Randomized Source Sketching for Full Waveform Inversion

Partial differential equation (PDE)-constrained optimization problems such as seismic full waveform inversion (FWI) frequently arise in the geoscience and related fields. For such problems, many observations are usually gathered by multiple sources, which form the right-hand sides of the PDE constra...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 12
Hauptverfasser: Aghazade, Kamal, Aghamiry, Hossein S., Gholami, Ali, Operto, Stephane
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
Algorithms
Alternating direction methods of multipliers (ADMMs)
Benchmarks
Computational modeling
Constraints
Convergence
Crosstalk
Dimensions
Earth Sciences
Encoding
Inverse problems
Massive data points
Mathematical models
Numerical models
Optimization
partial differential equation (PDE)
Partial differential equations
randomized source sketching
Regularization
Sciences of the Universe
seismic full waveform inversion (FWI)
Seismic surveys
sparsity-promoting regularization
Transforms
Waveforms
ISSN:0196-2892, 1558-0644
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Zusammenfassung:Partial differential equation (PDE)-constrained optimization problems such as seismic full waveform inversion (FWI) frequently arise in the geoscience and related fields. For such problems, many observations are usually gathered by multiple sources, which form the right-hand sides of the PDE constraint. Solving the inverse problem with such massive datasets is computationally demanding, in particular, when dealing with a large number of model parameters. This article proposes a novel randomized source sketching method for the efficient resolution of multisource PDE-constrained optimization problems. We first formulate the different source-encoding strategies used in seismic imaging into a unified framework based on a randomized sketching. To this end, the source dimension of the problem is projected in a smaller domain by a suitably defined projection matrix that gathers the physical sources in super-sources through a weighted summation. This reduction in the number of physical sources decreases significantly the number of PDE solves while suitable sparsity-promoting regularization can efficiently mitigate the footprint of the crosstalk noise to maintain the convergence speed of the algorithm. We implement the randomized sketching method in an extended search-space formulation of frequency-domain FWI, which relies on the alternating-direction method of multipliers (ADMMs). Numerical examples carried out with a series of well-documented 2-D benchmarks demonstrate that the randomized sketching algorithm reduces the cost of large-scale problems by at least one order of magnitude compared to the original deterministic algorithm.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3131039