Elastic Full Waveform Inversion (FWI) of reflection data with a phase misfit function
Full Waveform Inversion of elastic dataset is challenging due to the complexity introduced by free-surface effects or P-S wave conversions among others. In this context, large offsets are preferred for inversion because they favor transmission modes which are more linearly related to P-wave velocity...
| Autores: | , , , , , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/85902 |
| Acceso en línea: | https://hdl.handle.net/2117/85902 https://dx.doi.org/10.1007/978-3-319-32243-8_19 |
| Access Level: | acceso abierto |
| Palabra clave: | Elastic waves High performance computing Full waveform inversion High-performance computing Supercomputadors Tsunamis Àrees temàtiques de la UPC::Enginyeria electrònica |
| Sumario: | Full Waveform Inversion of elastic dataset is challenging due to the complexity introduced by free-surface effects or P-S wave conversions among others. In this context, large offsets are preferred for inversion because they favor transmission modes which are more linearly related to P-wave velocity. In this paper, we present an original approach which allows to dynamically select the near offset at each frequency. We illustrate this approach with the inversion of a dataset without density. In order to deal with a more realistic scenario, we next present the inversion with density effects included into the modeling. As inverting density is known to be a hard task, we choose to not invert it. This approach leads to the use of a phase misfit function, which is more connected to the kinematics of the problem than the classic L2 norm. |
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