a set of raypath-dependent S-wave static corrections, but also a velocity model that is based on those corrections. Our computed near-surface S-wave velocity model can be used for building migration velocity models or to initialize elastic full-waveform inversions. Our tests on synthetic and field data provided superior results to those obtained by using a surface-consistent solution.

scholarly journals The potential of seismic cross-hole tomography for geotechnical site investigation

2019 ◽  
Vol 92 ◽  
pp. 18006
Author(s):  
Yannick Choy Hing Ng ◽  
William Danovan ◽  
Taeseo Ku
Keyword(s):  
Wave Velocity ◽  
Site Investigation ◽  
Velocity Model ◽  
P Wave ◽  
Reconstruction Technique ◽  
Sufficient Information ◽  
Eastern Region ◽  
S Wave ◽  
Near Surface ◽  
Soil Layers

Seismic cross-hole tomography has been commonly used in oil and gas exploration and the mining industry for the detection of precious resources. For near-surface geotechnical site investigation, this geophysical method is relatively new and can be used to supplement traditional methods such as the standard penetration test, coring and sampling, thus improving the effectiveness of site characterization. This paper presents a case study which was carried out on a reclaimed land in the Eastern region of Singapore. A seismic cross-hole test was performed by generating both compressional waves and shear waves into the ground. The signals were interpreted by using first-arrival travel time wave tomography and the arrival times were subsequently inverted using Simultaneous Iterative Reconstruction Technique (SIRT). A comparison with the borehole logging data indicated that P-wave velocity model cannot provide sufficient information about the soil layers, especially when the ground water table is near the surface. The S-wave velocity model seemed to agree quite well with the variation in the SPT-N value and could identify to a certain extent the interface between the different soil layers. Finally, P-wave and S-wave velocities are used to compute the Poisson's ratio distribution which gave a good indication of the degree of saturation of the soil.

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.

Elastic reflection waveform inversion based on the decomposition of sensitivity kernels

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R235-R250 ◽  
Author(s):  
Zhiming Ren ◽  
Zhenchun Li ◽  
Bingluo Gu
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Background Model ◽  
Reverse Time ◽  
S Wave ◽  
Velocity Models ◽  
Time Migration ◽  
Starting Model

Full-waveform inversion (FWI) has the potential to obtain an accurate velocity model. Nevertheless, it depends strongly on the low-frequency data and the initial model. When the starting model is far from the real model, FWI tends to converge to a local minimum. Based on a scale separation of the model (into the background model and reflectivity model), reflection waveform inversion (RWI) can separate out the tomography term in the conventional FWI kernel and invert for the long-wavelength components of the velocity model by smearing the reflected wave residuals along the transmission (or “rabbit-ear”) paths. We have developed a new elastic RWI method to build the P- and S-wave velocity macromodels. Our method exploits a traveltime-based misfit function to highlight the contribution of tomography terms in the sensitivity kernels and a sensitivity kernel decomposition scheme based on the P- and S-wave separation to suppress the high-wavenumber artifacts caused by the crosstalk of different wave modes. Numerical examples reveal that the gradients of the background models become sufficiently smooth owing to the decomposition of sensitivity kernels and the traveltime-based misfit function. We implement our elastic RWI in an alternating way. At each loop, the reflectivity model is generated by elastic least-squares reverse time migration, and then the background model is updated using the separated traveltime kernels. Our RWI method has been successfully applied in synthetic and real reflection seismic data. Inversion results demonstrate that the proposed method can retrieve preferable low-wavenumber components of the P- and S-wave velocity models, which are reliable to serve as a starting model for conventional elastic FWI. Also, our method with a two-stage inversion workflow, first updating the P-wave velocity using the PP kernels and then updating the S-wave velocity using the PS kernels, is feasible and robust even when P- and S-wave velocities have different structures.

Time-reverse imaging with limited S-wave velocity model information

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. MA33-MA40 ◽  
Author(s):  
Brian Steiner ◽  
Erik H. Saenger ◽  
Stefan M. Schmalholz
Keyword(s):  
Energy Density ◽  
Wave Velocity ◽  
Wave Energy ◽  
Velocity Model ◽  
Body Waves ◽  
P Wave ◽  
S Wave ◽  
Velocity Models ◽  
Wave Energy Density ◽  
Wavefield Extrapolation

Time-reverse imaging is a wave propagation algorithm for locating sources. Signals recorded by synchronized receivers are reversed in time and propagated back to the source location by elastic wavefield extrapolation. Elastic wavefield extrapolation requires a P-wave as well as an S-wave velocity model. The velocity models available from standard reflection seismic methods are usually restricted to only P-waves. In this study, we use synthetically produced time signals to investigate the accuracy of seismic source localization by means of time-reverse imaging with the correct P-wave and a perturbed S-wave velocity model. The studies reveal that perturbed S-wave velocity models strongly influence the intensity and position of the focus. Imaging the results with the individual maximum energy density for both body wave types instead of mixed modes allows individual analysis of the two body waves. P-wave energy density images render stable focuses in case of a correct P-wave and incorrect S-wave velocity model. Thus, P-wave energy density seems to be a more suitable imaging condition in case of a high degree of uncertainty in the S-wave velocity model.

scholarly journals Time-average velocity estimation through surface-wave analysis: Part 1 — S-wave velocity

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. U49-U59 ◽  
Author(s):  
Laura Valentina Socco ◽  
Cesare Comina ◽  
Farbod Khosro Anjom
Keyword(s):  
Wave Velocity ◽  
Average Velocity ◽  
Field Data ◽  
Velocity Model ◽  
Velocity Estimation ◽  
S Wave ◽  
Simple Method ◽  
Near Surface ◽  
Velocity Models ◽  
Time Average

In some areas, the estimation of static corrections for land seismic data is a critical step of the processing workflow. It often requires the execution of additional surveys and data analyses. Surface waves (SWs) in seismic records can be processed to extract local dispersion curves (DCs) that can be used to estimate near-surface S-wave velocity models. Here we focus on the direct estimation of time-average S-wave velocity models from SW DCs without the need to invert the data. Time-average velocity directly provides the value of one-way time, given a datum plan depth. The method requires the knowledge of one 1D S-wave velocity model along the seismic line, together with the relevant DC, to estimate a relationship between SW wavelength and investigation depth on the time-average velocity model. This wavelength/depth relationship is then used to estimate all the other time-average S-wave velocity models along the line directly from the DCs by means of a data transformation. This approach removes the need for extensive data inversion and provides a simple method suitable for industrial workflows. We tested the method on synthetic and field data and found that it is possible to retrieve the time-average velocity models with uncertainties less than 10% in sites with laterally varying velocities. The error on one-way times at various depths of the datum plan retrieved by the time-average velocity models is mostly less than 5 ms for synthetic and field data.

Shallow velocity structure and poisson's ratio at the Tarzana, California, strong-motion accelerometer site

1996 ◽  
Vol 86 (6) ◽  
pp. 1704-1713 ◽  
Author(s):  
R. D. Catchings ◽  
W. H. K. Lee
Keyword(s):  
Urban Areas ◽  
Velocity Structure ◽  
Strong Motion ◽  
P Wave ◽  
S Wave ◽  
Near Surface ◽  
Velocity Models ◽  
Wave Velocities ◽  
Poisson's Ratios ◽  
Poisson’S Ratios

Abstract The 17 January 1994, Northridge, California, earthquake produced strong ground shaking at the Cedar Hills Nursery (referred to here as the Tarzana site) within the city of Tarzana, California, approximately 6 km from the epicenter of the mainshock. Although the Tarzana site is on a hill and is a rock site, accelerations of approximately 1.78 g horizontally and 1.2 g vertically at the Tarzana site are among the highest ever instrumentally recorded for an earthquake. To investigate possible site effects at the Tarzana site, we used explosive-source seismic refraction data to determine the shallow (<70 m) P-and S-wave velocity structure. Our seismic velocity models for the Tarzana site indicate that the local velocity structure may have contributed significantly to the observed shaking. P-wave velocities range from 0.9 to 1.65 km/sec, and S-wave velocities range from 0.20 and 0.6 km/sec for the upper 70 m. We also found evidence for a local S-wave low-velocity zone (LVZ) beneath the top of the hill. The LVZ underlies a CDMG strong-motion recording site at depths between 25 and 60 m below ground surface (BGS). Our velocity model is consistent with the near-surface (<30 m) P- and S-wave velocities and Poisson's ratios measured in a nearby (<30 m) borehole. High Poisson's ratios (0.477 to 0.494) and S-wave attenuation within the LVZ suggest that the LVZ may be composed of highly saturated shales of the Modelo Formation. Because the lateral dimensions of the LVZ approximately correspond to the areas of strongest shaking, we suggest that the highly saturated zone may have contributed to localized strong shaking. Rock sites are generally considered to be ideal locations for site response in urban areas; however, localized, highly saturated rock sites may be a hazard in urban areas that requires further investigation.

Two robust imaging methodologies for challenging environments: Wave-equation dispersion inversion of surface waves and guided waves and supervirtual interferometry + tomography for far-offset refractions

2018 ◽  
Vol 6 (4) ◽  
pp. SM27-SM37 ◽  
Author(s):  
Jing Li ◽  
Kai Lu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Wave Equation ◽  
Velocity Distribution ◽  
Surface Waves ◽  
Guided Waves ◽  
Velocity Model ◽  
Dispersion Curves ◽  
P Wave ◽  
Head Wave ◽  
Near Surface ◽  
Velocity Models

Two robust imaging technologies are reviewed that provide subsurface geologic information in challenging environments. The first one is wave-equation dispersion (WD) inversion of surface waves and guided waves (GW) for the shear-velocity (S-wave) and compressional-velocity (P-wave) models, respectively. The other method is traveltime inversion for the velocity model, in which supervirtual refraction interferometry (SVI) is used to enhance the signal-to-noise ratio of far-offset refractions. We have determined the benefits and liabilities of both methods with synthetic seismograms and field data. The benefits of WD are that (1) there is no layered-medium assumption, as there is in conventional inversion of dispersion curves. This means that 2D or 3D velocity models can be accurately estimated from data recorded by seismic surveys over rugged topography, and (2) WD mostly avoids getting stuck in local minima. The liability is that WD for surface waves is almost as expensive as full-waveform inversion (FWI) and, for Rayleigh waves, only recovers the S-velocity distribution to a depth no deeper than approximately 1/2 to 1/3 wavelength of the lowest-frequency surface wave. The limitation for GW is that, for now, it can estimate the P-velocity model by inverting the dispersion curves from GW propagating in near-surface low-velocity zones. Also, WD often requires user intervention to pick reliable dispersion curves. For SVI, the offset of usable refractions can be more than doubled, so that traveltime tomography can be used to estimate a much deeper model of the P-velocity distribution. This can provide a more effective starting velocity model for FWI. The liability is that SVI assumes head-wave first arrivals, not those from strong diving waves.

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