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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
1Department of Theoretical and Experimental Physics, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
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.
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.
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