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Improving Seismic Inversion Robustness via Deformed Jackson Gaussian.

Suzane A Silva1, Sérgio Luiz E F da Silva2, Renato F de Souza3

  • 1Programa de Pós-Graduação em Ciência e Engenharia de Petróleo, Universidade Federal do Rio Grande do Norte, Natal 59078-970, RN, Brazil.

Entropy (Basel, Switzerland)
|August 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new robust seismic inversion method using a deformed Jackson Gaussian distribution to effectively handle outlier data. The proposed method significantly outperforms traditional approaches in seismic model estimation.

Keywords:
Jackson statisticsdeformed Gaussian distributioninverse problemspost-stack seismic inversionrobust inference

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Area of Science:

  • Geophysics
  • Seismic data processing
  • Computational geosciences

Background:

  • Seismic data inversion is crucial for subsurface modeling but is challenged by outliers.
  • Existing methods for outlier mitigation in seismic inversion are often slow and computationally intensive.

Purpose of the Study:

  • To develop a robust seismic inversion methodology resistant to spurious measurements (outliers).
  • To improve the accuracy and efficiency of seismic model estimation in the presence of noisy data.

Main Methods:

  • A novel outlier-resistant seismic inversion formulation based on the deformed Jackson Gaussian distribution.
  • Testing the methodology on a classic geophysical inverse problem with varied noise scenarios, including incorrect seismic sources, Gaussian errors, and multiple outliers.

Main Results:

  • The proposed deformed Jackson Gaussian method demonstrates superior performance compared to the standard Gaussian distribution-based approach.
  • Effective mitigation of outlier influence on seismic inversion results was achieved across different contamination scenarios.

Conclusions:

  • The deformed Jackson Gaussian distribution offers a robust and effective solution for seismic data inversion contaminated by outliers.
  • This methodology enhances the reliability of subsurface model estimation in challenging geophysical data conditions.