Navigating the modelling puzzle: Using forward and inverse models to
make clear decisions when exploring and interpreting cooling ages in
both HeFTy and QTQt.
Abstract
Numerical thermal history modelling has become a core approach used for
interpretation of low-temperature thermochronometry data. Modelling
programs can find rock time-temperature (t-T) paths that fit the input
data while incorporating independent geologic information about a
sample’s history and leveraging the factors that impact the kinetics of
each thermochronometric system (e.g., grain size, radiation damage, and
composition). HeFTy (Ketcham, 2005) and QTQt (Gallagher, 2012) are two
of the commonly used tools for both forward and inverse t-T modeling.
The modelling process involves making key decisions about (i) data
input, (ii) initial set-up of model space and parameters, (iii) kinetic
model(s) (i.e. annealing, diffusion, radiation damage), and (iv)
additional t-T constraints. In addition, users need to have an
understanding of the statistical methods underlying the modelling
approach to be able to interpret the model outputs and the relationship
between the observed and predicted data. However, these modeling tools
currently lack clear and accessible entry-points for all
users—experienced and new thermochronologists alike—and thus for
many geoscientists, there is a substantial barrier to the modeling,
interpretation, and publication of thermochronologic datasets. Here we
present a suite of simple forward and inverse models that we recommend
everyone perform before embarking on t-T modeling in HeFTy and/or QTQt
for the first time. At the core of the exercises are the six different
t-T paths used by Wolf et al. (1998) to illustrate the partial-retention
behavior of the apatite He system; however, this approach can easily be
applied to other systems as well. This exercise not only illustrates the
fundamental behavior of thermochronologic systems but also guides users
through the main functionality of the modelling programs. Despite the
apparent simplicity of this exercise, users will experience most of the
challenges and opportunities common to thermal history modeling,
including: how to enter data; error handling; how to use geologic
constraints in t-T space; the non-unique nature of cooling ages; the
power of grain size and eU variability; the limitations on a model’s
ability to resolve the ‘right’ rock thermal history; and how to evaluate
the sensitivity of model results to all these factors. These exercises
were introduced in the Thermo2020/1 Sunday workshops for both QTQt and
HeFTy and are more fully fleshed out in two publications currently in
preparation.