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Transdimensionalinverse thermalhistory modeling forquantitativethermochronology Kerry Gallagher2012 год

Transdimensional inverse thermal history modeling for quantitative thermochronology
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Аннотация: 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.
Год издания: 2012
Авторы: Kerry Gallagher
Издательство: American Geophysical Union
Источник: Journal of Geophysical Research Atmospheres
Ключевые слова: Hydrocarbon exploration and reservoir analysis, Geochemistry and Geologic Mapping, NMR spectroscopy and applications
Другие ссылки: Journal of Geophysical Research Atmospheres (HTML)
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