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Abstract:
To understand the exhumation history of orogens and their fold-thrust belts, it is important to accurately reconstruct their time-temperature evolution. This is often done by employing thermokinematic models. One problem of current approaches is that they are limited in prescribing geometric constraints only as far as they affect transient thermal conditions. This often results in 2-D plane strain assumptions, and a simple treatment of structural and kinematic uncertainties. In this work, we combine 3-D kinematic forward modeling with a random sampling approach to automatically generate an ensemble of kinematic models in the range of assigned geometric uncertainties. Using Markov Chain Monte Carlo, each randomly generated model is assessed regarding how well it fits the available paleo-depth data taken from low-temperature thermochronology. The resulting, more robust model can then be used to re-interpret the thermal resetting data. We first apply this method to synthetic experiments with variable structural complexity and sample uncertainties, and later to the Alpine fold-thrust belt, the Subalpine Molasse. Results show that it is possible to use thermochronological data to make predictions about exhumation, which can be translated into likelihood functions to obtain the range of 3-D kinematic forward models explaining the data. Though the method performs well for the synthetic models, additional thermochronological parameters may need to be considered to improve the inversion results for structurally complex settings. The method is useful, however, to study alternative mechanisms of exhumation for the thermochronological samples that are not respected by the modeling, even when uncertainty is considered.
