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Full-waveform inversion based on generalized Rényi entropy using patched Green's function techniques.

Wagner A Barbosa1, Sérgio Luiz E F da Silva2,3, Erick de la Barra4,5

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Summary
This summary is machine-generated.

We introduce a robust data inversion framework, the alpha-PGF-FWI, using Rényi alpha-Gaussian distribution and patched Green's function techniques. This method enhances full-waveform inversion (FWI) by resisting noise and outliers for better physical parameter estimation.

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Area of Science:

  • Geophysics
  • Seismic Imaging
  • Data Analysis

Background:

  • Accurate physical parameter estimation is vital for complex system modeling.
  • Classical full-waveform inversion (FWI) can be sensitive to noisy or erroneous data.
  • Existing methods often struggle with non-Gaussian noise and outliers.

Purpose of the Study:

  • To develop a robust data inversion framework for seismic data analysis.
  • To introduce an outlier-resistant criterion for full-waveform inversion (FWI).
  • To improve the estimation of physical parameters in complex geological models.

Main Methods:

  • Utilized Rényi α-Gaussian distribution for a novel misfit function.
  • Implemented patched Green's function (PGF) techniques for efficient forward modeling.
  • Developed the alpha-PGF-FWI framework, integrating these methods.

Main Results:

  • The proposed alpha-PGF-FWI framework demonstrates robustness against Gaussian and non-Gaussian noise with outliers.
  • The method proved effective on realistic P-wave velocity models from the Kwanza Basin and Brazilian pre-salt.
  • Computational efficiency was achieved by solving the wave equation only once for the target area.

Conclusions:

  • The alpha-PGF-FWI offers a significant improvement over traditional FWI, especially in the presence of data irregularities.
  • This approach enhances the reliability of physical parameter estimation in geophysics.
  • The framework is particularly effective for seismic data analysis in complex geological settings.