Recovering the past history of natural recording media by Bayesian inversion

Tatsu Kuwatani, Hiromichi Nagao, Shin Ichi Ito, Atsushi Okamoto, Kenta Yoshida, Takamoto Okudaira

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Spatial growth patterns are natural recording media (NRMs) that preserve important historical information, which can be accessed and analyzed to reconstruct past environmental conditions and events. Here, we propose the Bayesian inversion method, which can reconstruct the evolution of target parameters from purely spatial data by extending data assimilation (DA), a method for integrating numerical simulations with time-series observations. Our method converts discrete spatial observation data to time-series data with the help of a law representing the NRM's time-evolution dynamics and Gaussian process regression, enabling us to directly compare the observations with a numerical simulation based on the DA framework. The method's effectiveness is demonstrated using a synthetic inversion problem, namely reconstructing the pressureerature-time (P-T-t) path of a metamorphic rock from chemical composition profiles of its zoned minerals. The proposed method is broadly applicable to a wide variety of NRMs.

Original languageEnglish
Article number043311
JournalPhysical Review E
Issue number4
Publication statusPublished - 2018 Oct 31

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


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