Multiscale seismic waveform inversion

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1457-1473 ◽  
Author(s):  
Carey Bunks ◽  
Fatimetou M. Saleck ◽  
S. Zaleski ◽  
G. Chavent
Keyword(s):  
Iterative Methods ◽  
Global Minimum ◽  
Multigrid Method ◽  
Waveform Inversion ◽  
Seismic Inversion ◽  
Local Minima ◽  
Data Set ◽  
Seismic Waveform ◽  
Inversion Methods ◽  
Iterative Inversion

Iterative inversion methods have been unsuccessful at inverting seismic data obtained from complicated earth models (e.g. the Marmousi model), the primary difficulty being the presence of numerous local minima in the objective function. The presence of local minima at all scales in the seismic inversion problem prevent iterative methods of inversion from attaining a reasonable degree of convergence to the neighborhood of the global minimum. The multigrid method is a technique that improves the performance of iterative inversion by decomposing the problem by scale. At long scales there are fewer local minima and those that remain are further apart from each other. Thus, at long scales iterative methods can get closer to the neighborhood of the global minimum. We apply the multigrid method to a subsampled, low‐frequency version of the Marmousi data set. Although issues of source estimation, source bandwidth, and noise are not treated, results show that iterative inversion methods perform much better when employed with a decomposition by scale. Furthermore, the method greatly reduces the computational burden of the inversion that will be of importance for 3-D extensions to the method.

A homotopy method for well-log constraint waveform inversion

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. R1-R7 ◽  
Author(s):  
Bo Han ◽  
Hongsun Fu ◽  
Hong Liu
Keyword(s):  
Iterative Methods ◽  
Global Minimum ◽  
Waveform Inversion ◽  
Homotopy Method ◽  
Well Log ◽  
Local Minima ◽  
Acoustic Wave Equation ◽  
Low Frequencies ◽  
True Model ◽  
Starting Model

The primary difficulty posed by the method of waveform inversion for the acoustic-wave equation is the presence of local minima of the objective function. Waveform inversion fails to converge to the global minimum unless the wavefield contains very low frequencies, or the starting model is very close to the true model. Constraints can improve the convergence of the method; however, if the starting model is far from the correct model, normal iterative methods will still get trapped in local minima. We designed a homotopy method to improve the robustness of waveform inversion; it makes natural use of constraints, such as well logs. In addition, to further condition the inverse problem, we incorporate standard Tikhonov regularization. We demonstrate through a synthetic example that our method is more likely to find a global minimum than normal iterative methods.

Data-driven Prestack Waveform Inversion Using Genetic Algorithm- Methodology and Examples

2021 ◽  
pp. 1-97
Author(s):  
Lingxiao Jia ◽  
Subhashis Mallick ◽  
Cheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Seismic Inversion ◽  
P Wave ◽  
Data Driven ◽  
Initial Model ◽  
Reservoir Properties ◽  
Well Control ◽  
Seismic Waveform

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model, that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizonal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudo wells by running a two-pass one-dimensional isotropic elastic prestack waveform inversion that uses the reflectivity method for forward modeling and genetic algorithm for optimization. We then use the estimated pseudo wells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geological settings, we demonstrate the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is however necessary to improve the compute-efficiency of the methodology.

Prestack waveform inversion of three-dimensional seismic data — An example from the Rock Springs Uplift, Wyoming, USA

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. B1-B12 ◽  
Author(s):  
Josiane Pafeng ◽  
Subhashis Mallick ◽  
Hema Sharma
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Waveform Inversion ◽  
Three Dimensional ◽  
Seismic Inversion ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Seismic Waveform ◽  
Rock Springs Uplift ◽  
Rock Springs

Applying seismic inversion to estimate subsurface elastic earth properties for reservoir characterization is a challenge in exploration seismology. In recent years, waveform-based seismic inversions have gained popularity, but due to high computational costs, their applications are limited, and amplitude-variation-with-offset/angle inversion is still the current state-of-the-art. We have developed a genetic-algorithm-based prestack seismic waveform inversion methodology. By parallelizing at multiple levels and assuming a locally 1D structure such that forward computation of wave equation synthetics is computationally efficient, this method is capable of inverting 3D prestack seismic data on parallel computers. Applying this inversion to a real prestack seismic data volume from the Rock Springs Uplift (RSU) located in Wyoming, USA, we determined that our method is capable of inverting the data in a reasonable runtime and producing much higher quality results than amplitude-variation-with-offset/angle inversion. Because the primary purpose for seismic data acquisition at the RSU was to characterize the subsurface for potential targets for carbon dioxide sequestration, we also identified and analyzed some potential primary and secondary storage formations and their associated sealing lithologies from our inversion results.

scholarly journals 2-D ocean temperature and salinity images from pre-stack seismic waveform inversion methods: an example from the South China Sea

2015 ◽  
Vol 202 (2) ◽  
pp. 800-810 ◽  
Author(s):  
Amit Padhi ◽  
Subhashis Mallick ◽  
Will Fortin ◽  
W. Steven Holbrook ◽  
Tanya M. Blacic
Keyword(s):  
South China Sea ◽  
South China ◽  
Waveform Inversion ◽  
The South China Sea ◽  
Ocean Temperature ◽  
The South ◽  
China Sea ◽  
Seismic Waveform ◽  
Inversion Methods

Sinkhole detection with 3D full seismic waveform tomography

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. B169-B179
Author(s):  
Majid Mirzanejad ◽  
Khiem T. Tran ◽  
Michael McVay ◽  
David Horhota ◽  
Scott J. Wasman
Keyword(s):  
Wave Equations ◽  
Waveform Inversion ◽  
Field Tests ◽  
Waveform Analysis ◽  
Full Waveform Inversion ◽  
3D Seismic ◽  
Data Set ◽  
Full Waveform ◽  
Seismic Waveform ◽  
Elastic Wave Equations

Sinkhole collapse may result in significant property damage and even loss of life. Early detection of sinkhole attributes (buried voids, raveling zones) is critical to limit the cost of remediation. One of the most promising ways to obtain subsurface imaging is 3D seismic full-waveform inversion. For demonstration, a recently developed 3D Gauss-Newton full-waveform inversion (3D GN-FWI) method is used to detect buried voids, raveling soils, and characterize variable subsurface soil/rock layering. It is based on a finite-difference solution of 3D elastic wave equations and Gauss-Newton optimization. The method is tested first on a data set constructed from the numerical simulation of a challenging synthetic model and subsequently on field data collected from two separate test sites in Florida. For the field tests, receivers and sources are placed in uniform 2D surface grids to acquire the seismic wavefields, which then are inverted to extract the 3D subsurface velocity structures. The inverted synthetic results suggest that the approach is viable for detecting voids and characterizing layering. The field seismic results reveal that the 3D waveform analysis identified a known manmade void (plastic culvert), unknown natural voids, raveling, as well as laterally variable soil/rock layering including rock pinnacles. The results are confirmed later by standard penetration tests, including depth to bedrock, two buried voids, and a raveling soil zone. Our study provides insight into the application of the 3D seismic FWI technique as a powerful tool in detecting shallow voids and other localized subsurface features.

Ensemble-based seismic inversion for a stratified medium

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. R29-R39 ◽  
Author(s):  
Michael Gineste ◽  
Jo Eidsvik ◽  
York Zheng
Keyword(s):  
Waveform Inversion ◽  
Seismic Inversion ◽  
Stratified Medium ◽  
Elastic Parameters ◽  
Waveform Data ◽  
Optimization Task ◽  
Estimation Uncertainty ◽  
Seismic Waveform ◽  
Probabilistic Setting ◽  
Iterative Ensemble Smoother

Seismic waveform inversion is a nontrivial optimization task, which is often complicated by the nonlinear relationship between the elastic attributes of interest and the large amount of data obtained in seismic experiments. Quantifying the solution uncertainty can be even more challenging, and it requires considering the problem in a probabilistic setting. Consequently, the seismic inverse problem is placed in a Bayesian framework, using a sequential filtering approach to invert for the elastic parameters. The method uses an iterative ensemble smoother to estimate the subsurface parameters, and from the ensemble, a notion of estimation uncertainty is readily available. The ensemble implicitly linearizes the relation between the parameters and the observed waveform data; hence, it requires no tangent linear model. The approach is based on sequential conditioning on partitions of the whole data record (1) to regularize the inversion path and effectively drive the estimation process in a top-down manner and (2) to circumvent a consequence of the ensemble reduced rank approximation. The method is exemplified on a synthetic case, inverting for elastic parameters in a 1D medium using a seismic shot record. Our results indicate that the iterative ensemble method is applicable to seismic waveform inversion and that the ensemble representation indeed indicates estimation uncertainty.

Frequency and spatial sampling strategies for crosshole seismic waveform spectral inversion experiments

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCC79-WCC89 ◽  
Author(s):  
Hansruedi Maurer ◽  
Stewart Greenhalgh ◽  
Sabine Latzel
Keyword(s):  
Information Content ◽  
Seismic Data ◽  
Velocity Model ◽  
Similar Amount ◽  
Data Sets ◽  
Data Set ◽  
Waveform Data ◽  
Velocity Models ◽  
Seismic Waveform ◽  
Iterative Inversion

Analyses of synthetic frequency-domain acoustic waveform data provide new insights into the design and imaging capability of crosshole surveys. The full complex Fourier spectral data offer significantly more information than other data representations such as the amplitude, phase, or Hartley spectrum. Extensive eigenvalue analyses are used for further inspection of the information content offered by the seismic data. The goodness of different experimental configurations is investigated by varying the choice of (1) the frequencies, (2) the source and receiver spacings along the boreholes, and (3) the borehole separation. With only a few carefully chosen frequencies, a similar amount of information can be extracted from the seismic data as can be extracted with a much larger suite of equally spaced frequencies. Optimized data sets should include at least one very low frequencycomponent. The remaining frequencies should be chosen fromthe upper end of the spectrum available. This strategy proved to be applicable to a simple homogeneous and a very complex velocity model. Further tests are required, but it appears on the available evidence to be model independent. Source and receiver spacings also have an effect on the goodness of an experimental setup, but there are only minor benefits to denser sampling when the increment is much smaller than the shortest wavelength included in a data set. If the borehole separation becomes unfavorably large, the information content of the data is degraded, even when many frequencies and small source and receiver spacings are considered. The findings are based on eigenvalue analyses using the true velocity models. Because under realistic conditions the true model is not known, it is shown that the optimized data sets are sufficiently robust to allow the iterative inversion schemes to converge to the global minimum. This is demonstrated by means of tomographic inversions of several optimized data sets.

scholarly journals Seismic shot-encoding schemes for waveform inversion

2020 ◽  
Vol 17 (5) ◽  
pp. 906-913 ◽  
Author(s):  
Edwin Fagua Duarte ◽  
Carlos A N da Costa ◽  
João M de Araújo ◽  
Yanghua Wang ◽  
Ying Rao
Keyword(s):  
Waveform Inversion ◽  
Random Phase ◽  
Computational Cost ◽  
Velocity Model ◽  
Dominant Frequency ◽  
Seismic Waveform ◽  
Rotation Scheme ◽  
Iterative Inversion ◽  
Inversion Procedure ◽  
The Time Domain

Abstract A shot-encoding technique can be used in seismic waveform inversion to significantly reduce the computational cost by reducing the number of seismic simulations in the inversion procedure. Here we developed two alternative shot-encoding schemes to perform simultaneous-sources waveform inversion. The first scheme (I) encodes shot gathers with random-phase rotations applied to seismic traces. The second scheme (II) encodes shot gathers with random static time shifts. The well-known polarity encoding scheme (III) is just a special case of the random-phase rotation scheme. The second scheme is a variation of the conventional static shift encoding (IV), but the static time shifts in the second scheme are limited to one period of the dominant frequency. All encoded shot gathers are added up into a single super-shot gather for seismic waveform inversion. We perform the time-domain waveform inversion, using these shot-encoding schemes in conjunction with a restarted L-BFGS algorithm in the iterative inversion. The effectiveness and efficiency analyses demonstrate that the two shot-encoding schemes (I and II) proposed in this paper may improve the convergence of the iterative inversion, reduce the crosstalk effect among shots and consequently produce a subsurface velocity model with a high resolution.

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