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Intelligent optimization based prestack inversion method for high resolution estimation of elastic parameters.

Wei Zhang1,2, Keyu Zhao3, Weiyu Sun1

  • 1School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.

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Summary

This study introduces a new seismic inversion method, mayfly algorithm (MA)-based nonlinear AVA inversion (MANAI), for accurate subsurface modeling. MANAI enhances computational efficiency and stability in seismic data analysis.

Keywords:
AVA inversionExact Zoeppritz equationsMayfly algorithmParallel computing

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Area of Science:

  • Geophysics
  • Computational Seismology

Background:

  • Seismic inversion is crucial for subsurface modeling in various applications.
  • Existing methods face challenges in accuracy and computational efficiency.

Purpose of the Study:

  • To develop a novel, efficient, and accurate pre-stack amplitude variation with angle (AVA) inversion method.
  • To improve subsurface model construction for reservoir characterization and geological analysis.

Main Methods:

  • Proposed a mayfly algorithm (MA)-based nonlinear AVA inversion (MANAI) within a Bayesian framework.
  • Utilized an indirect global optimization strategy with a prior model to extract gradient information.
  • Derived an accurate Jacobian matrix from the exact Zoeppritz equations.

Main Results:

  • MANAI demonstrated superior stability and accuracy compared to whale optimization algorithm (WOA)-based inversion and conventional local nonlinear methods.
  • Reduced computational cost by eliminating iterative reflection coefficient calculations.
  • Parallel implementation further accelerated the inversion process.

Conclusions:

  • MANAI offers an efficient and reliable solution for seismic inversion in complex exploration scenarios.
  • The method provides improved inversion results and reduced runtime on both synthetic and real seismic data.