Acoustic Full Waveform Inversion from Cauchy data via conditional well-posedness driven iterative regularization

Abstract : In this paper, we study the performance of Full Waveform Inversion (FWI) from time-harmonic Cauchy data via conditional well-posedness driven iterative regularization. The Cauchy data can be obtained with dual sensors measuring the pressure and the norma velocity. The conditional well-posedness is obtained for a hierarchy of subspaces in which the inverse problem with partial data is Lipschitz stable. Here, these subspaces yield piecewise linear representations of the wave speed on given domain partitions. Domain partitions can be adaptively obtained through segmentation of the gradient. The domain partitions can be taken as a coarsening of an unstructured tetrahedral mesh associated with a finite element discretization of the Helmholtz equation. We illustrate the effectiveness of the iterative regularization through computational experiments with three-dimensional data. In comparison with earlier work, the Cauchy data do not suffer from eigenfrequencies in the configurations.
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https://hal.archives-ouvertes.fr/hal-01623949
Contributor : Florian Faucher <>
Submitted on : Wednesday, October 25, 2017 - 7:35:35 PM
Last modification on : Wednesday, October 2, 2019 - 2:42:36 PM

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  • HAL Id : hal-01623949, version 1

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Florian Faucher, Maarten de Hoop, Giovanni Alessandrini, Romina Gaburro, Eva Sincich. Acoustic Full Waveform Inversion from Cauchy data via conditional well-posedness driven iterative regularization. Project Review, Geo-Mathematical Imaging Group at Rice University, Houston TX, Proceedings of the Project Review, Geo-Mathematical Imaging Group at Rice University, Houston TX, Apr 2017, Houston, TX, United States. pp.31--42. ⟨hal-01623949⟩

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