Full waveform inversion using envelope-based global correlation norm

Computers & Geosciences

Zhang

et al. 2019

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

A new full waveform inversion method based on shifted correlation of the envelope and its implementation based on OPENCL

Computers & Geosciences

Zhang

et al. 2019

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“…Therefore, considerable efforts have been devoted to build a promising objective function with better convexity. The objective functions using an envelope (Wu et al ., 2014; Oh and Alkhalifah, 2018), auxiliary bump functional (Bharadwaj et al ., 2016) and nonlinearly smoothed wavefield (Li et al ., 2018) show promising inversion results even in the case of a lack of low frequencies because the high‐frequency oscillations in the band‐limited seismic data are suppressed and in favour of artificially produced low‐frequency information.…”

Section: Introductionmentioning

confidence: 99%

Extended full waveform inversion with matching filter

Geophysical Prospecting

2021

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Full waveform inversion has shown its huge potentials in recovering a high‐resolution subsurface model. However, conventional full waveform inversion usually suffers from cycle skipping, resulting in an inaccurate local minimum model. Extended waveform inversion provides an effective way to mitigate cycle skipping. A matching filter between the predicted and observed data can provide an additional degree of freedom to improve the data fitting and avoid the cycle skipping. We extend the search space to treat the matching filter as an independent variable that we use to bring the compared data within a half cycle to obtain the accurate direction of velocity updates. We formulate the objective function using the penalty method by linearly combining a data‐misfit term and a penalty term. The objective function with a reasonable penalty parameter has a larger region of convergence compared to conventional full waveform inversion. We search for the optimal solution over the extended model by updating the matching filter and the velocity in a nested way. The normalization of the data can bring us an equivalent normalization to the filter and a more effective convergence. In the synthetic Marmousi model, the proposed inversion method recovers the velocity model stably and accurately starting from a linearly increasing model in the case of lack of low frequencies below 3 Hz, in which conventional full waveform inversion suffers from cycle skipping. We also use a marine field data to demonstrate the effectiveness and practicality of the proposed method.

“…As a result, people look for objective functions that can keep convexity for large model perturbations, as an alternate to the conventional L 2 norm of the difference of observed and synthetic data. As a result, a wide range of objective functions such as envelope-based (Bozdag et al 2011;Wu et al 2014;Oh & Alkhalifah 2018), deconvolution-based (Luo et al 2011;Warner & Guasch 2016;Choi & Alkhalifah 2018), optimal transport-based (Engquist & Froese 2013;Métivier et al 2016;Yang et al 2018), auxiliary bump functional (Bharadwaj et al 2016), weighted correlation-based (Van Leeuwen & Mulder 2010;Wu & Alkhalifah 2017) and so on attempt to deliver a converging model in conjunction with FWI. To expedite the convergence, Overlay of seismic traces (normalized) with and without the nonlinear step in FCT (ξ = 0.005).…”

Section: Introductionmentioning

confidence: 99%

Flux-corrected transport for full-waveform inversion

Kalita

Geophysical Journal International

2019

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Conventional full-waveform inversion (FWI) often fails to retrieve the unknown model parameters from noisy seismic data. A successful FWI implementation usually requires to follow a multistage recovery approach, starting from the retrieval of the lower model wavenumbers (tomography) to those with the higher resolutions (migration). Here, we propose a new method based on the flux-corrected transport (FCT) technique often used in computational fluid dynamics for the removal of instabilities in a shock profile. FCT involves three finite-difference steps: a transport, a diffusion and an antidiffusion process. This third step involves non-linear operators such as maximum and minimum, which are non-differentiable in a classic sense. However, since the seismic source wavelet and the corresponding wavefield are relatively smooth and continuous in nature without any strong ripples like shock waves, we exclude the non-linear step from FCT, which allows us to evaluate the novel FWI gradient efficiently. As a result, we achieve a converging FWI model by gradually reducing the diffusive fluxcorrection. We demonstrate the versatility of FCT-based FWI on a noisy synthetic data set from the Marmousi II model and a marine field data set from offshore Australia.