[H-TT17] 浅部物理探査が目指す新しい展開

[HTT17-11] Bayesian estimation method of near-surface density under irregular distribution of gravity network

*Yuanyuan Niu¹、Jiancang Zhuang¹ (1.Institute of Statistical Mathematics)

キーワード：ABIC, Delaunay triangulation, Second-order smoothness, Quadratic polynomial fitting, Near-surface density

Bouguer gravity anomaly is an important data to reveal underground structure and construction. It is calculated by terrain correction and Bouguer correction of Free-air gravity data. Near-surface density is an important parameter to be determined in these two corrections. We proposed an Bayesian estimation method to simultaneously estimate Bouguer gravity anomaly and near-surface density. Firstly, the inversion objective function is constructed from the observed Free-air gravity data, near-surface density model and Bouguer gravity anomaly model. At the same time, considering the irregular distribution of 2D gravity network, we use Delaunay triangulation and quadratic polynomial fitting to constrain the faltness and smoothness of Bouguer gravity anomaly model. Then, trade-off parameters are automatically determined by Akaike's Bayesian Information Criteria (ABIC). Finally, the optimal estimated results are obtained by maximizing the posterior estimation of model parameters. Estimated near-surface density can be used to indicate shallow geologic structure and estimated Bouguer gravity anomaly can provide more reliable data onto the study of regional deep structure and tectonics.