Multi-Wave and Full-Waveform Inversion in Southern Oman

Masclet, S.; Bouquard, G.; Prigent, H.

doi:10.3997/2214-4609.202010731

Cited by 4 publications

(1 citation statement)

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“…Travel time FWI solves the wave equation only for travel times and uses wave‐based propagators instead than rays to help mitigating the problem of cycle skipping and the need of low‐frequency data (Brittan & Jones, 2019). Progressive implementations may then include wave‐equation travel time inversion (Luo & Schuster, 1991; Reta‐Tang et al., 2023) based on a phase cross‐correlation objective function, elastic FWI (Leblanc et al., 2022), including surface waves dispersion curves in the FWI objective function (Masclet et al., 2021), and reflection FWI (Reta‐Tang et al., 2023; Warner et al., 2023), to cite a few possible workflows.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Ultra‐resolution surface‐consistent full waveform inversion

Colombo,

Sandoval‐Curiel,

Turkoglu

et al. 2023

Geophysical Prospecting

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Elastic FWI application to a land data set in the Middle East

Sedova

Leblanc

Allemand

et al. 2022

Second International Meeting for Applied Geoscience &Amp; Energy

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Multiwave inversion: A key step for depth model building — Examples from the Sultanate of Oman

Donno¹,

Farooqui²,

Khalil³

et al. 2021

The Leading Edge

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The near surface in the Middle East, particularly in the Sultanate of Oman, is characterized by very shallow high-velocity carbonates and anhydrites interleaved by slow-velocity clastic layers, resulting in sharp velocity inversions in the first few hundred meters below the surface. In addition, the surface is characterized by features such as unconsolidated materials within dry riverbeds (known as “wadis”), small jebels, and sand dunes, which cause distortions in the underlying shallow and deeper seismic images. This work presents the building of a near-surface model by using multiwave inversion that jointly inverts information from P-wave first breaks and surface-wave dispersion curves. The use of surface waves in combination with first breaks captures the lateral and vertical velocity variations, especially in the shallowest parts of the near surface. This paper focuses on the analysis of two drawbacks of this technology: the picking of the input data information, which can be cumbersome and time consuming, and the limited penetration depth of surface waves at the typical frequencies of active data. To overcome these issues, an innovative workflow is proposed that combines the use of an unsupervised machine learning technique to guide the pick extraction phase and the reconstruction of ultra-low-frequency surface waves (0.5 to 1.5 Hz) through an interferometry process using information from natural and ambient noise. Deeper near-surface P- and S-wave velocity models can be obtained with multiwave inversion using these ultra-low frequencies. The integration of a near-surface model into the velocity model building workflow brings a major improvement in depth imaging from shallow to deep structures, as demonstrated on two data sets from the Sultanate of Oman.

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Multi-Wave and Full-Waveform Inversion in Southern Oman

Cited by 4 publications

References 0 publications

Ultra‐resolution surface‐consistent full waveform inversion

Ultra‐resolution surface‐consistent full waveform inversion

Elastic FWI application to a land data set in the Middle East

Multiwave inversion: A key step for depth model building — Examples from the Sultanate of Oman

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