1887

Abstract

Summary

Considering the complex nonlinearity between seismic data and perturbations in the model, a waveform optimization problem based on the least square sample-to-sample comparison has proven to be inadequate in dealing with such nonlinearity. A slew of more global comparison based optimizations have proven their values in circumventing such nonlinearity. Among these methods in particular, adaptive waveform inversion (AWI), which is based on the deconvolution operator, offers the right balance between phase matching and amplitude normalization. The key element in the AWI success is the normalization factor, which is inherently offered by the instantaneous traveltime measure used to unwrap the phase. Thus, we recast the AWI problem using a misfit function based on the original generalized instantaneous travel time, which highlights some of the features of AWI and traces their roots. We demonstrate that the misfit function of AWI is actually the least square averaged instantaneous travel time over multiple frequencies. We use the Marmousi model and Chevron 2014 FWI benchmark dataset to verify the effectiveness of the proposed method.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201801028
2018-06-11
2024-03-28
Loading full text...

Full text loading...

References

  1. Alkhalifah, T. and Choi, Y.
    [2012] Taming waveform inversion non-linearity through phase unwrapping of the model and objective functions. Geophysical Journal International, 191(3), 1171–1178.
    [Google Scholar]
  2. [2014] From tomography to full-waveform inversion with a single objective function. Geophysics, 79(2), R55–R61.
    [Google Scholar]
  3. Debens, H.A., Mancini, F., Warner, M. and Guasch, L.
    [2017] Full-bandwidth adaptive waveform inversion at the reservoir. SEG Technical Program Expanded Abstracts 2017, 1378–1382.
    [Google Scholar]
  4. Huang, G., Nammour, R. and Symes, W.
    [2017] Full-waveform inversion via source-receiver extension. Geophysics, 82(3), R153–R171.
    [Google Scholar]
  5. van Leeuwen, T. and Herrmann, F.J.
    [2013] Mitigating local minima in full-waveform inversion by expanding the search space. Geophysical Journal International, 195(1), 661–667.
    [Google Scholar]
  6. Luo, S. and Sava, P.
    [2011] A deconvolution based objective function for wave equation inversion. SEG Technical Program Expanded Abstracts 2011, 2788–2792.
    [Google Scholar]
  7. Metivier, L., Brossier, R., Merigot, Q., Oudet, E. and Virieux, J.
    [2016] Measuring the misfit between seismograms using an optimal transport distance: application to full waveform inversion. Geophysical Journal International, 205(1), 345–377.
    [Google Scholar]
  8. Van Leeuwen, T. and Mulder, W.
    [2008] Velocity analysis based on data correlation. Geophysical Prospecting, 56(6), 791–803.
    [Google Scholar]
  9. Van Leeuwen, T. and Mulder, W.A.
    [2010] A correlation-based misfit criterion for wave-equation traveltime tomography. Geophysical Journal International, 182(3), 1383–1394.
    [Google Scholar]
  10. Virieux, J. and Operto, S.
    [2009] An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
    [Google Scholar]
  11. Warner, M. and Guasch, L.
    [2016] Adaptive waveform inversion: Theory. Geophysics, 81(6), R429–R445.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201801028
Loading
/content/papers/10.3997/2214-4609.201801028
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error