(U-Th)/He chronology: Part 2. Considerations for evaluating, integrating, and interpreting conventional individual aliquot data

Abstract

The (U-Th)/He dating technique is an essential tool in Earth science research with diverse thermochronologic, geochronologic, and detrital applications. It is now used in a wide range of tectonic, structural, petrological, sedimentary, geomorphic, volcanological, and planetary studies. While in some circumstances the interpretation of (U-Th)/He data is relatively straightforward, in other cases it is less so. In some geologic contexts, individual analyses of the same mineral from a single sample are expected to yield dates that differ well beyond their analytical uncertainty owing to variable He diffusion kinetics. Although much potential exists to exploit this phenomenon to decipher more detailed thermal history information, distinguishing interpretable intra-sample data variation caused by kinetic differences between crystals from uninterpretable overdispersion caused by other factors can be challenging. Nor is it always simple to determine under what circumstances it is appropriate to integrate multiple individual analyses using a summary statistic such as a mean sample date or to decide on the best approach for incorporating data into the interpretive process of thermal history modeling. Here we offer some suggestions for evaluating data, attempt to summarize the current state of thinking on the statistical characterization of data sets, and describe the practical choices (e.g., model structure, path complexity, data input, weighting of different geologic and chronologic information) that must be made when setting up thermal history models. We emphasize that there are no hard and fast rules in any of these realms, which continue to be an important focus of improvement and community discussion, and no single interpretational and modeling philosophy should be forced on data sets. The guiding principle behind all suggestions made here is for transparency in reporting the steps and assumptions associated with evaluating, integrating, and interpreting data, which will promote the continued development of (U-Th)/He chronology.

Thermal history modeling techniques and interpretation strategies: Applications using HeFTy

“…2B). The AHe ages predicted for these paths have variability that is ≤15% of the standard deviation of the mean age, which is well within the range that we define as "reproducible" for He thermochronometers (Flowers et al, 2015; see also Flowers et al, 2022). Path 2 yields ages of 31-52 Ma (18% variability).…”

“…The details of these examples are tailored to HeFTy's approach to searching tT space, to the fact that HeFTy inversion results are suites of individual tT histories that each fit the data, and to the important role of constraint boxes in controlling HeFTy model results. However, many of the approaches are applicable to other thermal history modeling programs and support other recent discussions of thermal history modeling practices (e.g., Fox and Carter, 2020;Flowers et al, 2022). We also emphasize that there are many appropriate approaches to handling thermal history modeling results; so, one's preferred method will depend on the data and modeling tools available as well as the geologic problem(s) of interest.…”

“…Thus, although there is general agreement about the importance of conducting tT modeling with careful consideration of thermochronometer kinetics, inversion algorithms, statistical methods, model design, data input, and geologic context, exactly how this should be done is extensively debated (e.g., Ketcham, 2018, 2020;Green and Duddy, 2021). Although valuable recommendations have been made about the reporting of modeling results (Flowers et al, 2015(Flowers et al, , 2016(Flowers et al, , 2022Gallagher, 2016), most thermochronologic studies do not provide explicit descriptions of their modeling practices and interpretation strategies. The absence of these details in the published literature means that many new and experienced modelers must reconstruct or reinvent modeling and interpretation strategies for themselves.…”

Existing thermochronologic data do not constrain Snowball glacial erosion below the Great Unconformities

“…1 ). Different scenarios fit some results better at the expense of others, with data scatter stemming from imperfect knowledge of diffusion and annealing kinetics, and likely other factors influencing the results ( 4 ).…”

“…Reducing the vast parameter space ( Fig. 1 , Right ) requires integrating critical geologic data in targeted localities ( 1 , 4 ) to discern the timing, magnitude, spatial extent, and mechanistic drivers of the Great Unconformities.…”