Thermal history modelling: HeFTy vs. QTQt

Abstract

a b s t r a c tHeFTy is a popular thermal history modelling program which is named after a brand of trash bags as a reminder of the 'garbage in, garbage out' principle. QTQt is an alternative program whose name refers to its ability to extract visually appealing ('cute') time-temperature paths from complex thermochronological datasets. This paper compares and contrasts the two programs and aims to explain the algorithmic underpinnings of these 'black boxes' with some simple examples. Both codes consist of 'forward' and 'inverse' modelling functionalities. The 'forward model' allows the user to predict the expected data distribution for any given thermal history. The 'inverse model' finds the thermal history that best matches some input data. HeFTy and QTQt are based on the same physical principles and their forward modelling functionalities are therefore nearly identical. In contrast, their inverse modelling algorithms are fundamentally different, with important consequences. HeFTy uses a 'Frequentist' approach, in which formalised statistical hypothesis tests assess the goodness-of-fit between the input data and the thermal model predictions. QTQt uses a Bayesian 'Markov Chain Monte Carlo' (MCMC) algorithm, in which a random walk through model space results in an assemblage of 'most likely' thermal histories. In principle, the main advantage of the Frequentist approach is that it contains a built-in quality control mechanism which detects bad data ('garbage') and protects the novice user against applying inappropriate models. In practice, however, this quality-control mechanism does not work for small or imprecise datasets due to an undesirable sensitivity of the Frequentist algorithm to sample size, which causes HeFTy to 'break' when datasets are sufficiently large or precise. QTQt does not suffer from this problem, as its performance improves with increasing sample size in the form of tighter credibility intervals. However, the robustness of the MCMC approach also carries a risk, as QTQt will accept physically impossible datasets and come up with 'best fitting' thermal histories for them. This can be dangerous in the hands of novice users. In conclusion, the name 'HeFTy' would have been more appropriate for QTQt, and vice versa.

Miocene Range Growth Along the Altyn Tagh Fault: Insights From Apatite Fission Track and (U‐Th)/He Thermochronometry in the Western Danghenan Shan, China

“…We use QTQt (v 5.4.0; Gallagher, ) to further investigate the thermal evolution of samples in the western Danghenan Shan. QTQt extracts time–temperature paths from complex thermochronological data sets based on the Bayesian transdimensional Markov chain Monte Carlo inversion scheme (Gallagher, ; Vermeesch & Tian, ). AFT ages and projected fission track lengths with D par as kinetic parameter are included in the fission track annealing model of Ketcham et al ().…”

Synorogenic morphotectonic evolution of the Gangdese batholith, South Tibet: Insights from low‐temperature thermochronology

“…To better constrain the thermal history of samples, the results from AFT, ZHe and AHe analyses were modeled in combination where possible using the inverse approach of HeFTy program [ Ketcham , ], which uses a Kolmogorov‐Smirnov (K‐S) test to compare the predictions of a thermal history model to observations [ Ketcham , ], thus ruling out the use of inappropriate models [ Vermeesch and Tian , ].…”

Unraveling the Mesozoic and Cenozoic Tectonothermal Evolution of the Eastern Basque‐Cantabrian Zone–Western Pyrenees by Low‐Temperature Thermochronology

“…In order to constrain the time‐temperature ( t ‐ T ) history, forward and inverse modeling of thermochronological ages and track length distributions were performed with two programs: HeFTy (Ketcham, ) and QTQt (Gallagher, ). These thermochronological modeling programs use different approaches, each one with its strengths and weaknesses (see discussions in Flowers et al, , ; Gallagher, ; Gallagher & Ketcham, ; Vermeesch & Tian, , ). For forward modeling we used the HeFTy code and the zircon radiation‐damage annealing model of Guenthner et al ().…”