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\begin{document}

\title{Bayesian multi-model estimation for fault slip distribution: the effect
of prior constraints in the estimation for slow slip events beneath the
Bungo Channel, southwest Japan}



\author[1]{Ryoichiro Agata}%
\author[2]{Ryoko Nakata}%
\author[3]{Amato Kasahara}%
\author[4]{Yuji Yagi}%
\author[5]{Yukinari Seshimo}%
\author[5]{Shoichi Yoshioka}%
\author[1]{Takeshi Iinuma}%
\affil[1]{Japan Agency for Marine-Earth Science and Technology}%
\affil[2]{Tohoku University}%
\affil[3]{Independent researcher}%
\affil[4]{University of Tsukuba}%
\affil[5]{Kobe University}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\begin{abstract}
We consider a Bayesian multi-model fault slip estimation (BMMFSE), in
which many candidates of the underground-structure model characterized
by a prior probability density function (PDF) are retained for a fully
Bayesian estimation of fault slip distribution to manage model
uncertainty properly. We performed geodetic data inversions to estimate
slip distribution in long-term slow slip events (L-SSEs) that occurred
beneath the Bungo Channel, southwest Japan, in around 2010 and 2018,
focusing on the two advantages of BMMFSE: First, it allows for
estimating slip distribution without introducing strong prior
information such as smoothing constraints, handling an ill-posed inverse
problem by combining a full Bayesian inference and accurate
consideration of model uncertainty to avoid overfitting; second, the
posterior PDF for the underground structure is also obtained through a
fault slip estimation, which enables the estimation of sequential events
by reducing the model uncertainty. The estimated slip distribution
obtained using BMMFSE agreed better with the distribution of deep
tectonic tremors at the down-dip side of the main rupture area than
those obtained based on strong prior constraints in terms of the spatial
distribution of the Coulomb failure stress change. This finding suggests
a mechanical relationship between the L-SSE and the synchronized
tremors. The use of the posterior PDF for the underground structure
updated by the estimation for the 2010 L-SSE as an input of the analysis
for the one in 2018 resulted in a more preferable Bayesian inference,
indicated by a smaller value of an information criterion.%
\end{abstract}%



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