Source Wavelet Estimation in Full Waveform Inversion

Sun, D.; Jiang, Kun; Vigh, Denes; Coates, R.

doi:10.3997/2214-4609.20140708

Cited by 9 publications

(4 citation statements)

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“…The 1.5D scheme described, however, is easy to handle on several computing platforms, including clusters where embarrassing parallelization can be easily implemented. Some of the attractive traits of frequency‐domain solution schemes also relate to a natural implementation of multiscaling through frequency, the ability of working with a selected subset of the wavefield information and the relative ease on the handling of the source wavelet estimation, which represents an important component in land seismic FWI problems (Sun et al., 2014). The complex frequency inversion, as implemented with the damping parameter, enables the implementation of additional workflows where unwanted secondary arrivals difficult to handle in inversion, such as surface waves, may be effectively suppressed.…”

Section: Discussionmentioning

confidence: 99%

Ultra‐resolution surface‐consistent full waveform inversion

Colombo,

Sandoval‐Curiel,

Turkoglu

et al. 2023

Geophysical Prospecting

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Full waveform inversion for land seismic data requires the development of specific strategies for modelling the complex response associated to the near surface. Seismic wave propagation is distorted by several effects, such as topographic relief, wavefield scattering, attenuation (often frequency‐dependent) and anisotropy. The modelling of such shallow complexities is often unmanageable by parametric inversions such that data preconditioning and surface‐related corrections are required before tackling the full waveform inversion velocity modelling problem. We developed a unified framework addressing both surface‐consistent corrections and full waveform inversion by using the transmitted portion of the wavefield. The problem of surface‐consistent decomposition is recast in terms of the transmitted wavefield leading to kinematic and dynamic corrections to account for sub‐resolution wave propagation distortions occurring in the near surface weathering layer. Seismic data are deconvolved from the effects of the near surface to better represent the deeper subsurface wavefield propagation. Signal‐to‐noise enhancement is obtained after the surface‐consistent transmission preconditioning by the generation of virtual super gathers reconstructed in the midpoint‐offset sorting domain. An original scheme of 1.5D Laplace–Fourier full waveform inversion, involving 3D radiation and 1D velocity inversion, is then applied for the velocity reconstruction where the amplitude information of the seismic data is fully preserved and utilized. The unified approach of surface‐consistent transmission preconditioning and velocity modelling is demonstrated on the Society of Exploration Geophysicists Advanced Modelling arid model dataset as well as on two complex field datasets containing near surface complexities typical of arid regions. The approach provides the solution of the near surface distortions by performing data preconditioning and velocity inversions in a unified scheme. The surface‐consistent full waveform inversion approach has been utilized for large land seismic projects and represents a robust tool for supporting land seismic imaging.

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Section: Discussionmentioning

confidence: 99%

Ultra‐resolution surface‐consistent full waveform inversion

Colombo,

Sandoval‐Curiel,

Turkoglu

et al. 2023

Geophysical Prospecting

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“…The estimation of the source signature is one of the fundamental steps for the successful application of any FWI workflow (Rickett, 2013;Sun et al, 2014;Skopintseva et al, 2016).…”

Section: Data Preprocessingmentioning

confidence: 99%

Target-oriented elastic full-waveform inversion through extended-migration redatuming

Biondi

Barnier

2018

SEG Technical Program Expanded Abstracts 2018

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“…(2009) and Coats et al. (2014), but using the early arrivals instead of full waveforms. One reason is that FWI may not back‐propagate the received energy to the source position owing to acquisition geometries, whereas EWI can largely mitigate this problem.…”

Section: Introductionmentioning

confidence: 99%

A collocated inversion of sources and early arrival waveforms for credible tomograms: Synthetic and field data examples

Yu,

Li,

Hanafy

et al. 2024

Near Surface Geophysics

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Waveform inversion is theoretically a powerful tool to reconstruct subsurface structures, but a usually encountered problem is that accurate sources are very rare, causing the computation to be unstable or divergent. This challenging practical problem, although sometimes ignored and even imperceptible, can easily create discrepancies in calculated shot gathers, which will then lead to wrong residuals that will be smeared back to the gradients, hence jeopardizing the inverted tomograms. For any real dataset, every shot gather corresponds to its unique source even if some gathers can be transformed alike after data processing. To resolve this problem, we propose a collocated inversion of sources and early arrival waveforms with the two submodules executing successively. Not only can this method reconstruct a decent source wavelet that approaches the ground truth, but also it can produce credible background tomograms with optimized sources. Part of the cycle skipping problems can also be mitigated because it avoids the trial and error experiments on various sources. Numerical tests on a synthetic and a land dataset validate the effectiveness of this method. Restrictions on initial sources or starting velocity models will be relaxed, and this method can be extended to any other applications for engineering or exploration purposes.

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Source Wavelet Estimation in Full Waveform Inversion

Cited by 9 publications

References 0 publications

Ultra‐resolution surface‐consistent full waveform inversion

Ultra‐resolution surface‐consistent full waveform inversion

Target-oriented elastic full-waveform inversion through extended-migration redatuming

A collocated inversion of sources and early arrival waveforms for credible tomograms: Synthetic and field data examples

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