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Randomized Dimensionality Reduction for Full–waveform InversionNormal access

Author: F. J. Herrmann
Event name: 72nd EAGE Conference and Exhibition incorporating SPE EUROPEC 2010
Session: FWI & Modelling (EAGE)
Publication date: 14 June 2010
DOI: 10.3997/2214-4609.201400799
Organisations: SPE, EAGE
Language: English
Info: Extended abstract, PDF ( 497.37Kb )
Price: € 20

Full-waveform inversion relies on the collection of large multi-experiment data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While improvements in acquisition and inversion have been extremely successful, the current trend of incessantly pushing for higher quality models in increasingly complicated regions of the Earth reveals fundamental shortcomings in our ability to handle increasing problem size numerically. Two main culprits can be identified. First, there is the so-called ``curse of dimensionality'' exemplified by Nyquist's sampling criterion, which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution increases. Secondly, there is the recent ``departure from Moore's law'' that forces us to lower our expectations to compute ourselves out of this. In this paper, we address this situation by randomized dimensionality reduction, which we adapt from the field of compressive sensing. In this approach, we combine deliberate randomized subsampling with structure-exploiting transform-domain sparsity promotion. Our approach is successful because it reduces the size of seismic data volumes without loss of information. With this reduction, we compute Newton-like updates at the cost of roughly one gradient update for the fully-sampled wavefield.

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