Transdimensional inverse thermal history modeling for quantitative thermochronology

• 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
• ISSN: 0148-0227; 2169-9313; 2156-2202; 2169-9356
• DOI: 10.1029/2011JB008825

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