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An objective function for full-waveform inversion based on frequency-dependent offset-preconditioning.

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This study introduces a new weighting operator for full-waveform inversion (FWI) to improve subsurface model accuracy. The method enhances deep geological structure updates by emphasizing long-offset, low-frequency seismic data.

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Area of Science:

  • Geophysics
  • Seismic Imaging
  • Inversion Methods

Background:

  • Full-waveform inversion (FWI) is crucial for high-resolution subsurface modeling from seismic data.
  • FWI is an ill-posed problem requiring expert data utilization, especially with wide-aperture acquisitions.
  • Addressing FWI ill-posedness is key for reliable subsurface model generation.

Purpose of the Study:

  • To investigate multiscale frequency-domain FWI using a novel weighting operator.
  • To enhance the accuracy of subsurface models by mitigating FWI ill-posedness.
  • To improve the update of deep geological structures in seismic inversion.

Main Methods:

  • Developed a weighting operator for the data misfit in frequency-domain FWI.
  • The operator considers source-receiver distance (offset) and frequency for objective function preconditioning.
  • Applied the method to the 2D acoustic Marmousi2 case study with varying acquisition geometries.

Main Results:

  • The proposed weighting operator effectively emphasizes long offsets, particularly at low frequencies.
  • Numerical simulations demonstrated improved updates of deep geological structures.
  • The method outperformed existing strategies across different offset scenarios, yielding more reliable quantitative models.

Conclusions:

  • The novel weighting operator significantly enhances the reliability and accuracy of FWI subsurface models.
  • The approach provides superior results compared to similar strategies, achieving lower error and higher similarity to true models.
  • This method offers a robust solution for improving deep geological structure imaging in seismic inversion.