Transdimensional inverse thermal history modeling for quantitative thermochronology

A new approach for inverse thermal history modeling is presented. The method uses Bayesian transdimensional Markov Chain Monte Carlo and allows us to specify a wide range of possible thermal history models to be considered as general prior information on time, temperature (and temperature offset for...

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Published in	Journal of Geophysical Research Vol. 117; no. B2
Main Author	Gallagher, Kerry
Format	Journal Article
Language	English
Published	Washington, DC Blackwell Publishing Ltd 01.02.2012 American Geophysical Union
Subjects	Bayesian transdimensional MCMC Earth Sciences Earth, ocean, space Exact sciences and technology Geological time Geophysics inverse modelling Markov chains Mathematics Plate tectonics Probability distribution Sciences of the Universe thermochronology United States Markov chain analysis thermal history models fission tracks Monte Carlo analysis thermochronology Probability distribution North America Modeling Markov chain collections interpretation maximum likelihood Vertical profile temperature Likelihood function
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Abstract	A new approach for inverse thermal history modeling is presented. The method uses Bayesian transdimensional Markov Chain Monte Carlo and allows us to specify a wide range of possible thermal history models to be considered as general prior information on time, temperature (and temperature offset for multiple samples in a vertical profile). We can also incorporate more focused geological constraints in terms of more specific priors. The Bayesian approach naturally prefers simpler thermal history models (which provide an adequate fit to the observations), and so reduces the problems associated with over interpretation of inferred thermal histories. The output of the method is a collection or ensemble of thermal histories, which quantifies the range of accepted models in terms a (posterior) probability distribution. Individual models, such as the best data fitting (maximum likelihood) model or the expected model (effectively the weighted mean from the posterior distribution) can be examined. Different data types (e.g., fission track, U‐Th/He, 40Ar/39Ar) can be combined, requiring just a data‐specific predictive forward model and data fit (likelihood) function. To demonstrate the main features and implementation of the approach, examples are presented using both synthetic and real data. Key Points New method for quantifying thermal histories from multiple samples Transdimensional approach naturally prefers simpler models to explain the data Outputs are probability distributions on unknowns and fully characterise model
AbstractList	A new approach for inverse thermal history modeling is presented. The method uses Bayesian transdimensional Markov Chain Monte Carlo and allows us to specify a wide range of possible thermal history models to be considered as general prior information on time, temperature (and temperature offset for multiple samples in a vertical profile). We can also incorporate more focused geological constraints in terms of more specific priors. The Bayesian approach naturally prefers simpler thermal history models (which provide an adequate fit to the observations), and so reduces the problems associated with over interpretation of inferred thermal histories. The output of the method is a collection or ensemble of thermal histories, which quantifies the range of accepted models in terms a (posterior) probability distribution. Individual models, such as the best data fitting (maximum likelihood) model or the expected model (effectively the weighted mean from the posterior distribution) can be examined. Different data types (e.g., fission track, U-Th/He, 40Ar/39Ar) can be combined, requiring just a data-specific predictive forward model and data fit (likelihood) function. To demonstrate the main features and implementation of the approach, examples are presented using both synthetic and real data. A new approach for inverse thermal history modeling is presented. The method uses Bayesian transdimensional Markov Chain Monte Carlo and allows us to specify a wide range of possible thermal history models to be considered as general prior information on time, temperature (and temperature offset for multiple samples in a vertical profile). We can also incorporate more focused geological constraints in terms of more specific priors. The Bayesian approach naturally prefers simpler thermal history models (which provide an adequate fit to the observations), and so reduces the problems associated with over interpretation of inferred thermal histories. The output of the method is a collection or ensemble of thermal histories, which quantifies the range of accepted models in terms a (posterior) probability distribution. Individual models, such as the best data fitting (maximum likelihood) model or the expected model (effectively the weighted mean from the posterior distribution) can be examined. Different data types (e.g., fission track, U‐Th/He, 40 Ar/ 39 Ar) can be combined, requiring just a data‐specific predictive forward model and data fit (likelihood) function. To demonstrate the main features and implementation of the approach, examples are presented using both synthetic and real data. New method for quantifying thermal histories from multiple samples Transdimensional approach naturally prefers simpler models to explain the data Outputs are probability distributions on unknowns and fully characterise model A new approach for inverse thermal history modeling is presented. The method uses Bayesian transdimensional Markov Chain Monte Carlo and allows us to specify a wide range of possible thermal history models to be considered as general prior information on time, temperature (and temperature offset for multiple samples in a vertical profile). We can also incorporate more focused geological constraints in terms of more specific priors. The Bayesian approach naturally prefers simpler thermal history models (which provide an adequate fit to the observations), and so reduces the problems associated with over interpretation of inferred thermal histories. The output of the method is a collection or ensemble of thermal histories, which quantifies the range of accepted models in terms a (posterior) probability distribution. Individual models, such as the best data fitting (maximum likelihood) model or the expected model (effectively the weighted mean from the posterior distribution) can be examined. Different data types (e.g., fission track, U‐Th/He, 40Ar/39Ar) can be combined, requiring just a data‐specific predictive forward model and data fit (likelihood) function. To demonstrate the main features and implementation of the approach, examples are presented using both synthetic and real data. Key Points New method for quantifying thermal histories from multiple samples Transdimensional approach naturally prefers simpler models to explain the data Outputs are probability distributions on unknowns and fully characterise model
Author	Gallagher, Kerry
Author_xml	– sequence: 1 givenname: Kerry surname: Gallagher fullname: Gallagher, Kerry email: kerry.gallagher@univ-rennes1.fr organization: Géosciences, Université de Rennes 1, Rennes, France
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25982182$$DView record in Pascal Francis https://insu.hal.science/insu-00676497$$DView record in HAL
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Snippet	A new approach for inverse thermal history modeling is presented. The method uses Bayesian transdimensional Markov Chain Monte Carlo and allows us to specify a...
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SubjectTerms	Bayesian transdimensional MCMC Earth Sciences Earth, ocean, space Exact sciences and technology Geological time Geophysics inverse modelling Markov chains Mathematics Plate tectonics Probability distribution Sciences of the Universe thermochronology
Title	Transdimensional inverse thermal history modeling for quantitative thermochronology
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