[M-GI34] データ駆動地球惑星科学

[MGI34-P03] マルコフ確率場モデルを用いた観測画像からの連続および不連続な物理量の空間分布同時推定

*桑谷 立¹、永田 賢二²、岡田 真人³ (1.国立研究開発法人海洋研究開発機構、2.国立研究開発法人物質材料研究機構、3.東京大学大学院新領域創成科学研究科)

キーワード：ベイズ推論、地殻流体

Quantitative geological interpretation of geophysical observation data is essential in geosciences. In such inversion problems, geological unknown parameters can be modeled as either continuous variables or discontinuous variables. For example, Iwamori et al. (2021, JGR) assumed geometrical property and amount of geofluids as continuous variables, whereas they assumed the type of fluid (melt or aqueous fluid) and lithology as discontinuous variables. This study develops a general method that simultaneously extracts spatial structures of continuous and discontinuous variables from observed images by extending the Markov random field (MRF) modeling, a Bayesian image-processing method which effectively uses a spatial continuity of physical quantities. Specifically, we use a Potts spin for a discontinuous variable and introducing it into the Markov random field as a region-based latent variable. In this presentation, we will briefly introduce the preliminary synthetic inversion tests that simultaneously estimate the continuous fluid distribution and the discontinuous lithological structure from the seismic velocity structure.