Near-surface S-wave velocity and attenuation structure from full-waveform inversion of distributed acoustic sensing data

Author(s):  
Luping Qu ◽  
Wenyong Pan ◽  
Kristopher A. Innanen ◽  
Jan Dettmer ◽  
Marie Macquet ◽  
...  
Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. B95-B105 ◽  
Author(s):  
Yao Wang ◽  
Richard D. Miller ◽  
Shelby L. Peterie ◽  
Steven D. Sloan ◽  
Mark L. Moran ◽  
...  

We have applied time domain 2D full-waveform inversion (FWI) to detect a known 10 m deep wood-framed tunnel at Yuma Proving Ground, Arizona. The acquired seismic data consist of a series of 2D survey lines that are perpendicular to the long axis of the tunnel. With the use of an initial model estimated from surface wave methods, a void-detection-oriented FWI workflow was applied. A straightforward [Formula: see text] quotient masking method was used to reduce the inversion artifacts and improve confidence in identifying anomalies that possess a high [Formula: see text] ratio. Using near-surface FWI, [Formula: see text] and [Formula: see text] velocity profiles were obtained with void anomalies that are easily interpreted. The inverted velocity profiles depict the tunnel as a low-velocity anomaly at the correct location and depth. A comparison of the observed and simulated waveforms demonstrates the reliability of inverted models. Because the known tunnel has a uniform shape and for our purposes an infinite length, we apply 1D interpolation to the inverted [Formula: see text] profiles to generate a pseudo 3D (2.5D) volume. Based on this research, we conclude the following: (1) FWI is effective in near-surface tunnel detection when high resolution is necessary. (2) Surface-wave methods can provide accurate initial S-wave velocity [Formula: see text] models for near-surface 2D FWI.


Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Dmitry Borisov ◽  
Ryan Modrak ◽  
Fuchun Gao ◽  
Jeroen Tromp

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.


Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. R109-R117 ◽  
Author(s):  
Lisa Groos ◽  
Martin Schäfer ◽  
Thomas Forbriger ◽  
Thomas Bohlen

The S-wave velocity of the shallow subsurface can be inferred from shallow-seismic Rayleigh waves. Traditionally, the dispersion curves of the Rayleigh waves are inverted to obtain the (local) S-wave velocity as a function of depth. Two-dimensional elastic full-waveform inversion (FWI) has the potential to also infer lateral variations. We have developed a novel workflow for the application of 2D elastic FWI to recorded surface waves. During the preprocessing, we apply a line-source simulation (spreading correction) and perform an a priori estimation of the attenuation of waves. The iterative multiscale 2D elastic FWI workflow consists of the preconditioning of the gradients in the vicinity of the sources and a source-wavelet correction. The misfit is defined by the least-squares norm of normalized wavefields. We apply our workflow to a field data set that has been acquired on a predominantly depth-dependent velocity structure, and we compare the reconstructed S-wave velocity model with the result obtained by a 1D inversion based on wavefield spectra (Fourier-Bessel expansion coefficients). The 2D S-wave velocity model obtained by FWI shows an overall depth dependency that agrees well with the 1D inversion result. Both models can explain the main characteristics of the recorded seismograms. The small lateral variations in S-wave velocity introduced by FWI additionally explain the lateral changes of the recorded Rayleigh waves. The comparison thus verifies the applicability of our 2D FWI workflow and confirms the potential of FWI to reconstruct shallow small-scale lateral changes of S-wave velocity.


Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. R247-R259 ◽  
Author(s):  
Yuwei Wang ◽  
Liangguo Dong ◽  
Yuzhu Liu ◽  
Jizhong Yang

Elastic full-waveform inversion (EFWI) of multicomponent seismic data is a powerful tool for estimating the subsurface elastic parameters with high accuracy. However, the trade-offs between multiple parameters increase the nonlinearity of EFWI. Although the conventional diagonal-approximate Hessian matrix describes the illumination and limited bandwidth effects, it ignores the trade-off effects and decreases the convergence rate of EFWI. We have developed a block-diagonal pseudo-Hessian operator for 2D frequency-domain EFWI to take into account the approximate trade-offs among the P-wave (compressional-wave) velocity, S-wave (shear-wave) velocity, and density without extra computational costs on forward simulations. The Hessian matrix tends toward a block-diagonal matrix as the frequency grows to infinity; thus, the proposed block-diagonal pseudo-Hessian matrix is more accurate at higher frequencies. The inverse of the block-diagonal pseudo-Hessian matrix is used as a preconditioner for the nonlinear conjugate-gradient method to simultaneously reconstruct P- and S-wave velocities and density. This approach effectively mitigates the crosstalk artifacts by correcting the gradients from the trade-off effects and produces more rapid inversion convergence, which becomes more significant at higher frequencies. Synthetic experiments on an inclusion model and the elastic Marmousi2 model demonstrate its feasibility and validity in EFWI.


Geophysics ◽  
2021 ◽  
pp. 1-65
Author(s):  
Rebekka Mecking ◽  
Daniel Köhn ◽  
Matthias Meinecke ◽  
Wolfgang Rabbel

The detection of cavities with geophysical methods is a challenging task for which a general approach has not yet been found. We show that viscoelastic SH full waveform inversion (FWI), focusing primarily on reflection events, is able to accurately locate the position of cavities, areas of decompacted sediments and, more generally, seismic low-velocity anomalies down to 30 m depth. The key for a successful FWI application is the enhancement of the reflected wavefield relative to the surface wavefield. For this purpose, we applied automatic gain control normalization in the objective function. By focusing the inversion on the reflected wavefield, we demonstrate that one can differentiate between air-filled cavities with zero shear-wave velocity and low-velocity zones. Additionally, we test the FWI approach on a field dataset, with a known collapsed tunnel system inside a 32 m high, monumental, antique grave mound. The results show that the location and extent, as well as density and S-wave velocity of the collapsed tunnel system, can be determined with sufficient accuracy by applying a 2D FWI approach to intersecting profiles, despite the 3D nature of the problem.


Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R185-R206 ◽  
Author(s):  
Wenyong Pan ◽  
Kristopher A. Innanen ◽  
Yu Geng ◽  
Junxiao Li

Simultaneous determination of multiple physical parameters using full-waveform inversion (FWI) suffers from interparameter trade-off difficulties. Analyzing the interparameter trade-offs in different model parameterizations of isotropic-elastic FWI, and thus determining the appropriate model parameterization, are critical for efficient inversion and obtaining reliable inverted models. Five different model parameterizations are considered and compared including velocity-density, modulus-density, impedance-density, and two velocity-impedance parameterizations. The scattering radiation patterns are first used for interparameter trade-off analysis. Furthermore, a new framework is developed to evaluate the interparameter trade-off based upon multiparameter Hessian-vector products: Multiparameter point spread functions (MPSFs) and interparameter contamination sensitivity kernels (ICSKs), which provide quantitative, second-order measurements of the interparameter contaminations. In the numerical experiments, the interparameter trade-offs in various model parameterizations are evaluated using the MPSFs and ICSKs. Inversion experiments are carried out with simple Gaussian-anomaly models and a complex Marmousi model. Overall, the parameterization of the P-wave velocity, S-wave velocity, and density, and the parameterization of the P-wave velocity, S-wave velocity, and S-wave impedance perform best for reconstructing all of the physical parameters. Isotropic-elastic FWI of the Hussar low-frequency data set with various model parameterizations verifies our conclusions.


Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. R109-R119 ◽  
Author(s):  
Timothy J. Sears ◽  
Penny J. Barton ◽  
Satish C. Singh

Elastic full waveform inversion of multichannel seismic data represents a data-driven form of analysis leading to direct quantification of the subsurface elastic parameters in the depth domain. Previous studies have focused on marine streamer data using acoustic or elastic inversion schemes for the inversion of P-wave data. In this paper, P- and S-wave velocities are inverted for using wide-angle multicomponent ocean-bottom cable (OBC) seismic data. Inversion is undertaken using a two-dimensional elastic algorithm operating in the time domain, which allows accurate modeling and inversion of the full elastic wavefield, including P- and mode-converted PS-waves and their respective amplitude variation with offset (AVO) responses. Results are presented from the application of this technique to an OBC seismic data set from the Alba Field, North Sea. After building an initial velocity model and extracting a seismic wavelet, the data are inverted instages. In the first stage, the intermediate wavelength P-wave velocity structure is recovered from the wide-angle data and then the short-scale detail from near-offset data using P-wave data on the [Formula: see text] (vertical geophone) component. In the second stage, intermediate wavelengths of S-wave velocity are inverted for, which exploits the information captured in the P-wave’s elastic AVO response. In the third stage, the earlier models are built on to invert mode-converted PS-wave events on the [Formula: see text] (horizontal geophone) component for S-wave velocity, targeting first shallow and then deeper structure. Inversion of [Formula: see text] alone has been able to delineate the Alba Field in P- and S-wave velocity, with the main field and outlier sands visible on the 2D results. Inversion of PS-wave data has demonstrated the potential of using converted waves to resolve shorter wavelength detail. Even at the low frequencies [Formula: see text] inverted here, improved spatial resolution was obtained by inverting S-wave data compared with P-wave data inversion results.


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