Full Reciprocity-Gap Waveform Inversion in the frequency domain, enabling sparse-source acquisition

Abstract : We perform quantitative sub-surface Earth-imaging by setting up the Full Reciprocity-gap Waveform Inversion (FRgWI ) method. The reconstruction relies on iterative minimization of a misfit functional which is specifically designed for data obtained from dual-sensors devices. In addition to the pressure field, the dual-sensors devices are able to measure the normal velocity of the waves and have been deployed in exploration geophysics. The use of reciprocity-based misfit functional provides additional features compared to the more traditional least-squares approaches with, in particular, that the observational and computational acquisitions can be different. Therefore, the source positions and wavelets that generate the measurements are not needed for the reconstruction procedure and, in fact, the numerical acquisition (for the simulations) can be arbitrarily chosen. We illustrate our method with three-dimensional experiments, where we first show that the reciprocity-gap inversion behaves better than the Full Waveform Inversion (FWI) in the same context. Next, we investigate arbitrary numerical acquisitions in two ways: firstly, when few measurements are given, we use a dense numerical acquisition (compared to the observational one). On the other hand, with a dense observational acquisition, we employ a sparse computational acquisition, with multiple-point sources, to reduce the numerical cost. We highlight that the reciprocity-gap functional is very efficient in both situations and is much more robust with respect to cross-talk than shot-stacking.
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Contributor : Florian Faucher <>
Submitted on : Tuesday, July 23, 2019 - 7:48:12 AM
Last modification on : Wednesday, July 24, 2019 - 1:25:32 AM

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  • HAL Id : hal-02190824, version 1
  • ARXIV : 1907.09163



Florian Faucher, Giovanni Alessandrini, Hélène Barucq, Maarten V. de Hoop, Romina Gaburro, et al.. Full Reciprocity-Gap Waveform Inversion in the frequency domain, enabling sparse-source acquisition. 2019. ⟨hal-02190824⟩



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