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Markov-random-field modeling for linear seismic tomography.

Tatsu Kuwatani1, Kenji Nagata2, Masato Okada3

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Summary
This summary is machine-generated.

This study introduces a robust Markov-random-field model for seismic tomography, successfully reconstructing velocity structures by optimizing hyperparameters. The method offers a versatile framework for various scientific inversion problems.

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Area of Science:

  • Geophysics
  • Earth Sciences
  • Computational Seismology

Background:

  • Seismic tomography is crucial for imaging Earth's subsurface.
  • Estimating model parameters, like smoothness and noise, is vital for accurate tomographic results.
  • Current methods may face challenges in hyperparameter optimization.

Purpose of the Study:

  • To develop and validate a novel method for estimating hyperparameters in linear seismic tomography.
  • To enhance the accuracy and robustness of seismic velocity structure reconstruction.
  • To establish a flexible mathematical framework applicable to diverse scientific inversion problems.

Main Methods:

  • Application of the Markov-random-field model to linear seismic tomography.
  • Analytical estimation of hyperparameters by minimizing a free energy function.
  • Utilizing marginalization of the evaluation function to define the free energy.

Main Results:

  • Successful reconstruction of assumed velocity structures in synthetic inversion tests.
  • Demonstration of the method's effectiveness and robustness across various settings.
  • Validation of the analytical hyperparameter estimation technique.

Conclusions:

  • The proposed method provides an effective and robust approach to seismic tomography hyperparameter estimation.
  • The developed mathematical framework is broadly applicable to inversion problems in natural sciences.
  • This work advances the field of geophysical imaging and computational science.