Markov random field modeling for mapping geofluid distributions from seismic velocity structures

Abstract

We applied the Markov random field model, which is a kind of a Bayesian probabilistic method, to the spatial inversion of the porosity and pore shape in rocks from an observed seismic structure. Gaussian Markov chains were used to incorporate the spatial continuity of the porosity and the aspect ratio of the pore shape. Synthetic inversion tests were able to show the effectiveness and validity of the proposed model by appropriately reducing the statistical noise from the observations. The proposed model was also applied to natural data sets of the seismic velocity structures in the mantle wedge beneath northeastern Japan, under the assumptions that the fluid was melted and the temperature and petrologic structures were uniformly distributed. The result shows a significant difference between the volcanic front and the forearc regions, at a depth of 40 km. Although the parameters and material properties will need to be determined more precisely, the Markov random field model presented here can serve as a basic inversion framework for mapping geofluids.