Combining Ensemble Transform Kalman Filter and FWI for Assessing Uncertainties
| Autor: | Romain Brossier, Ludovic Métivier, Julien Thurin |
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| Přispěvatelé: | Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Equations aux Dérivées Partielles (EDP ), Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]) |
| Rok vydání: | 2019 |
| Předmět: |
Scale (ratio)
Computer science Feature (computer vision) Inverse transform sampling [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph] Inversion (meteorology) Kalman filter Inverse problem [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Algorithm Full waveform Field (computer science) |
| Zdroj: | 81st EAGE Conference and Exhibition 2019 Workshop Programme 81st EAGE Conference and Exhibition 2019 Workshop Programme, Jun 2019, London, United Kingdom. ⟨10.3997/2214-4609.201901997⟩ |
| DOI: | 10.3997/2214-4609.201901997 |
| Popis: | International audience; Full Waveform Inversion (FWI) is an iterative inversion method whose purpose is to retrieve high-resolution models of subsurface physical parameters. Because FWI relies on the solution of a non-linear ill-posed inverse problem, uncertainty estimation is a crucial issue in practical applications, both in seismology and exploration seismic. While uncertainty assessment is a strongly desired feature for FWI, it remains a challenging problem. In this presentation, we investigate uncertainty estimation within the framework provided by ensemble data-assimilation strategies. We combine the Ensemble Transform Kalman Filter and FWI. We review the concepts underlying our ETKF-FWI method, discuss its limitations and appeals for uncertainty estimation, and illustrate it on a 2D multiparameter inversion of an exploration scale field dataset. |
| Databáze: | OpenAIRE |
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