Inversion of donor vertical time shifts to update a near-surface depth/velocity model

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. B23-B33
Author(s):  
Ralph Bridle ◽  
Shelton Hubbell
Keyword(s):  
Joint Inversion ◽  
Low Frequency ◽  
Velocity Model ◽  
Generalized Solution ◽  
Surface Model ◽  
Additional Time ◽  
Near Surface ◽  
Long Wavelength ◽  
Velocity Models ◽  
Static Corrections

The near-surface model for static corrections requires a consistent regional depth/velocity model, while incorporating the fidelity of additional static solutions. We addressed the challenge of tying new seismic acquisition to a depth/velocity model, in which there are static corrections derived independently for each seismic data. The generalized method is a four-step procedure that starts with the grafting of the additional shifts to the recipient static model. The time shifts were then adjusted to constrain the long wavelength at defined locations. The next procedure was to split the time shifts into high- and low-frequency components. The final procedure inverted the high frequency into the shallowest layers and the long wavelength to the velocity from base of model to datum. The result was an updated regional depth/velocity model into which new 2D depth/velocity models could be tied. The generalized solution would work with any additional near-surface static corrections, which could include, and not be limited to, those built from surface waves, remote sensing, and joint inversion with nonseismic data. The inversion of the additional time shifts was primarily intended to provide a solution to their tie and any image improvement is serendipitous. We progressively learned lessons from a simple inversion and achieved the generalized solution.

3‐D tomographic static correction

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1275-1285 ◽  
Author(s):  
Xu Chang ◽  
Yike Liu ◽  
Hui Wang ◽  
Fuzhong Li ◽  
Jing Chen
Keyword(s):  
Computation Time ◽  
Matrix Decomposition ◽  
Velocity Model ◽  
Seismic Survey ◽  
Surface Model ◽  
Low Velocity ◽  
Near Surface ◽  
Model Inversion ◽  
Static Corrections ◽  
Refracted Waves

A 3‐D tomographic inversion approach based on a surface‐consistent model for static corrections is presented in this paper. Direct, reflected, and refracted waves are used simultaneously to update the near‐surface model. We analyze the characteristics of the first‐break traveltime in complicated low‐velocity layers. To improve the accuracy for the velocity model, the various first‐break times from direct, reflected, and refracted waves are considered for model inversion. A fractal algorithm which overcomes the error caused by wavelet shape differences is applied to pick first breaks. It also overcomes the leg jump of refractions. The method can pick a large number of first breaks automatically. The raypaths and traveltimes are calculated with a 3‐D ray tracer that does not increase computation time for complicated geological models. Our method can determine the raypath associated with minimum traveltimes regardless of wave mode (direct, refracted, or reflected). We use a least‐squares approach in conjunction with a matrix decomposition to reconstruct a 3‐D velocity model from the actual first‐break times obtained from 3‐D data. Finally, long‐ and short‐wavelength static corrections are calculated concurrently, based on the reconstructed velocity profile. The method can be applied to wide‐line profiles, crooked lines, and 2‐D and 3‐D seismic survey geometries. The results applied to a real 3‐D data example indicate that the 3‐D tomographic static corrections are effective for field data.

scholarly journals Shear wave structure of a transect of the Los Angeles basin from multimode surface waves and H/V spectral ratio analysis

2019 ◽  
Vol 220 (1) ◽  
pp. 415-427 ◽  
Author(s):  
Mathieu Perton ◽  
Zack J Spica ◽  
Robert W Clayton ◽  
Gregory C Beroza
Keyword(s):  
Los Angeles ◽  
Joint Inversion ◽  
Seismic Station ◽  
Broad Band ◽  
Low Frequency ◽  
Wave Structure ◽  
Spectral Ratio ◽  
Velocity Model ◽  
Velocity Models ◽  
Rayleigh And Love Waves

SUMMARY We use broad-band stations of the ‘Los Angeles Syncline Seismic Interferometry Experiment’ (LASSIE) to perform a joint inversion of the Horizontal to Vertical spectral ratios (H/V) and multimode dispersion curves (phase and group velocity) for both Rayleigh and Love waves at each station of a dense line of sensors. The H/V of the autocorrelated signal at a seismic station is proportional to the ratio of the imaginary parts of the Green’s function. The presence of low-frequency peaks (∼0.2 Hz) in H/V allows us to constrain the structure of the basin with high confidence to a depth of 6 km. The velocity models we obtain are broadly consistent with the SCEC CVM-H community model and agree well with known geological features. Because our approach differs substantially from previous modelling of crustal velocities in southern California, this research validates both the utility of the diffuse field H/V measurements for deep structural characterization and the predictive value of the CVM-H community velocity model in the Los Angeles region. We also analyse a lower frequency peak (∼0.03 Hz) in H/V and suggest it could be the signature of the Moho. Finally, we show that the independent comparison of the H and V components with their corresponding theoretical counterparts gives information about the degree of diffusivity of the ambient seismic field.

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.

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.

Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R63-R75 ◽  
Author(s):  
Gregory Ely ◽  
Alison Malcolm ◽  
Oleg V. Poliannikov
Keyword(s):  
Uncertainty Quantification ◽  
Seismic Imaging ◽  
Low Frequency ◽  
Expansion Method ◽  
Velocity Model ◽  
Image Features ◽  
Visible Image ◽  
Waveform Data ◽  
Velocity Models ◽  
Seismic Reflections

Seismic imaging is conventionally performed using noisy data and a presumably inexact velocity model. Uncertainties in the input parameters propagate directly into the final image and therefore into any quantity of interest, or qualitative interpretation, obtained from the image. We considered the problem of uncertainty quantification in velocity building and seismic imaging using Bayesian inference. Using a reduced velocity model, a fast field expansion method for simulating recorded wavefields, and the adaptive Metropolis-Hastings algorithm, we efficiently quantify velocity model uncertainty by generating multiple models consistent with low-frequency full-waveform data. A second application of Bayesian inversion to any seismic reflections present in the recorded data reconstructs the corresponding structures’ position along with its associated uncertainty. Our analysis complements rather than replaces traditional imaging because it allows us to assess the reliability of visible image features and to take that into account in subsequent interpretations.

Full waveform inversion: early start of model building process to resolve the complex near surface model in the Exmouth sub-basin, North West Shelf Australia

2018 ◽  
Vol 58 (2) ◽  
pp. 884
Author(s):  
Lianping Zhang ◽  
Haryo Trihutomo ◽  
Yuelian Gong ◽  
Bee Jik Lim ◽  
Alexander Karvelas
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Surface Velocity ◽  
Image Point ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
North West ◽  
Full Waveform

The Schlumberger Multiclient Exmouth 3D survey was acquired over the Exmouth sub-basin, North West Shelf Australia and covers 12 600 km2. One of the primary objectives of this survey was to produce a wide coverage of high quality imaging with advanced processing technology within an agreed turnaround time. The complexity of the overburden was one of the imaging challenges that impacted the structuration and image quality at the reservoir level. Unlike traditional full-waveform inversion (FWI) workflow, here, FWI was introduced early in the workflow in parallel with acquisition and preprocessing to produce a reliable near surface velocity model from a smooth starting model. FWI derived an accurate and detailed near surface model, which subsequently benefitted the common image point (CIP) tomography model updates through to the deeper intervals. The objective was to complete the FWI model update for the overburden concurrently with the demultiple stages hence reflection time CIP tomography could start with a reasonably good velocity model upon completion of the demultiple process.

Inversion of seismic refraction and reflection data for building long-wavelength velocity models

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. R81-R93 ◽  
Author(s):  
Haiyang Wang ◽  
Satish C. Singh ◽  
Francois Audebert ◽  
Henri Calandra
Keyword(s):  
Wave Equation ◽  
Short Wavelength ◽  
Velocity Model ◽  
Density Model ◽  
Data Set ◽  
Reflected Waves ◽  
Long Wavelength ◽  
Velocity Models ◽  
Computation Efficiency ◽  
Refracted Waves

Long-wavelength velocity model building is a nonlinear process. It has traditionally been achieved without appealing to wave-equation-based approaches for combined refracted and reflected waves. We developed a cascaded wave-equation tomography method in the data domain, taking advantage of the information contained in the reflected and refracted waves. The objective function was the traveltime residual that maximized the crosscorrelation function between real and synthetic data. To alleviate the nonlinearity of the inversion problem, refracted waves were initially used to provide vertical constraints on the velocity model, and reflected waves were then included to provide lateral constraints. The use of reflected waves required scale separation. We separated the long- and short-wavelength subsurface structures into velocity and density models, respectively. The velocity model update was restricted to long wavelengths during the wave-equation tomography, whereas the density model was used to absorb all the short-wavelength impedance contrasts. To improve the computation efficiency, the density model was converted into the zero-offset traveltime domain, where it was invariant to changes of the long-wavelength velocity model. After the wave-equation tomography has derived an optimized long-wavelength velocity model, full-waveform inversion was used to invert all the data to retrieve the short-wavelength velocity structures. We developed our method in two synthetic tests and then applied it to a marine field data set. We evaluated the results of the use of refracted and reflected waves, which was critical for accurately building the long-wavelength velocity model. We showed that our wave-equation tomography strategy was robust for the real data application.

Model–parameterization scaling for improving accuracy of seismic tomography in reconstructing near–surface velocity model

2020 ◽  
Author(s):  
Gleb S. Chernyshov ◽  
◽  
Anton A. Duchkov ◽  
Aleksander A. Nikitin ◽  
Ivan Yu. Kulakov ◽  
...  
Keyword(s):  
Model Building ◽  
Regularization Parameter ◽  
Velocity Model ◽  
Surface Velocity ◽  
Surface Model ◽  
Tomographic Inversion ◽  
Near Surface ◽  
Model Parameterization ◽  
Regularization Parameters ◽  
Improving Accuracy

The problem of tomographic inversion is non–unique and requires regularization to solve it in a stable manner. It is highly non–trivial to choose between various regularization approaches or tune the regularization parameters themselves. We study the influence of one particular regularization parameter on the resolution and accuracy the tomographic inversion for the near–surface model building. We propose another regularization parameter, which allows to increase the accuracy of model building.

Near-surface modeling challenges highlighted in recent workshop

2020 ◽  
Vol 39 (5) ◽  
pp. 354-356
Author(s):  
Abdulaziz Saad ◽  
Moosa Al-Jahdhami
Keyword(s):  
Guided Waves ◽  
Joint Inversion ◽  
Waveform Inversion ◽  
Surface Modeling ◽  
Hydrocarbon Exploration ◽  
Surface Model ◽  
Near Surface ◽  
Velocity Inversion ◽  
Geophysical Imaging ◽  
Near Surface Geophysics

Despite technological and computational advances in geophysical imaging, near-surface geophysics continues to pose significant challenges in modeling and imaging the subsurface. Geoscientists from around the world attended the first and second editions of the SEG/DGS Near-surface Modeling and Imaging Workshop in 2014 and 2016 to address these challenges. A range of near-surface disciplines were represented from academia and industry, covering aspects of engineering and hydrocarbon exploration. The previous workshops explored emerging and underdeveloped techniques, including deep learning (machine learning), nonseismic methods, full-waveform inversion (FWI), and joint inversion. The necessity to further understand guided waves, anisotropy, velocity inversion, and the creation of an inclusive near-surface model was identified. The previous editions led to a greater understanding of the importance of knowledge sharing among various disciplines in modeling and imaging of the near surface.

Overthrust imaging with tomo‐datuming: A case study

Geophysics ◽  
1998 ◽  
Vol 63 (1) ◽  
pp. 25-38 ◽  
Author(s):  
Xianhuai Zhu ◽  
Burke G. Angstman ◽  
David P. Sixta
Keyword(s):  
Velocity Model ◽  
Surface Velocity ◽  
Time Shift ◽  
Lateral Velocity ◽  
Prestack Depth Migration ◽  
Near Surface ◽  
Depth Migration ◽  
Static Corrections ◽  
Velocity Variations ◽  
Kirchhoff Method

Through the use of iterative turning‐ray tomography followed by wave‐equation datuming (or tomo‐datuming) and prestack depth migration, we generate accurate prestack images of seismic data in overthrust areas containing both highly variable near‐surface velocities and rough topography. In tomo‐datuming, we downward continue shot records from the topography to a horizontal datum using velocities estimated from tomography. Turning‐ray tomography often provides a more accurate near‐surface velocity model than that from refraction statics. The main advantage of tomo‐datuming over tomo‐statics (tomography plus static corrections) or refraction statics is that instead of applying a vertical time‐shift to the data, tomo‐datuming propagates the recorded wavefield to the new datum. We find that tomo‐datuming better reconstructs diffractions and reflections, subsequently providing better images after migration. In the datuming process, we use a recursive finite‐difference (FD) scheme to extrapolate wavefield without applying the imaging condition, such that lateral velocity variations can be handled properly and approximations in traveltime calculations associated with the raypath distortions near the surface for migration are avoided. We follow the downward continuation step with a conventional Kirchhoff prestack depth migration. This results in better images than those migrated from the topography using the conventional Kirchhoff method with traveltime calculation in the complicated near surface. Since FD datuming is only applied to the shallow part of the section, its cost is much less than the whole volume FD migration. This is attractive because (1) prestack depth migration usually is used iteratively to build a velocity model, so both efficiency and accuracy are important factors to be considered; and (2) tomo‐datuming can improve the signal‐to‐noise (S/N) ratio of prestack gathers, leading to more accurate migration velocity analysis and better images after depth migration. Case studies with synthetic and field data examples show that tomo‐datuming is especially helpful when strong lateral velocity variations are present below the topography.

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