Thematic Set: Full waveform inversion: the next leap forward in imaging at Valhall

Sirgue, L.; Barkved, O. I.; Dellinger, Joe; Etgen, John; Albertin, Uwe; Kommedal, Jan H.

doi:10.3997/1365-2397.2010012

Cited by 300 publications

(183 citation statements)

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“…Three-dimensional FWI has yielded impressive results on marine seismic datasets, producing high resolution velocity models which can be used directly for geological interpretation or for the migration of reflection seismic data to produce detailed images (e.g. Sirgue et al 2010;Ratcliffe et al 2011;Warner et al 2013;Mispel et al 2013;Jones et al 2013;Mothi et al 2013). The vast majority of such studies have utilised seismic data recorded on either hydrophone streamers or ocean bottom cables (OBC), in relatively shallow marine environments (water depth < 1,000 m).…”

Section: Introductionmentioning

confidence: 99%

Resolving the fine-scale velocity structure of continental hyperextension at the Deep Galicia Margin using full-waveform inversion

Davy

Morgan

Minshull

et al. 2017

Geophysical Journal International

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SummaryContinental hyperextension during magma-poor rifting at the Deep Galicia Margin is characterised by a complex pattern of faulting, thin continental fault blocks, and the serpentinisation, with local exhumation, of mantle peridotites along the S-reflector, interpreted as a detachment surface. In order to understand fully the evolution of these features, it is important to image seismically the structure and to model the velocity structure to the greatest resolution possible. Travel-time tomography models have revealed the longwavelength velocity structure of this hyperextended domain, but are often insufficient to match accurately the short-wavelength structure observed in reflection seismic imaging. Here we demonstrate the application of two-dimensional (2D) time-domain acoustic full-waveform inversion to deep water seismic data collected at the Deep Galicia Margin, in order to attain a

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Section: Introductionmentioning

confidence: 99%

Resolving the fine-scale velocity structure of continental hyperextension at the Deep Galicia Margin using full-waveform inversion

Davy

Morgan

Minshull

et al. 2017

Geophysical Journal International

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“…Full-waveform inversion (FWI) is a model building technique that is increasingly being used in the industry to obtain highly accurate subsurface velocity models (Sirgue et al, 2010;Warner et al, 2013). This nonlinear optimization method requires modeling the data at the receiver positions and minimizing its difference with the observed data in the field in an iterative fashion.…”

Section: Introductionmentioning

confidence: 99%

Acoustic full-waveform inversion in an elastic world

Agudo

Silva

Warner

et al. 2016

SEG Technical Program Expanded Abstracts 2016

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Full-waveform inversion (FWI) is a technique used to obtain high-quality velocity models of the subsurface. Despite the elastic nature of the earth, the anisotropic acoustic wave equation is typically used to model wave propagation in FWI. In part, this simplification is essential for being efficient when inverting large 3D data sets, but it has the adverse effect of reducing the accuracy and resolution of the recovered P-wave velocity models, as well as a loss in potential to constrain other physical properties, such as the S-wave velocity given that amplitude information in the observed data set is not fully used. Here, we first apply conventional acoustic FWI to acoustic and elastic data generated using the same velocity model to investigate the effect of neglecting the elastic component in field data and we find that it leads to a loss in resolution and accuracy in the recovered velocity model. Then, we develop a method to mitigate elastic effects in acoustic FWI using matching filters that transform elastic data into acoustic data and find that it is applicable to marine and land data sets. Tests show that our approach is successful: The imprint of elastic effects on the recovered P-wave models is mitigated, leading to better-resolved models than those obtained after conventional acoustic FWI. Our method requires a guess of V P ∕V S and is marginally more computationally demanding than acoustic FWI, but much less so than elastic FWI.

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“…The method is able to produce dramatic improvements in depth-migrated images of conventional reflection data (Sirgue et al, 2010;Warner et al, 2013) and to produce interpretable images of physical properties directly at the reservoir . Despite these and other successes (Vigh et al, 2010;Lu et al, 2013;Mancini et al, 2015), and its widening uptake across the industry (Kapoor et al, 2013), current implementations of FWI suffer from significant shortcomings; most prominent among these is the problem of local minima in the objective function produced by cycle skipping (Virieux and Operto, 2009).…”

Section: Introductionmentioning

confidence: 99%

Adaptive waveform inversion: Theory

Warner

Guasch²

2014

SEG Technical Program Expanded Abstracts 2014

129

107

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Conventional full-waveform seismic inversion attempts to find a model of the subsurface that is able to predict observed seismic waveforms exactly; it proceeds by minimizing the difference between the observed and predicted data directly, iterating in a series of linearized steps from an assumed starting model. If this starting model is too far removed from the true model, then this approach leads to a spurious model in which the predicted data are cycle skipped with respect to the observed data. Adaptive waveform inversion (AWI) provides a new form of full-waveform inversion (FWI) that appears to be immune to the problems otherwise generated by cycle skipping. In this method, least-squares convolutional filters are designed that transform the predicted data into the observed data. The inversion problem is formulated such that the subsurface model is iteratively updated to force these Wiener filters toward zero-lag delta functions. As that is achieved, the predicted data evolve toward the observed data and the assumed model evolves toward the true model. This new method is able to invert synthetic data successfully, beginning from starting models and under conditions for which conventional FWI fails entirely. AWI has a similar computational cost to conventional FWI per iteration, and it appears to converge at a similar rate. The principal advantages of this new method are that it allows waveform inversion to begin from less-accurate starting models, does not require the presence of low frequencies in the field data, and appears to provide a better balance between the influence of refracted and reflected arrivals upon the final-velocity model. The AWI is also able to invert successfully when the assumed source wavelet is severely in error.

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Thematic Set: Full waveform inversion: the next leap forward in imaging at Valhall

Cited by 300 publications

References 0 publications

Resolving the fine-scale velocity structure of continental hyperextension at the Deep Galicia Margin using full-waveform inversion

Resolving the fine-scale velocity structure of continental hyperextension at the Deep Galicia Margin using full-waveform inversion

Acoustic full-waveform inversion in an elastic world

Adaptive waveform inversion: Theory

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