1887

Abstract

Summary

This work presents advancements in multiparameter elastic full-waveform inversion (FWI) with composite alternating misfit functions. Elastic FWI is a valuable tool for seismic imaging, capable of deriving Earth attributes directly from recorded waveforms, particularly in geologically complex settings. We propose a composite misfit function that combines a time-shift term and a shifted least-squares norm. The time-shift term ensures kinematic alignment of waveforms, while the shifted least-squares norm adjusts elastic parameters to match the amplitude variation with offset (AVO) response. A dynamic weighting scheme alternates between these terms during FWI iterations, eliminating the need for static hyperparameters. Field applications illustrate the method’s effectiveness. For the 2014 Chevron FWI benchmark test, the composite elastic FWI approach reduced imaging artifacts and enhanced subsurface detail compared to conventional least-squares misfit functions. A shallow-water field example from the North Sea demonstrated its capacity to reconstruct P-wave velocity and elastic properties while handling multiples and ghosts without significant preprocessing. Although the method requires substantial computational resources, its application to minimally processed field data shows promise for imaging and elastic property extraction in complex environments. Continued research and development are needed to refine the approach and address its computational demands.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202510680
2025-06-02
2026-02-08

  • From This Site

    /content/papers/10.3997/2214-4609.202510680
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Bergounioux, E., Rivera, C., Duquet, B. and Dolliazal, M. [2017]. A Generic Multi-parameter FWI Framework Based on Symbolic Expressions. Third EAGE Workshop on High Performance Computing for Upstream, 1–5
     [Google Scholar]
  2. Bogey, C. and Bailly, C. [2004]. A family of low dispersive and low dissipative explicit schemes for noise computation. Journal of Computational Physics, 194(1), 194–214
     [Google Scholar]
  3. Brando, G., Huard, B. and Cypriano, L. [2023]. Imaging the presalt with elastic FWI using OBN data. Third International Meeting for Applied Geoscience & Energy, SEG/AAPG, Expanded Abstracts, 685–689
     [Google Scholar]
  4. Cho, Y., Pérez Solano, C., Kimbro, J., Yang, Y., Plessix, R.-É. and Matson, K. [2022]. Influence of shear velocity on elastic full-waveform inversion: Gulf of Mexico case study using multicomponent ocean-bottom node data. Geophysics, 87(5), R391–R400
     [Google Scholar]
  5. He, W., Brossier, R. and Métivier, L. [2019]. 3D elastic FWI for land seismic data: A graph space OT approach. SEG Technical Program, Expanded Abstracts, 1320–1324
     [Google Scholar]
  6. Kennett, B.L.N. and Williamson, P.R. [1988]. Subspace methods for large-scale nonlinear inversion. Mathematical Geophysics, D. Reidel, 139–154
     [Google Scholar]
  7. Kingma, D.P. and Ba, J. [2015]. Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA.
     [Google Scholar]
  8. Lailly, P. [1983]. The seismic inverse problem. Proceedings of the 53rd Meeting of the Society of Exploration Geophysicists, 1–5
     [Google Scholar]
  9. Martin, R., Komatitsch, D. and Ezziani, A. [2008]. An unsplit convolutional perfectly matched layer improved at grazing incidence for seismic wave propagation in poroelastic media. Geophysics, 73, T51–T61
     [Google Scholar]
  10. Rivera, C., Trinh, P.-T., Bergounioux, E. and Duquet, B. [2019]. Elastic multiparameter FWI in sharp contrast medium. 89th Annual International Meeting, SEG, Expanded Abstracts, 1410–1414
     [Google Scholar]
  11. Routh, P., Nayak, S., Dorsett, J., Banerji, K., Cha, Y.H., Gottumukkula, V., Palacharla, G., Gudipati, V., Neelamani, R., Conway, S. and Raphael, A. [2023]. Value of elastic full-wavefield inversion in derisking clastic reservoirs in presence of noise. SEG Technical Program, Expanded Abstracts, 576–580
     [Google Scholar]
  12. Tarantola, A. [1984]. Inverse problem theory. Elsevier, Amsterdam
     [Google Scholar]
  13. Vigh, D., Cheng, X., Bai, B., Feng, Z. and Glaccum, K. [2023]. Elastic multiparameter full-waveform inversion application on sparse ocean-bottom node data. SEG Technical Program, Expanded Abstracts, 595–599
     [Google Scholar]
  14. Wang, K., and Lazaratos, S. [2017]. Multi-parameter inversion through offset dependent elastic FWI: U.S. patent no. 9702993
     [Google Scholar]
  15. Zhang, Z., Mei, J., Lin, F., Huang, R. and Wang, P. [2018]. Correcting for salt misinterpretation with full-waveform inversion. 88th Annual International Meeting, SEG, Expanded Abstracts, 1143–1147
     [Google Scholar]
  16. Zhang, Z., Wu, Z., Wei, Z., Mei, J., Huang, R. and Wang, P. [2023]. Enhancing salt model resolution and subsalt imaging with elastic FWI. The Leading Edge, 42, 207–215
     [Google Scholar]
/content/papers/10.3997/2214-4609.202510680
Loading
Multiparameter Elastic Full Waveform Inversion with Composite Alternating Misfit Functions
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202510680
/content/papers/10.3997/2214-4609.202510680
/content/papers/10.3997/2214-4609.202510680
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

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