A VTI anisotropic media inversion method based on the exact reflection coefficient equation

Wu, Fan; Li, Jingye; Geng, Weiheng; Tang, Wei

doi:10.3389/fphy.2022.926636

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…The new parameterization is a crucial tool with different applications in cryospheric sciences. In particular, we seek to trigger new microstructure retrievals through advanced anisotropic inversion methods of seismic data (Wu et al, 2022). Along these lines, our results shed new light on the relative importance of the two different types of elastic anisotropy (crystallographic and geometrical) in snow and firn that may influence the interpretation of seismic measurements (Schlegel et al, 2019).…”

Section: Discussionmentioning

confidence: 89%

“…Thus, a parameterization of the elastic modulus based on density and geometrical anisotropy for the entire possible range of porosities would constitute a first step towards understanding this concurrent anisotropy problem. This could have immediate applications, for example, for retrieving subsurface density and anisotropy through seismics using advanced inversion methods (Wu et al, 2022). Leinss et al (2016) show that an electromagnetic inversion model could be exploited to retrieve the geometrical anisotropy of snow, despite a subdominant impact of the geometrical anisotropy on the effective permittivity tensor.…”

Section: Introductionmentioning

confidence: 99%

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A microstructure-based parameterization of the effective anisotropic elasticity tensor of snow, firn, and bubbly ice

Sundu,

Freitag,

Fourteau

et al. 2024

The Cryosphere

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Abstract. Quantifying the link between microstructure and effective elastic properties of snow, firn, and bubbly ice is essential for many applications in cryospheric sciences. The microstructure of snow and ice can be characterized by different types of fabrics (crystallographic and geometrical), which give rise to macroscopically anisotropic elastic behavior. While the impact of the crystallographic fabric has been extensively studied in deep firn, the present work investigates the influence of the geometrical fabric over the entire range of possible volume fractions. To this end, we have computed the effective elasticity tensor of snow, firn, and ice by finite-element simulations based on 391 X-ray tomography images comprising samples from the laboratory, the Alps, Greenland, and Antarctica. We employed a variant of Eshelby's tensor that has been previously utilized for the parameterization of thermal and dielectric properties of snow and utilized Hashin–Shtrikman bounds to capture the nonlinear interplay between density and geometrical anisotropy. From that we derive a closed-form parameterization for all components of the (transverse isotropic) elasticity tensor for all volume fractions using two fit parameters per tensor component. Finally, we used the Thomsen parameter to compare the geometrical anisotropy to the maximal theoretical crystallographic anisotropy in bubbly ice. While the geometrical anisotropy clearly dominates up to ice volume fractions of ϕ≈0.7, a thorough understanding of elasticity in bubbly ice may require a coupled elastic theory that includes geometrical and crystallographic anisotropy.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A microstructure-based parameterization of the effective anisotropic elasticity tensor of snow, firn, and bubbly ice

Sundu,

Freitag,

Fourteau

et al. 2024

The Cryosphere

View full text Add to dashboard Cite

show abstract

“…In particular we seek to trigger new microstructure retrievals through advanced anisotropic inversion methods of seismic data (Wu et al, 2022). Along these lines, our results shed new light on the relative importance of the two different types of elastic anisotropy (crystallographic, geometrical) in snow and firn that may influence the interpretation of seismic measurements (Schlegel et al, 2019).…”

Section: Comparison Of Geometrical and Crystallographical Anisotropymentioning

confidence: 89%

“…This could have immediate applications e.g. for retrieving sub-surface density, elasticity, and anisotropy through seismics using advanced inversion methods (Wu et al, 2022).…”

mentioning

confidence: 99%

A microstructure-based parameterization of the effective, anisotropic elasticity tensor of snow, firn, and bubbly ice

Sundu¹,

Freitag

Fourteau

et al. 2023

Preprint

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Abstract. Quantifying the link between microstructure and effective elastic properties of snow, firn, and bubbly ice is essential for many applications in cryospheric sciences. The microstructure of snow and ice can be characterized by different types of fabrics (crystallographic, geometrical) that gives rise to macroscopically anisotropic elastic behavior. While the impact of the crystallographic fabric has been extensively studied in deep firn, the present work investigates the influence of the geometrical fabric over the entire range of possible volume fractions. To this end we have computed the effective elasticity tensor of snow, firn, and ice by finite element simulations based on 395 X-ray tomography images comprising samples from the laboratory, Alps, Greenland, and Antarctica. We employed a variant of the Eshelby tensor that has been previously utilized for the parametrization of thermal and dielectric properties of snow and utilized Hashin-Shtrikman bounds to capture the nonlinear interplay between density and geometrical anisotropy. From that we derive a closed-form parametrization for all components of the (transverse isotropic) elastic tensor for all volume fractions using 2 fit parameters per tensor component. Finally we used the Thomsen parameter to compare the geometrical anisotropy to the crystallographic anisotropy in bubbly ice. While the geometrical anisotropy is clearly dominating up to ice volume fractions of Ø ≈ 0.7, a thorough understanding of elasticity in bubbly ice may require a coupled elastic theory that includes geometrical and crystallographic anisotropy.

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The AVO Effect of Formation Pressure on Time-Lapse Seismic Monitoring in Marine Carbon Dioxide Storage

Wu,

Li,

et al. 2024

J. Marine. Sci. Appl.

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A VTI anisotropic media inversion method based on the exact reflection coefficient equation

Cited by 4 publications

References 29 publications

A microstructure-based parameterization of the effective anisotropic elasticity tensor of snow, firn, and bubbly ice

A microstructure-based parameterization of the effective anisotropic elasticity tensor of snow, firn, and bubbly ice

A microstructure-based parameterization of the effective, anisotropic elasticity tensor of snow, firn, and bubbly ice

The AVO Effect of Formation Pressure on Time-Lapse Seismic Monitoring in Marine Carbon Dioxide Storage

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