QTQt is a program to infer thermal histories from low temperature thermochronology data using multiple samples. The name comes from QT being Quantitative Thermochronology and Qt (pronounced as cute or cutie) being the software used to develop the user interface. QTQt is currently implemented for apatite and zircon fission track, apatite (U-Th)/He data and vitrinite reflectance, although future versions should include Argon data too. You can enter you own kinetics for any mineral/isotope system combination (e.g. to simulate zircon (U-Th)/He or mica argon data, subject to the requirement that the diffusion domain can be treated as a single sphere, whose size, or equivalent dimesions of a rectangular crystal need to be sepcified also). Also, the current version allows for multiple sample modelling only if the samples under consideration have the same form of thermal history, i.e. can be treated as a vertical profile (see Gallagher et al 2005). You can still model a single sample, but for generality we will still refer to a profile (even if there is just one sample). A future version will include the 3D partition model-vertical profile approach developed by Stephenson et al. (2006).

The program uses the multicompositional algorithms of Ketcham et al (1999, 2007) and the original Durango apatite-based algorithm of Laslett et al. (1987) for predicting fission track annealing in apatite, and those of Tagami et al (1998) and Yamada (2007) for zircon. Currently, a given sample can be modelled with a constant composition (if appropriate). The composition could be taken as the average of measured single grain compositions or alternatively a single real sample could be divided up into multiple samples based on different compositions for example amd then treated as mutliple sample for modelling purposes. Similarly a sample with both apatite and zircon data should be treated as 2 samples. For predicting He diffusion, standard diffusion equations are used (explicitly a spherical grain with the same surface area to volume ratio as the dimensions specified for a real grain). You can input kinetic parameters to simulate He diffusion in any mineral (zircon for example). The He diffusion model also includes the recent developments on radiation damage trapping (Flowers et al. 2009, Gautheron et al. 2010), using fission track annealing as a proxy to recalibrate the helium diffusion coefficient. It is possible also to include 4 He/3He degassing spectrum as part of the He data modelling process. Finally, vitrinite reflectance data can be incorporated, being used either as a direct constraint on the inferred thermal histories, or it is possible just to predict vitrinite reflectance and make a qualititative comparison to the observed values.

The inversion scheme is Bayesian transdimensional Markov chain Monte Carlo (MCMC), in which the number of time temperature points (or the complexity of the thermal history solutions are inferred from the data rather than being specified in advance). The development of the method for thermal history modelling is given in Gallagher (2012), and some other relevant publications are Gallagher et al. (2009), Charvin et al. (2009), Hopcroft et al. (2007) and Sambridge et al (2006). The approach as implemented in QTQt allows the user to specify one general time-temperature box, from which time-temperature points are sampled to construct a continuous thermal history by linear interpolation between the sampled points. It also allows for up to 5 additional time-temperature boxes to be specified to allow the user to add more specific constraints on the thermal history.

Figure 1. Sample result.

A program user guide in PDF form is available:

QTQt User Guide v5Red.pdf 1.7MB.

The code is not available here however it can be downloaded from the author's web site using these instructions:

Accessing QTQt and BayesMixQT for Windows or Macintosh.pdf 670KB.


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