| 01 Jun 2026
Technical note: Disentangling pre- and post-depositional thermal histories in partially reset samples
Abstract. Interpreting thermochronological data of a partially reset detrital sample is challenging because its age distribution reflects a mixture of each grain’s individual pre-depositional and the shared post-depositional thermal history. A promising approach to meet this challenge is combined geo- and thermochronological dating on the same grains. We present an algorithm for disentangling the pre- and post-depositional thermal histories using data from zircon U/Pb-(U-Th)/He double-dating. It proceeds in three steps: (1) determining candidate post-depositional temperature-time paths by inverse thermal-history modeling of syn-depositional grains. (2) Calculating pre-depositional (U-Th)/He model ages for each candidate thermal history. (3) Evaluating the likelihood for each post-depositional history by testing if resulting pre-depositional model ages against the U-Pb crystallization ages of each grain and the timing of sediment deposition. We illustrate this strategy by applying it to a Devonian sandstone from the Northern Canadian Cordillera. The results show the general agreement of the modeled thermal histories and pre-depositional (U-Th)/He ages with existing thermochronological data. The temperature-time paths are also consistent with additional zircon-Raman thermochronological data from the same grains. We discuss the limitations of our approach imposed by the need for syn-depositional grains, its application to other thermochronometers, and how targeting rather than avoiding partially reset samples enhances our capability to extract thermal-history information from the detrital record.
Peer-review report
Title: Technical note: Disentangling pre- and post-depositional thermal histories in partially reset samples
Author(s): Birk P. Härtel et al.
MS No.: egusphere-2026-2376
General comment
This manuscript presents a new workflow for interpreting partially reset detrital zircon U/Pb–(U-Th)/He double-dating data by combining inverse thermal-history modeling of syn-depositional grains with forward modeling and a likelihood-based evaluation of pre-depositional model ages.
Overall, I found the manuscript to be scientifically sound, well-written and well-structured. The study addresses an important methodological problem in detrital thermochronology and provides a practical solution that is likely to be useful for future applications. The manuscript does not aim to introduce a fundamentally new thermochronological model but rather proposes an elegant integration of existing concepts into a coherent and reproducible workflow. In my opinion, this is fully appropriate for a Technical Note in GChron. The methodology is generally clearly presented, the synthetic example effectively illustrates the workflow, and the application to the Canadian Cordillera demonstrates that the approach produces geologically meaningful results consistent with previous independent studies. Importantly, the manuscript remains appropriately balanced regarding the capabilities and limitations of the method.
I did not identify conceptual inconsistencies or methodological flaws that would require major revisions. Most of my suggestions therefore concern clarification, presentation and readability.
I recommend publication after minor revision.
Main comments of the manuscript
Section 2 - Selection of the best-fitting thermal histories: The use of a knee-detection algorithm (Satopää et al., 2011) to define the likelihood cutoff is an interesting and apparently objective way of selecting acceptable thermal histories. However, this choice is introduced rather briefly. Since this algorithm may not be familiar to many readers in the thermochronology community, I encourage the authors to add one or two sentences explaining the rationale for using this approach and its advantages over a more conventional fixed likelihood threshold.
Section 3 - Practical guidance on selecting inversion grains: One practical aspect I wondered about concerns the initial selection of syn- or near-syndepositional grains used for the inverse thermal-history modeling (blue circles). While the manuscript explains how these grains are identified, it would be helpful if the authors could briefly comment on the extent to which the inferred post-depositional thermal histories are expected to depend on both the number and representativeness of the selected grains for the initial inversion. Even a short qualitative discussion would provide useful practical guidance for future users of the workflow.
Discussion / Conclusions: The manuscript clearly discusses several simplifying assumptions and possible improvements (e.g., U-Th zoning, radiation damage, He concentration profiles, laser-ablation geometry). It could nevertheless be worthwhile to conclude with a brief perspective on the future development of the PaRACAS workflow. A short paragraph outlining the main priorities for future versions of the code would nicely emphasize that the current implementation provides a flexible framework that can accommodate these refinements.
Minor Specific/technical comments
General: Most figures appear blurred or compressed, particularly when viewed at higher magnification. Providing higher-resolution versions for the final publication would substantially improve readability and the overall presentation quality.
Figure 1: The schematic pre-depositional thermal histories (grey paths) are all illustrated as monotonic cooling trajectories that eventually reach near-surface temperatures prior to deposition. While this is perfectly adequate as a conceptual illustration, I wondered whether this representation might inadvertently suggest that the proposed workflow assumes monotonic pre-depositional cooling or complete exhumation of all source rocks prior to deposition. One of the main strengths of the method, as I understand it, is precisely that it does not require prescribing a specific pre-depositional thermal history. It may therefore be helpful to briefly clarify in the text or figure caption that these grey paths are intended only as illustrative examples and are not meant to represent a methodological constraint.
Figure 2: The figure is very informative and successfully highlights the differences between the three workflows. If possible, the authors may consider harmonizing the time-axis scales across some of the thermal-history panels or explicitly emphasizing why different time windows are shown. This could facilitate visual comparison between the different approaches. 2f: Unless I overlooked it, the meaning of the vertical grey lines is not explained in either the figure caption or the main text. I assume they indicate the individual U-Pb ages of the synthetic grains, but a brief explanation in the caption or in the text would make the figure more self-contained.
Lines 83-85: The authors may also consider citing the following field-based study, which documents the influence of radiation damage on effective ZHe closure temperatures:
Gérard, B., Robert, X., Grujic, D., Gautheron, C., Audin, L., Bernet, M., Balvay, M., 2022. Zircon (U-Th)/He Closure Temperature Lower Than Apatite Thermochronometric Systems: Reconciliation of a Paradox. Minerals 12, 145. https://doi.org/10.3390/min12020145
Line 93: Typo? According to Supplementary Table T1 and Figure 2, the minimum grain radius is 38 μm, not 36 μm.
Line 151: Typo. "each of theses paths" >> each of these paths
Line 176: Grammar. "confirms the validity this approach" >> confirms the validity of this approach
Line 216: Grammar. "25 out 30 grains" >> 25 out of 30 grains
Line 224: Typo. "expected envelop" >> expected envelope
Line 504: Typo. "misift" >> misfit
Check consistency of forward-modeling vs. forward modeling. Both spellings appear throughout the manuscript. Likewise, check consistency between temperature-time (T–t) and T-t.
Notebook documentation:
Typo in “1 Load functions”: "kneed an seaborn" >> "kneed and seaborn". The installation example could also be updated to include both required packages (e.g., pip install kneed seaborn).
Run 1 Load functions: The notebook runs correctly, but it raises a minor deprecation warning when importing display and HTML. Replacing “from IPython.core.display import display, HTML” with “from IPython.display import display, HTML” would avoid this warning in recent IPython versions.
Plot modeling results: The notebook runs well, but I encountered a matplotlib compatibility issue when plotting the modeling results. The error occurs when creating the colorbar in Visualization.py (plt.colorbar(mappable)), with recent versions of matplotlib requiring an explicit axis argument. Replacing this with, for example, plt.colorbar(mappable, ax=plt.gca()) appears to solve the issue. This is a minor technical point.
Plots for zircon raman: The variable axis_range_model_ZR is used in the plotting section but is not defined. It appears that axis_range_model_ZHe should instead be renamed to axis_range_model_ZR. This is a minor typo, but it currently raises a NameError when executing the notebook.
Abrasion helper: The default pit-geometry values are set to zero (r0 = 0, h = 0), which produces NaN values if the cell is run without modification. It may be helpful to either use realistic example values or add a warning that non-zero pit dimensions are required.
Benjamin Gérard