Exploring the relationships between low-temperature thermochronometers, temperature-time histories, and geological processes using Tc1D
Bibliographic record
Abstract
Low temperature thermochronology is a field of research in which the thermally controlled retention of radioactive decay products in geological materials is measured to reconstruct mineral and rock temperature-time histories, especially in regard to their passage through the upper crust (i.e., <350 °C). Such temperature-time histories are most often constructed by inverting low temperature thermochronological data using geological constraints in order to identify envelopes of plausible rock thermal histories. While such inversions are highly informative models of the thermal history of rocks, the ultimate goal of most low temperature thermochronological studies is to relate thermal histories to geological processes in order to reconstruct upper crustal tectonic activity and/or landscape evolution. To do this, the (evolving) depths of thermochronometer effective closure temperatures must be estimated, as both heat transfer processes and crustal rock composition/thermal properties will affect the crustal thermal field. Here we present an exploration of the relationships between low temperature thermochronometers, temperature-time histories, and geological processes produced using the software Tc1D (https://doi.org/10.5281/zenodo.7124271). Tc1D is a new, open-source thermal and thermochronometer age prediction model for simulating the competing effects of tectonic and surface processes on thermochronometer ages. The Tc1D software is written in Python and uses the finite difference method to solve the heat transfer equation in 1D including the effects of heat conduction, advection (e.g., erosion, sedimentation), and radiogenic heat production on the thermal profile of the lithosphere. The flexibility of the software means that it can be used to explore the effects of a variety of geological processes, including magmatic intrusion and lithospheric delamination, for example. Thermochronometer ages (U-Th/He and fission track ages for apatite and zircon) are predicted by tracking the thermal history of rock particles in the model as they travel from depth to the surface during their exhumation history, both for samples at the modern-day surface and those reaching the surface at past times. The thermal histories are input to age prediction algorithms, including those that account for the effects of radiation damage in minerals (e.g., Flowers et al., 2009; Guenthner et al., 2013), making the software applicable to thermochronometer age interpretation in a wide variety of geological scenarios. In this contribution, we present a selection of results using Tc1D, demonstrating potential applications and providing some examples of unintuitive temperature and age relationships. These examples include cases where sample depth does not correlate with temperature, where variations in predicted effective closure temperatures produce unexpected age relationships, and where the thickness of the layer of exhumed rocks can significantly affect predicted ages. We hope that these illustrative examples demonstrate the role for Tc1D in the thermochronologist’s interpretational toolbox.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".