Thermal history modeling techniques and interpretation strategies: Applications using HeFTy

Abstract

Advances in low-temperature thermochronology, and the wide range of geologic problems that it is used to investigate, have prompted the routine use of thermal history (time-temperature, tT) models to quantitatively explore and evaluate rock cooling ages. As a result, studies that investigate topics ranging from Proterozoic tectonics to Pleistocene erosion now commonly require a substantial numerical modeling effort that combines the empirical understanding of chronometer thermochemical behavior (kinetics) with independent knowledge or hypotheses about a study area’s geologic history (geologic constraints). Although relatively user-friendly programs, such as HeFTy and QTQt, are available to facilitate thermal history modeling, there is a critical need to provide the geoscience community with more accessible entry points for using these tools. This contribution addresses this need by offering an explicit discussion of modeling strategies in the program HeFTy. Using both synthetic data and real examples, we illustrate the opportunities and limitations of thermal history modeling. We highlight the importance of testing the sensitivity of model results to model design choices and describe a strategy for classifying model results that we call the Path Family Approach. More broadly, we demonstrate how HeFTy can be used to build an intuitive understanding of the thermochronologic data types and model design strategies that are capable of discriminating among geologic hypotheses.

Thermal history modeling techniques and interpretation strategies: Applications using QTQt

“…This manuscript is designed to aid modelers in developing a path forward in these situations in QTQt. In concert with Murray et al (2022), here we reiterate the conclusions of Ketcham (2018, 2020) and describe specific functionalities and modeling strategies for QTQt, so that modelers are equipped to choose the best approach for their own data sets.…”

“…Here, we use those same t-T paths from Wolf et al (1998), plus one additional Path 6, but apply the Flowers et al (2009) radiation damage accumulation and annealing model (RDAAM) during forward and inverse modeling, and as such, we do not obtain a 40 Ma age for a 60 µm grain for all six thermal histories (as the AHe age will be different depending on the eU concentration of the grain). Murray et al (2022) present minor adjustments to the t-T paths such that they each produce a 40 Ma age for a Durango-like 60 ppm [eU] apatite with a grain size of 60 µm using the RDAAM (see table 1 in Murray et al, 2022).…”

“…The ExTH should reflect the true solution where it is well resolved with tight credible intervals. However, because the ExTH is a weightedmean model, parts of the ExTH path could be the product of individual paths with very different behaviors (see our "Path Structure Approach" and the "Path Family Approach" from Murray et al, 2022). For example, there may be thermal histories that predict reheating over a given interval and others that predict cooling over the same interval, and therefore the ExTH suggests a scenario in between.…”

Low‐Temperature Thermochronologic Response to Magmatic Reheating: Insights From the Takab Metallogenic District of NW Iran, (Arabia‐Eurasia Collision Zone)

“…Therefore, the two models may have a different influence on the same LTT system. To test if: (a) the modeled thermal perturbations could reset LTT data and (b) reheating could explain our LTT ages, we designed a forward model with HeFty (Ketcham, 2005; see Murray et al., 2022 and references therein for a recent review). The forward model is based on geological constraints and observations reported below.…”

“…The advance of low‐temperature thermochronology (LTT) and associated modeling techniques has significantly improved our ability to determine the timing and rates of near‐surface geological processes and to document spatio‐temporal patterns of erosional/tectonic exhumation (e.g., Braun et al., 2012; Gallagher, 2012; Gallagher & Parra, 2020; Ketcham, 2005; Ketcham et al., 2018; Murray et al., 2022; Willett et al., 2021). However, the interpretation of LTT data sets in regions characterized by widespread and prolonged magmatic activity, can be complicated by transient episodes of heating and cooling (i.e., reheating) that could be misinterpreted as episodes of burial and exhumation, respectively (e.g., Murray et al., 2018).…”