New Open-Source Tool Turns Rock Cooling Ages into Rates of Geological Change

Every mountain range keeps a hidden diary. Written into the crystals of apatite and zircon grains is a record of when rocks last cooled below specific temperatures deep within the Earth, typically somewhere between 50 and 400 degrees Celsius. Geoscientists read this diary using thermochronology, a dating method that reveals how fast plate tectonics and erosion have reshaped the planet over millions of years. But the diary is maddeningly ambiguous: many different temperature histories can produce exactly the same age. A team of researchers at the University of Helsinki and the Geological Survey of Canada-Atlantic has now released a new software package designed to cut through that ambiguity, linking cooling ages directly to the geological processes that produced them in a framework almost anyone with a laptop can use.

The software, called Tc1D, is described in a technical note published in the journal Geochronology by David M. Whipp, Benjamin Gérard, Sanni Laaksonen, and Dawn A. Kellett. It is open source, written mainly in Python, and fills a conspicuous gap in the toolbox of thermochronology. Existing programs tend to cluster at two ends of a spectrum. At one end sit user-friendly thermal history packages such as HeFTy and QTQt, which can infer a rock’s temperature path through time but offer only limited insight into the rates of the processes responsible, and which are not open source. At the other end sits Pecube, a powerful three-dimensional thermo-kinematic model that can translate ages into exhumation rates but demands deep computational expertise, software compilation skills, and often access to high-performance computing clusters.

Tc1D aims for the middle ground. It uses a one-dimensional geometry, tracking heat and rock movement only in the vertical direction, which makes it computationally light and conceptually simpler than its three-dimensional cousins while still fundamentally connecting thermal histories to rates of exhumation, burial, magmatic heating, or other crustal processes. The core of the model solves the transient heat transfer equation in one dimension, including heat advection by moving rock, conduction, and heat production from radioactive decay. Rock properties such as density, heat capacity, thermal conductivity, and heat production can vary among the crust, mantle lithosphere, and asthenosphere, and crustal heat production can decay exponentially with depth to mimic the concentration of heat-producing elements in upper crustal rocks.

The way the model tracks material is elegantly simple. Grid nodes stay fixed in space while a user-selected erosion model defines a vertical advection velocity. A virtual particle that ends at the surface at the end of the simulation has its starting depth back-calculated by running the erosion model backwards in time, and its depth, pressure, and temperature are then recorded forward through every time step. Those recorded thermal histories feed into age prediction routines for four widely used thermochronometer systems: apatite and zircon (U–Th)/He and apatite and zircon fission tracks. Crucially, the (U–Th)/He predictions incorporate the effects of radiation damage and its annealing through the RDAAM and ZRDAAM models, a sophistication that simpler tools such as age2exhume, which relies on Dodson’s classic closure temperature approach, cannot offer.

Accessibility was a deliberate design choice. Python was selected because it is among the most widely used programming languages in the world and is commonly taught across the natural and data sciences. Models can be run from a Python interpreter, a script, a Jupyter Notebook, or a command-line interface, and a human-readable YAML input file centralizes model parameters in a way that makes complete experiments easy to archive, share, and rerun, bolstering reproducibility. For newcomers, the documentation site links to an interactive Jupyter environment that runs in a web browser on a computer, tablet, or even a smartphone, with no software installation required. The authors describe the package as complementary to existing tools rather than a replacement for them.

The paper walks through four illustrative examples that showcase what the model can do. In the first, a two-kilometer-thick pluton emplaced at 1000 degrees Celsius between 8 and 10 kilometers depth at 30 million years ago cools conductively before the entire crust is exhumed at 0.9 kilometers per million years from 10 million years ago to the present. Only the zircon fission-track system records cooling tied directly to magmatism, yielding an age of roughly 24 million years, while the lower-temperature apatite and zircon helium and fission-track systems all record the later exhumation phase, with predicted ages between about 7 and 3 million years. The example neatly illustrates both the sensitivity of low-temperature chronometers to magmatic heating and the interpretive challenge posed by overlapping thermal events.

The second example simulates the exhumation of a 10-kilometer-thick thrust sheet emplaced at 30 million years ago and eroded along with five kilometers of footwall rock over the following 30 million years. By predicting ages for particles reaching the surface every million years, the model reveals a striking pattern: as erosion strips away the thrust sheet and exposes footwall rocks at 10 million years ago, apatite fission-track and zircon helium ages suddenly jump because insufficiently reset rocks are brought to the surface. The authors note that such age jumps, if preserved in foreland basin sediments, could constrain the timing and thickness of thrust emplacement in convergent tectonic settings, a potentially powerful new signature for tectonic studies.

The third example tackles deep time. Using a ten-stage burial and exhumation history based on published work on Canada’s Slave Craton, the model reproduces measured apatite (U–Th)/He ages spanning hundreds of millions of years and refines a key geological inference. Previous work suggested that Ordovician to Devonian burial exceeded three kilometers; the Tc1D simulations show that at least five kilometers of burial is required to fully reset the apatite helium and fission-track ages, since reducing burial to 4.5 kilometers produces only partially reset ages. The fourth example demonstrates the software’s Bayesian inverse mode on a natural dataset from southern Peru, inverting four thermochronometer systems from a single granite sample and recovering an acceleration of erosion around 2 million years ago, in good agreement with independent time-temperature modeling.

That inverse capability is one of the package’s most significant features. Users can search parameter space with either the Neighborhood Algorithm, which is computationally efficient but sensitive to local minima, or an affine invariant Markov chain Monte Carlo sampler implemented in the emcee library, which provides more rigorous characterization of posterior distributions at higher computational cost and can run in parallel using MPI. The software produces chain plots, pairwise scatter plots, and corner plots to diagnose convergence and visualize parameter trade-offs. A batch mode built on the scikit-learn ParameterGrid function also allows systematic sensitivity analysis, running every permutation of selected parameters and logging results to a file for comparison.

The authors are candid about limitations. The one-dimensional framework cannot capture lateral heat transfer from dipping faults or topographic perturbations of isotherms, so the tool is best suited to regions with limited relief and predominantly vertical rock motion, such as tectonically quiescent areas, rather than active fold-and-thrust belts or rugged mountain ranges. Boundary temperatures are currently fixed in time, and the model treats each sample as a single vertical column without spatial relationships between samples. Future development plans address several of these gaps, including a flexible multi-stage erosion framework, a trans-dimensional Markov chain Monte Carlo approach that can infer the number of erosion stages directly from data, topographic corrections, improved handling of sedimentary burial in basins, and a broader library of age prediction models. For now, the package is installable via pip or runnable directly in the browser, putting process-rate thermochronology within reach of students and seasoned researchers alike.

Subject of Research: A one-dimensional thermal and thermochronometer age prediction model for interpreting geological process rates

Article Title: Technical note: Tc1D – a 1D thermal and thermochronometer age prediction model

Article References: Technical note: Tc1D – a 1D thermal and thermochronometer age prediction model. (n.d.). https://doi.org/10.5194/gchron-8-627-2026

Image Credits: AI Generated

DOI: 10.5194/gchron-8-627-2026

Keywords: thermochronology, Tc1D, geochronology, numerical modeling, exhumation rates, thermal history, apatite (U-Th)/He, fission-track dating, open-source software, Python, inverse modeling, crustal heat transfer