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An objective function for full-waveform inversion based on frequency-dependent offset-preconditioning

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  • Sérgio Luiz E F da Silva
  • Pedro T C Carvalho
  • Carlos A N da Costa
  • João M de Araújo
  • Gilberto Corso

Abstract

Full-waveform inversion (FWI) is a powerful technique to obtain high-resolution subsurface models, from seismic data. However, FWI is an ill-posed problem, which means that the solution is not unique, and therefore the expert use of the information is required to mitigate the FWI ill-posedness, especially when wide-aperture seismic acquisitions are considered. In this way, we investigate the multiscale frequency-domain FWI by using a weighting operator according to the distances between each source-receiver pair. In this work, we propose a weighting operator that acts on the data misfit as preconditioning of the objective function that depends on the source-receiver distance (offset) and the frequency used during the inversion. The proposed operator emphasizes information from long offsets, especially at low frequencies, and as a consequence improves the update of deep geological structures. To demonstrate the effectiveness of our proposal, we perform numerical simulations on 2D acoustic Marmousi2 case study, which is widely used in seismic imaging tests, considering three different scenarios. In the first two ones, we have used an acquisition geometry with a maximum offset of 4 and 8 km, respectively. In the last one, we have considered all-offsets. The results show that our proposal outperforms similar strategies, for all scenarios, providing more reliable quantitative subsurface models. In fact, our inversion result has the lowest error and the highest similarity to the true model than similar approaches.

Suggested Citation

  • Sérgio Luiz E F da Silva & Pedro T C Carvalho & Carlos A N da Costa & João M de Araújo & Gilberto Corso, 2020. "An objective function for full-waveform inversion based on frequency-dependent offset-preconditioning," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
  • Handle: RePEc:plo:pone00:0240999
    DOI: 10.1371/journal.pone.0240999
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    Cited by:

    1. da Silva, Sérgio Luiz E.F. & Silva, R. & dos Santos Lima, Gustavo Z. & de Araújo, João M. & Corso, Gilberto, 2022. "An outlier-resistant κ-generalized approach for robust physical parameter estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).

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