International audienceThe pur pose of this paper is to give an upscaling tool valid for the wave equation in general elastic media. This paper is focused on P–SV wave propagation in 2-D, but the methodology can be extended without any theoretical difficulty to the general 3-D case. No assumption on the heterogeneity spectrum is made and the medium can show rapid variations of its elastic properties in all spatial directions. The method used is based on the two-scale homogenization expansion, but extended to the non-periodic case. The scale separation is made using a spatial low-pass filter. The ratio of the filter wavelength cut-off and the minimum wavelength of the propagating wavefield defines a parameter ε0 with which the wavefield propagating in the homogenized medium converges to the reference wavefield. In the general case, this non- periodic extension of the homogenization technique is only valid up to the leading order and for the so-called first-order cor rector. We apply this non-periodic homogenization procedure to two kinds of heterogeneous media: a randomly generated, highly heterogeneous medium and the Marmousi2 geological model. The method is tested with the Spectral Element Method as a solver to the wave equation. Comparing computations in the homogenized media with those obtained in the original ones shows that convergence with ε0 is even better than expected. The effects of the leading order cor rection to the source and first cor rection at the receivers' location are shown
According to different types of observations, the nature of lithosphere‐asthenosphere boundary (LAB) is controversial. Using a massive data set of surface wave dispersions in a broad period range (15–300 s), we have developed a three‐dimensional upper mantle tomographic model (first‐order perturbation theory) at the global scale. This is used to derive maps of the LAB from the resolved elastic parameters. The key effects of shallow layers and anisotropy are taken into account in the inversion process. We investigate LAB distribution primarily below the oceans, according to different kinds of proxies that correspond to the base of the lithosphere from the shear velocity variation at depth, the amplitude radial anisotropy, and the changes in azimuthal anisotropy G orientation. The estimations of the LAB depth based on the shear velocity increase from a thin lithosphere (∼20 km) in the ridges, to a thick old‐ocean lithosphere (∼120–130 km). The radial anisotropy proxy shows a very fast increase in the LAB depth from the ridges, from ∼50 km to the older ocean where it reaches a remarkable monotonic subhorizontal profile (∼70–80 km). The LAB depths inferred from the azimuthal anisotropy proxy show deeper values for the increasing oceanic lithosphere (∼130–135 km). The difference between the evolution of the LAB depth with the age of the oceanic lithosphere computed from the shear velocity and azimuthal anisotropy proxies and from the radial anisotropy proxy raises questions about the nature of the LAB in the oceanic regions and of the formation of the oceanic plates.
The rock‐ice avalanche that occurred in 2005 on Mount Steller, Alaska and the resulting long period seismic waves have been simulated for different avalanche scenarios (i.e., flow histories), with and without erosion processes taken into account. This 40–60 Mm3avalanche traveled about 10 km down the slope, mainly on top of a glacier, eroding a significant amount of ice. It was recorded by 7 broadband seismic stations. The simulations were compared with the recorded long period seismic signal and with the inverted flow history. The results show that, when erosion processes are taken into account, the simulations reproduce the observed signal at all the stations over a wide range of azimuths and source‐station distances (37–623 km). This comparison makes it possible to constrain the rheological parameters involved which should help constrain the volume of eroded material. Because landslides are continuously recorded by seismic networks, this method could significantly broaden quantitative insights into natural flow dynamics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.