Acoustic Land Full Waveform Inversion on a Broadband Land Dataset: the Impact of Optimal Transport

Sedova, A.; Messud, Jérémie; Prigent, H.; Masclet, S.; Royle, G.; Lambaré, Gilles

doi:10.3997/2214-4609.201901568

Cited by 9 publications

(19 citation statements)

References 1 publication

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“…17 compares 2D KR FWI to 1D KR FWI on the same survey. As a careful denoising of the data has been performed before FWI, following the workflow proposed by Sedova et al (2019), the 1D KR adjoint-source looks quite similar to the 2D KR adjoint-source (not shown here). Nevertheless, 2D KR FWI leads to a better model update than 1D KR FWI, with better structural consistency and well matching.…”

Section: Land Datamentioning

confidence: 85%

“…Successful applications of KR FWI on large scale field data have been published in (Poncet et al, 2018;Messud and Sedova, 2019;Sedova et al, 2019;Hermant et al, 2019;Carotti et al, 2020;Hermant et al, 2020). In this section, we gather few illustrations from Messud and Sedova (2019) and Carotti et al (2020).…”

Section: Application To Field Datamentioning

confidence: 96%

“…This data set was processed to enhance diving and post-critical waves. The FWI has been run with the frequency increasing from 2 Hz to 16 Hz, following the workflow proposed by Sedova et al (2019). Fig.…”

Section: Land Datamentioning

confidence: 99%

“…Recently, this approach led to numerous successful industrial FWI applications, see e.g. Poncet et al (2018); Messud and Sedova (2019); Sedova et al (2019); Hermant et al (2019); Carotti et al (2020); Hermant et al (2020). Compared to LSQ FWI, an interesting reduction in sensitivity to cycle-skipping has been observed, together with an improved structural consistency in inverted subsurface models.…”

Section: Introductionmentioning

confidence: 99%

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Optimal transport in full-waveform inversion: Analysis and practice of the multidimensional Kantorovich-Rubinstein norm

Messud,

Poncet,

Lambaré

2021

Preprint

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In the last ten years, full-waveform inversion has emerged as a robust and efficient high-resolution velocity model-building tool for seismic imaging, with the unique ability to recover complex subsurface structures. Originally based on a data fitting process using a least-squares cost function, it suffered from high sensitivity to cycle-skipping and was therefore of poor efficiency in handling large time shifts between observed and modelled seismic events. To tackle this problem, a common practice is to start the inversion using the low temporal frequencies of the data and selecting diving wave events. Complementary to this, the use of other cost functions has been investigated. Among these, cost functions based on optimal transport appeared appealing to possibly handle large time shifts between seismic events. Several strategies inspired by optimal transport have been proposed, taking into account the specificities of seismic data. Among them, the approach based on the Kantorovich-Rubinstein norm offers the possibility of the direct use of seismic data and an efficient numerical implementation allowing for a multidimensional (data coordinate space) application.We present here an analysis of the Kantorovich-Rubinstein norm, discussing its theoretical and practical aspects. A key component of our analysis is the back-propagated adjoint-source. We highlight its piecewise linearity, analyze its frequency content and amplitude balancing. We also emphasize the benefit of having a multidimensional implementation. Furthermore, we give practical rules for setting the tuning parameters of the numerical implementation. Our set of synthetic and field data examples demonstrate the improvements brought by the use of the Kantorovich-Rubinstein norm over least-squares full-waveform inversion, and highlight the improvements brought by the multidimensional approach over the one-dimensional one.

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Section: Land Datamentioning

confidence: 85%

Section: Application To Field Datamentioning

confidence: 96%

Section: Land Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal transport in full-waveform inversion: Analysis and practice of the multidimensional Kantorovich-Rubinstein norm

Messud,

Poncet,

Lambaré

2021

Preprint

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“…One solution is to rely on a specific optimal transport distance, which can be extended to comparing non-positive data. This is the Kantorovich-Rubinstein optimal transport (KROT) approach, which has been promoted in Métivier Sedova et al, 2019). One interest of this approach is its ability to account for lateral coherency in 2D or 3D shot gathers.…”

Section: Introductionmentioning

confidence: 99%

On cycle-skipping and misfit function modification for full-wave inversion: Comparison of five recent approaches

Pladys

Brossier

et al. 2021

GEOPHYSICS

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Full waveform inversion, a high-resolution seismic imaging method, is known to require sufficiently accurate initial models to converge toward meaningful estimations of the subsurface mechanical properties. This limitation is due to the non-convexity of the least-squares distance with respect to kinematic mismatch. We propose a comparison of five misfit functions promoted recently to mitigate this issue: adaptive waveform inversion, instantaneous envelope, normalized integration, and two methods based on optimal transport. We explain which principles these methods are based on and illustrate how they are designed to better handle kinematic mismatch than a least-squares misfit function. By doing so, we can exhibit specific limitations of these methods in canonical cases. We further assess the interest of these five approaches for application to field data based on a synthetic Marmousi case study. We illustrate how adaptive waveform inversion and the two methods based on optimal transport possess interesting properties, making them appealing strategies applicable to field data. Another outcome is the definition of generic tools to compare misfit functions for full-waveform inversion.

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Graph‐Space Optimal Transport Concept for Time‐Domain Full‐Waveform Inversion of Ocean‐Bottom Seismometer Data: Nankai Trough Velocity Structure Reconstructed From a 1D Model

2021

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Full-waveform inversion (FWI) has proven to be a powerful tool able to reconstruct high-resolution velocity models of the subsurface. Rapid development of this technology during past decades has led to its successful application in imaging of various geological targets, from near-surface to global-scale endpoints (see Tromp, 2019 for a recent review). In particular, the oil & gas industry (stimulated by the hydrocarbon market) is currently routinely applying FWI at the exploration scale for high-resolution imaging of reservoirs (

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Acoustic Land Full Waveform Inversion on a Broadband Land Dataset: the Impact of Optimal Transport

Cited by 9 publications

References 1 publication

Optimal transport in full-waveform inversion: Analysis and practice of the multidimensional Kantorovich-Rubinstein norm

Optimal transport in full-waveform inversion: Analysis and practice of the multidimensional Kantorovich-Rubinstein norm

On cycle-skipping and misfit function modification for full-wave inversion: Comparison of five recent approaches

Graph‐Space Optimal Transport Concept for Time‐Domain Full‐Waveform Inversion of Ocean‐Bottom Seismometer Data: Nankai Trough Velocity Structure Reconstructed From a 1D Model

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