Recovering the past history of natural recording media by Bayesian inversion

[1]  Masato Okada,et al.  Precise estimation of pressure–temperature paths from zoned minerals using Markov random field modeling: theory and synthetic inversion , 2012, Contributions to Mineralogy and Petrology.

[2]  Genshiro Kitagawa Introduction to Time Series Modeling , 2010 .

[3]  J. Blundy,et al.  Linking Petrology and Seismology at an Active Volcano , 2012, Science.

[4]  M. St-Onge Zoned Poikiloblastic Garnets: P-T Paths and Syn-Metamorphic Uplift through 30 km of Structural Depth, Wopmay Orogen, Canada , 1987 .

[5]  M. Kohn Uncertainties in differential thermodynamic (Gibbs' method) P-T paths , 1993 .

[6]  H. Rollinson Metamorphic history suggested by garnet-growth chronologies in the Isua Greenstone Belt, West Greenland , 2003 .

[7]  J. Hermann,et al.  Yo-yo subduction recorded by accessory minerals in the Italian Western Alps , 2011 .

[8]  Yoshinori Nakanishi-Ohno,et al.  Three levels of data-driven science , 2016 .

[9]  D. Hilton,et al.  Magma reservoir dynamics at Toba caldera, Indonesia, recorded by oxygen isotope zoning in quartz , 2017, Scientific Reports.

[10]  Dominique Poirel,et al.  Bayesian parameter estimation and model selection for strongly nonlinear dynamical systems , 2015, Nonlinear Dynamics.

[11]  J. Ganguly,et al.  Garnet compositions as recorders of P-T-t history of metamorphic rocks , 2010 .

[12]  J. Blundy,et al.  Magma heating by decompression-driven crystallization beneath andesite volcanoes , 2006, Nature.

[13]  Atsushi Okamoto,et al.  Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions. , 2016, Physical review. E.

[14]  R. K. O’nions,et al.  Isotopic chronometry of zoned garnets: growth kinetics and metamorphic histories , 1990 .

[15]  G. Wörner,et al.  Crystal Zoning as an Archive for Magma Evolution , 2007 .

[16]  B. Yardley An empirical study of diffusion in garnet , 1977 .

[17]  T. Higuchi,et al.  A sequential Bayesian approach for the estimation of the age–depth relationship of the Dome Fuji ice core , 2015 .

[18]  K. Hodges,et al.  Realistic propagation of uncertainties in geologic thermobarometry. , 1987 .

[19]  M. Okada,et al.  Maximum tsunami height prediction using pressure gauge data by a Gaussian process at Owase in the Kii Peninsula, Japan , 2016, Marine Geophysical Research.

[20]  T. Okudaira Temperature–time path for the low‐pressure Ryoke metamorphism, Japan, based on chemical zoning in garnet , 1996 .

[21]  M. Kohn,et al.  Error propagation for barometers; 2, Application to rocks , 1991 .

[22]  Teuta Pilizota,et al.  Inferring time derivatives including cell growth rates using Gaussian processes , 2016, Nature Communications.

[23]  T. Ishikawa,et al.  Bayesian Inference of Forces Causing Cytoplasmic Streaming in Caenorhabditis elegans Embryos and Mouse Oocytes , 2016, PloS one.

[24]  Akinori Yamanaka,et al.  Data assimilation for massive autonomous systems based on a second-order adjoint method. , 2016, Physical review. E.

[25]  Y. Kubota,et al.  The spatio-temporal forest patch dynamics inferred from the fine-scale synchronicity in growth chronology , 2011 .

[26]  Mitsuyuki Hoshiba,et al.  Numerical shake prediction for Earthquake Early Warning: data assimilation, real-time shake-mapping, and simulation of wave propagation , 2014 .

[27]  M. Rosing,et al.  Elements of Eoarchean life trapped in mineral inclusions , 2017, Nature.

[28]  C. Gouriéroux,et al.  Non-Gaussian State-Space Modeling of Nonstationary Time Series , 2008 .