Thermal history modeling techniques and interpretation strategies: Applications using QTQt

Abstract

Abstract Advances in low-temperature thermochronology have made it applicable to a plethora of geoscience investigations. The development of modeling programs (e.g., QTQt and HeFTy) that extract thermal histories from thermochronologic data has facilitated growth of this field. However, the increasingly wide range of scientists who apply these tools requires an accessible entry point to thermal history modeling and how these models develop our understanding of complex geological processes. This contribution offers a discussion of modeling strategies, using QTQt, including making decisions about model design, data input, kinetic parameters, and other factors that may influence the model output. We present a suite of synthetic data sets derived from known thermal histories with accompanying tutorial exercises in the Supplemental Material1. These data sets illustrate the opportunities and limitations of thermal history modeling. Examining these synthetic data helps to develop intuition about which thermochronometric data are most sensitive to different thermal events and to what extent user decisions on data handling and model setup can control the recovery of the true solution. We also use real data to demonstrate the importance of incorporating sensitivity testing into thermal history modeling and suggest several best practices for exploring model sensitivity to factors including, but not limited to, the model design or inversion algorithm, geologic constraints, data trends, the spatial relationship between samples, or the choice of kinetics model. Finally, we provide a detailed and explicit workflow and an applied example for a method of interrogating vague model results or low observation-prediction fits that we call the “Path Structure Approach.” Our explicit examination of thermal history modeling practices is designed to guide modelers to identify the factors controlling model results and demonstrate reproducible approaches for the interpretation of thermal histories.