Markov-random-field modeling for linear seismic tomography

Tatsu Kuwatani, Kenji Nagata, Masato Okada, Mitsuhiro Toriumi

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences.

Original languageEnglish
Article number042137
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Issue number4
Publication statusPublished - 2014 Oct 23

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


Dive into the research topics of 'Markov-random-field modeling for linear seismic tomography'. Together they form a unique fingerprint.

Cite this