Application of the finite-difference contrast-source inversion algorithm to seismic full-waveform data

Abubakar, Aria; Hu, Wenyi; Habashy, Tarek M.; Berg, Peter

doi:10.1190/1.3250203

Cited by 77 publications

(53 citation statements)

References 46 publications

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“…The source compression would provide a linear speedup for all non-linear inversion methods such as the (preconditioned) nonlinear conjugate gradient (CG) method (see Pratt (1999); Shin et al (2001); Operto et al (2007); Vigh and Starr (2008); Hu et al (2009a)), the contrast source inversion (CSI) method (see Abubakar et al (2003Abubakar et al ( , 2009)) or the Gauss-Newton method (see Pratt et al (1998);Hu et al (2009b)). The speedup factor would be of the order of N S /L.…”

Section: Theorymentioning

confidence: 99%

“…In the last decades, developments in the numerical computation techniques have motivated research on the full-waveform inversion (FWI) approaches for geophysical applications (Pratt et al (1998); Shin et al (2001); Abubakar et al (2003); Operto et al (2007); Vigh and Starr (2008); Hu et al (2009b); Abubakar et al (2009)). In FWI seismic inversion, one usually deals with a large size data set.…”

Section: Introductionmentioning

confidence: 99%

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Seismic full-waveform inversion using a compressed simultaneous sources and receivers scheme

2011

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We apply the source-receiver compression approach for reducing the computational time and memory usage of the seismic (acoustic) full-waveform inversions. By detecting and quantifying the extent of redundancy in the data, we assemble a reduced set of simultaneous sources and receivers that are weighted sums of the physical sources and receivers employed in the survey. Because the number of these simultaneous sources and receivers can be significantly less than those of the physical sources and receivers, the computational time and memory usage of an inversion method can be tremendously reduced. The scheme is based on decomposing the data into their principal components using a singular-value decomposition approach and the data reduction is done through the elimination of the small eigenvalues. Consequently this will suppress the effect of noise in the data. Moreover, taking advantage of the redundancy in the data, this compression scheme effectively stacks the redundant data resulting in an increased signal-to-noise ratio. We present inversion results for the Marmousi model for surface measurements. We show that this approach has the potential of significantly reducing both computational time and memory usage of the Gauss-Newton method by 1-2 orders of magnitude.

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Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seismic full-waveform inversion using a compressed simultaneous sources and receivers scheme

2011

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“…To reduce the computational cost, Abubakar et al . () introduced the so‐called contrast source inversion (CSI) method, where the need to perform a full forward simulation at each iteration is eliminated. A drawback of the CSI method is that its convergence properties for strongly scattering media are sometimes unsatisfactory and difficult to analyse (Wiik et al .…”

Section: Introductionmentioning

confidence: 99%

Accelerating the T‐matrix approach to seismic full‐waveform inversion by domain decomposition

Jakobsen

2018

Geophysical Prospecting

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A seismic variant of the distorted Born iterative inversion method, which is commonly used in electromagnetic and acoustic (medical) imaging, has been recently developed on the basis of the T‐matrix approach of multiple scattering theory. The distorted Born iterative method is consistent with the Gauss–Newton method, but its implementation is different, and there are potentially significant computational advantages of using the T‐matrix approach in this context. It has been shown that the computational cost associated with the updating of the background medium Green functions after each iteration can be reduced via the use of various linearisation or quasi‐linearisation techniques. However, these techniques for reducing the computational cost may not work well in the presence of strong contrasts. To deal with this, we have now developed a domain decomposition method, which allows one to decompose the seismic velocity model into an arbitrary number of heterogeneous domains that can be treated separately and in parallel. The new domain decomposition method is based on the concept of a scattering‐path matrix, which is well known in solid‐state physics. If the seismic model consists of different domains that are well separated (e.g., different reservoirs within a sedimentary basin), then the scattering‐path matrix formulation can be used to derive approximations that are sufficiently accurate but far more speedy and much less memory demanding because they ignore the interaction between different domains. However, we show here that one can also use the scattering‐path matrix formulation to calculate the overall T‐matrix for a large model exactly without any approximations at a computational cost that is significantly smaller than the cost associated with an exact formal matrix inversion solution. This is because we have derived exact analytical results for the special case of two interacting domains and combined them with Strassen's formulas for fast recursive matrix inversion. To illustrate the fact that we have accelerated the T‐matrix approach to full‐waveform inversion by domain decomposition, we perform a series of numerical experiments based on synthetic data associated with a complex salt model and a simpler two‐dimensional model that can be naturally decomposed into separate upper and lower domains. If the domain decomposition method is combined with an additional layer of multi‐scale regularisation (based on spatial smoothing of the sensitivity matrix and the data residual vector along the receiver line) beyond standard sequential frequency inversion, then one apparently can also obtain stable inversion results in the absence of ultra‐low frequencies and reduced computation times.

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“…There is already a wealth of publications on this approach in the seismic and electromagnetic literature. See for instance Zhdanov, 2002;Abubakar et al, 2009;Gisolf and Verschuur, 2010. At each gridpoint, the contrast acts as a point scatterer in the background medium, generally referred to as a "contrast source" (see above references).…”

Section: S46mentioning

confidence: 99%

“…(Figure 4). It is important to realize that this inversion process is very elaborate as it solves a volume integral equation of the second kind (Zhdanov, 2002;Abubakar et al, 2009). Later in this paper, I will propose a recursive alternative, referred to as WFI, that is based on solving the integral equation per depth layer.…”

Section: S46mentioning

confidence: 99%

Combining full wavefield migration and full waveform inversion, a glance into the future of seismic imaging

Berkhout

2012

GEOPHYSICS

125

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The next generation seismic migration and inversion technology considers multiple scattering as vital information, allowing the industry to derive significantly better reservoir models -with more detail and less uncertainty -while requiring a minimum of user intervention. Three new insights have been uncovered with respect to this fundamental transition. Unblended or blended multiple scattering can be included in the seismic migration process, and it has been proposed to formulate the imaging principle as a minimization problem. The resulting process yields angle-dependent reflectivity and is referred to as recursive full wavefield migration (WFM). The full waveform inversion process for velocity estimation can be extended to a recursive, optionally blended, anisotropic multiple-scattering algorithm. The resulting process yields angle-dependent velocity and is referred to as recursive full waveform inversion (WFI). The mathematical equations of WFM and WFI have an identical structure, but the physical meaning behind the expressions is fundamentally different. In WFM the reflection process is central, and the aim is to estimate reflection operators of the subsurface, using the up-and downgoing incident wavefields (including the codas) in each gridpoint. In WFI, however, the propagation process is central and the aim is to estimate velocity operators of the subsurface, using the total incident wavefield (sum of up-and downgoing) in each gridpoint. Angle-dependent reflectivity in WFM corresponds with angle-dependent velocity (anisotropy) in WFI. The algorithms of WFM and WFI could be joined into one automated joint migration-inversion process. In the resulting hybrid algorithm, being referred to as recursive joint migration inversion (JMI), the elaborate volume integral solution was replaced by an efficient alternative: WFM and WFI are alternately applied at each depth level, where WFM extrapolates the incident wavefields and WFI updates the velocities without any user interaction. The output of the JMI process offers an integrated picture of the subsurface in terms of angle-dependent reflectivity as well as anisotropic velocity. This two-fold output, reflectivity image and velocity model, offers new opportunities to extract accurate rock and pore properties at a fine reservoir scale.

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Application of the finite-difference contrast-source inversion algorithm to seismic full-waveform data

Cited by 77 publications

References 46 publications

Seismic full-waveform inversion using a compressed simultaneous sources and receivers scheme

Seismic full-waveform inversion using a compressed simultaneous sources and receivers scheme

Accelerating the T‐matrix approach to seismic full‐waveform inversion by domain decomposition

Combining full wavefield migration and full waveform inversion, a glance into the future of seismic imaging

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