Time Variable Stress Inversion of Centroid Moment Tensor Data Using
Gaussian Processes
Abstract
This study presents a new method of centroid moment tensor (CMT) data
inversion to estimate time-dependent regional stress fields. A Gaussian
process (GP) is applied to resolve a computational difficulty of the
existing basis function expansion method in analyzing high-dimensional
data including time dependence. A critical step in the formulation is an
analytical derivation of the relationship of the covariance function,
which is a key ingredient of GP, between CMT data and a model stress
field based on an observation equation. Applications to CMT data in and
around Japan after the 2011 Tohoku earthquake show the efficiency and
validity of the method, which clarifies that the stress field has
small-scale heterogeneity in space and long-term stability in time for
most regions. Additionally, significant temporal variations are observed
around the margin of the focal region of the 2011 event, the sense of
which is opposite in the landward side and the oceanward side. GP would
be particularly effective in geophysical inversions of high-dimensional
data distributed in a broad region.