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Target-oriented Elastic Full-Waveform Inversion. SEP-183 (2021)


Table of contents

  • Chapter 1: Introduction
  • Chapter 2: Theory
  • Chapter 3: Synthetic application of target-oriented elastic FWI
  • Chapter 4: Deep-water ocean-bottom-node field-data application
  • Chapter 5: Conclusions
  • Appendix A: The adjoint-state method
  • Appendix B: Subsurface-offset to angle transformation
  • Bibliography

Wave-equation-based parameter estimation techniques can retrieve accurate and high-resolution subsurface physical properties from seismic data acquired close to the surface of the Earth. In fact, multiple acoustic full-waveform inversion methods have been proposed over the years to retrieve the P-wave velocity of the subsurface. Moreover, researchers have extended full-waveform inversion approaches to estimate anisotropic and absorption parameters as well. Nowadays, some applications of elastic full-waveform inversion can also be found. However, given its prohibitive computational cost compared to the acoustic counterpart, elastic wave-equation inversion workflows still have limited applicability within seismic exploration datasets. To tackle this challenge, I propose a novel wave-equation-based elastic parameter estimation workflow based on wave-equation operators. I refer to the entire approach as target-oriented elastic full-waveform inversion. The method is composed of two steps. In the first one, I apply an extended linearized waveform inversion to the surface data. The obtained subsurface image is then employed to synthesize data as if they were acquired close to a target area. Finally, this dataset is inverted using an elastic full-waveform inversion workflow to estimate the subsurface elastic parameters. I demonstrate its efficacy on a 2D synthetic test and an ocean-bottom-node dataset acquired in the Gulf of Mexico, showing its ability to retrieve the elastic parameters of potential subsurface prospects. Compared to the elastic inversion of the surface dataset, the proposed method has a computational cost lower by two orders of magnitude.

Reproducibility and source codes

This thesis has been tested for reproducibility. The source codes are made available at these GitHub repositories:

Ettore Biondi
Publication Date
January, 2021