Markov-random-field modeling for linear seismic tomography.
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.
Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan.
Tatsu
T
Kuwatani
Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan.
Kenji
K
Nagata
Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan and RIKEN Brain Science Institute, Saitama 351-0198, Japan.
Masato
M
Okada
Laboratory of Ocean-Earth Life Evolution Research, Japan Agency for Marine-Earth Science and Technology, Kanagawa 237-0061, Japan.
Mitsuhiro
M
Toriumi
D003198:Computer Simulation; D055593:Geological Phenomena; D016014:Linear Models; D008390:Markov Chains; D008962:Models, Theoretical