Thermal history modeling techniques and interpretation strategies: Applications using QTQt

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 set-up 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.

Magma‐Assisted Continental Rifting: The Broadly Rifted Zone in SW Ethiopia, East Africa

“…QTQt employs a Bayesian trans‐dimensional Markov Chain Monte Carlo algorithm to search time‐temperature paths that are compatible with observed data based on a posterior probability, whereas HeFTy employs a Frequentist approach using a random, non‐learning Monte Carlo time‐temperature search algorithm to determine thermal histories that agree with observed data based on a goodness‐of‐fit statistical test. The contrasting modeling and visualization outputs from QTQt and HeFTy each have their own unique advantages (cf., Abbey et al., 2023; Gallagher & Ketcham, 2018; Murray et al., 2022; Vermeesch & Tian, 2014), which we strategically exploited through a systematic and iterative modeling approach to thoroughly assess and interpret our (U‐Th)/He data set.…”

“…For each QTQt modeling result, we estimated the onset of rapid cooling and its uncertainty (earliest and latest possible onset of rapid cooling relative to each other; e.g., Abbey et al., 2023; Murray et al., 2022) by objectively defining inflection points (i.e., rapid cooling start times, Figure 3e) from the 95% confidence envelope (e.g., Balestrieri et al., 2016). When either of the inflection points was poorly expressed, defining the earliest or the latest possible onset of rapid cooling was aided by the corresponding relative probability distribution plot from the QTQt modeling results.…”

Thermochronological Evidence for Eocene Deformation in the Southern Patagonian Andes: Linking Orogenesis Along the Patagonian Orocline

“…Thermal history modeling provides a quantitative comparison between the measured thermochronology data sets reported above—including cooling age (ZFT, ZHe), grain size (ZHe), and eU concentration (ZHe)—and possible time temperature histories (Abbey et al., 2023; Murray et al., 2022; Wolf et al., 1998). The thermochronometric cooling ages in this study collectively span (Aptian) Late Cretaceous through Miocene cooling (Figure 5), suggesting that modeling this data set can evaluate thermal histories during the Paleogene.…”

Phanerozoic Burial and Erosion History of the Southern Canadian Shield from Apatite (U-Th)/He Thermochronology

“…Several approaches and software programs are available for inverse thermal history modeling to decipher the time-temperature (tT) paths that can explain both the thermochronology and geologic data in a thermochronology study. The choice of strategy is affected by the probable complexity of the tT path and the geologic context [60,[66][67][68][69]. We chose to use HeFTy [70] because geologic and geochronologic data can be easily incorporated into inverse thermal history simulations, making it effective for the hypothesis testing approach we employed [60].…”