the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
02 Aug 2021
02 Aug 2021
Simulating sedimentary burial cycles – Part 2: Elemental-based multikinetic apatite fission-track interpretation and modelling techniques illustrated using examples from northern Yukon
- 1Natural Resources Canada, Geological Survey of Canada, Calgary, AB, T2L 2A7, Canada
- 2Department of Earth Sciences, Dartmouth College, Hanover NH, 03755, United States
- 3GeoSep Services, Moscow, ID, 83843, United States
- 1Natural Resources Canada, Geological Survey of Canada, Calgary, AB, T2L 2A7, Canada
- 2Department of Earth Sciences, Dartmouth College, Hanover NH, 03755, United States
- 3GeoSep Services, Moscow, ID, 83843, United States
Abstract. Compositionally dependent apatite fission track (AFT) annealing is a common but underappreciated cause for age dispersion in detrital AFT samples. We present an interpretation and modelling strategy that exploits multikinetic AFT annealing to obtain thermal histories that can provide more detail and better resolution compared to conventional methods. We illustrate our method using a Permian and a Devonian sample from the Yukon, Canada, both with complicated geological histories and long residence times in the AFT partial annealing zone. Effective Cl values (eCl; converted from rmr0 values), derived from detailed apatite elemental data, are used to define AFT statistical kinetic populations with significantly different total annealing temperatures (~110–245 °C) and ages that agree closely with the results of age mixture modelling. These AFT populations are well-resolved using eCl values but exhibit significant overlap with respect to the conventional parameters, Cl content or Dpar. Elemental analyses and measured Dpar for Phanerozoic samples from the Yukon and Northwest Territories confirm that Dpar has low precision and that Cl content alone cannot account for the compositional and associated kinetic variability observed in natural samples. An inverse multikinetic AFT model, AFTINV, is used to obtain thermal history information by simultaneously modelling multiple kinetic populations as distinct thermochronometers with different temperature sensitivities. A nondirected Monte Carlo scheme generates a set of statistically acceptable solutions at the 0.05 significance level and then these solutions are updated to the 0.5 level using a controlled random search (CRS) learning algorithm. The smoother, closer-fitting CRS solutions allow for a more consistent assessment of the eCl values and thermal history styles that are needed to satisfy the AFT data. The high-quality Devonian sample (39 single grain ages and 202 track lengths) has two kinetic populations that require three cycles of heating and cooling (each subsequent event of lower intensity) to obtain close-fitting solutions. The younger and more westerly Permian sample with three kinetic populations only records the latter two heating events. These results are compatible with known stratigraphic and thermal maturity constraints and the QTQt software produces similar results. Model results for these and other samples suggest that elemental-derived eCl values are accurate within the range, 0–0.25 apfu (rmr0 values of 0.73–0.84), which encompasses most of the data from annealing experiments. Outside of this range, eCl values for more exotic compositions may require adjustment relative to better constrained apatite compositions when trying to fit multiple kinetic populations. Our results for natural and synthetic samples suggest that an element-based multikinetic approach has great potential to increase the temperature range and resolution of thermal histories dramatically relative to conventional AFT thermochronology.
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Dale R. Issler et al.
Status: final response (author comments only)
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RC1: 'Comment on gchron-2021-22', Richard A. Ketcham, 27 Sep 2021
This is a very nice demonstration of a multi-kinetic approach to apatite fission-track (AFT) modeling; it should be of great interest to the community, and I strongly recommend its publication. Some revisions are necessary, however, with respect to some of the method presentation and discussion, and possibly how total annealing temperatures are calculated.
Multi-kinetic effects in AFT thermochronology have long been neglected by much of the community, I gather in large part because it entails more trouble and expense to acquire sufficient compositional data, and the rewards are unclear, especially since thermal history inversion software will often produce a result without it. Hopefully this paper, and others from this group, will bend the curve. At the same time, to be effective in doing so (or at least transparent in trying), it would be good to better document the costs. For example, how long does the EMPA protocol take per spot? What considerations went into decision(s) of whether to do compositional analysis first versus laser ablation? Does doing laser ablation first save time, by figuring out which grains work and providing evidence of whether there is kinetic dispersion, and does this outweigh the disadvantage of not getting the analysis precisely where the tracks were measured? Are there cases where changing the order would be a good idea? Provide the reader with more data and use cases to enable a cost-benefit analysis.
[line 44] Justifying the 20°C bound seems to require citing Donelick et al (1990), and optionally Tamer and Ketcham (2020).
[line 243] Change to “thermal history modeling” (or “all model calculations”).
[line 249] Although replicate values are indeed important for assessing the reproducibility of kinetic parameter values, they may also be taken as an indication of the presence of zoning. The authors do not specify how many spots they took per analysis, but I suspect the answer is one, and that it reflects the usual 2-µm activation zone for EMP; was this driven by the desire for a faster and/or less expensive analysis? Likewise, how many Dpar measurements are averaged for each Dpar determination? The usual procedure is to average four, which ought to make the reproducibility better than observed in Fig. 2c. Also, it’s a little unfortunate that the discussion of the downsides of this procedure (lines ~326-340; might not get a compositional analysis near the counting area, or for the grain at all, I gather partly due to the LAICPMS spot) is in the next section; the authors can probably clarify and condense things by briefly mentioning these here, and then referring to them in section 2.3.
[line 269] Although Dpar imprecision is certainly responsible for a lot of the scatter in Fig. 2e, it’s not clear it’s the main reason; the authors might try only plotting the points within the 20% bars in Fig. 2c and seeing what the Dpar vs. eDpar scatter looks like. The even scatter might simply be an indication that the things that throw Dpar off are bidirectional; a little OH might increase resistance to annealing compared to no OH (i.e. F-apatite), and a lot of OH might decrease it (e.g., OH-apatite HS from Carlson et al. (1999), but the more OH you have the higher Dpar is.
[Figure 2] Maybe smaller symbols would be better to avoid some of the “solid cloud” effect; some “N =” annotations also would not hurt, and maybe correlation coefficients for d and e.
[line 291] “colour-coded”
[line 295] It may be worth noting that compositional populations may also be good candidates for shared inheritance. Although eCl is one such possibility, insofar as it combines a number of compositional variables into one number, apatites with similar eCl may get there via different compositional components, and thus not constitute a good candidate for shared inheritance. This is discussed further below.
[line 333-337] Maybe here or elsewhere, discuss the choice between switching which bin a grain is in, versus leaving the grain out altogether.
[line 438] The claim that population 3 has retained tracks from 540 Ma, or from about 245°C (Figure 6) is eye-catching, and probably overly optimistic about the ability of AFT to retain information about such high temperatures. It appears to stem from a difference in how AFTINV evaluates total annealing versus HeFTy’s “oldest track”. HeFTy assumes total annealing after reduced mean length falls below 0.4095 for non-projected lengths, corresponding to a mean length of just under 7 µm, whereas AFTINV appears to have total annealing correspond to a mean track length of 2 µm (line 419). This may be based on a slight misinterpretation of what’s written in Ketcham et al. (2000); the 2 µm limit mentioned there corresponds to the smallest track that can appear in a track length distribution. However, such occurrences are due to including a population of tracks with a higher mean and large standard deviation. The 0.4095 value arises in part from the observation that no annealing experiments reported by Green (1988) or Carlson et al. (1999) had a mean length below 7 µm (although there are some 6’s and 5’s reported by Barbarand et al. (2003), and even an occasional 4 or 3 by their Analyst 3). Willett (1997) uses a similar value of 0.428 as the zero-density intercept of reduced length versus density reported by Green (1988). In other words, by the time a mean length falls below some limit, the track population becomes undetectable. I believe this provides a more realistic basis for evaluating total annealing and the oldest retained track. Using the revised criterion, the TA for the oldest track for an rmr0=0.491 apatite is closer to 200°C, which seems a lot more reasonable considering the closure temperature is 161°C. This is not the most crucial of issues, but it’s prudent to avoid distracting claims.
[Figure 6, 7] I appreciate the authors’ efforts to incorporate the CRS method into AFTINV, and intrigued by the result – it looks to be a powerful addition. I have long been considering doing something similar myself, having dropped the CRS method when I converted my earlier program AFTSolve to HeFTy. However, one of the reasons I did so may still be evident in the model results here. The CRS method has a tendency to quickly converge to a relatively smooth solution that does not explore the solution space as well as the Monte Carlo method, and thus map out the range of solutions that fit well. In HeFTy results, this allows the resolving power of the data to be evaluated by looking at the width of the solution envelopes. In the results here, what puzzles me for P013-12 is the relatively tight band of good solutions above 175°C from 600-450 Ma, and probably a fair bit younger/cooler than that. Given the 161°C closure temperature of the most resistant population, the idea that it would exert much constraint in the 175-250°C temperature range seems improbable, and is not reflected in the QTQt results either. This all is not necessarily a problem, but I think it should be discussed so people interpreting these results have a more complete knowledge of what they are looking at.
Along similar lines, did both the AFTINV and QTQt models assume that all apatites in each sample had the same inherited, pre-depositional history? If so, was the fact that they did so, and their success in fitting their models, and indication that there was shared provenance, or an indication that, for these samples, results are not terribly sensitive to the pre-depositional history? Or, are the results sensitive – do the few earlier-cooling 0.5 paths for P013-12 corresponds to the earlier peaks T’s at ~195 Ma and/or ~70 Ma?
The manual (AFTINV) and automatic (QTQt) raising of the rmr0 values for the most resistant populations in each sample is interesting. What seems to be going on is that the different populations need greater separation in their partial annealing zones to produce their respective divergent age and length distributions. It’s further interesting that the higher resistance is corroborated by the vitrinite data for sample LHA003, though less so for P013-12. The authors recommended approach of “anchoring” on low-resistance kinetic seems like a good one. Another possible “advantage” of the Ketcham et al. (1999) model over the (2007) one beyond the different rmr0 equation is that it has a much higher temperature range, which these results may imply is necessary to create these divergent populations.
Lastly, the comparison between AFTINV and QTQt results appears to gloss over their differences a bit. For P013-12, the first reheating peaks at ~168 Ma in AFTINV and could go as far back as 195 Ma, whereas QTQt appears to strongly say that it was at about 140 Ma. Similarly, AFTINV implies that the first peak reheating for LHA003 was at 345 Ma, compared to 300 Ma for QTQt. If you lay the models pairs on top of each other, they appear to exclude each other at these times. Is this because QTQt calculated different kinetics than the manually-shifted ones in AFTINV, or because of QTQt favoring simpler histories, or some combination of these and possibly other factors?
References cited:
Barbarand, J., Carter, A., Wood, I., and Hurford, A. J., 2003, Compositional and structural control of fission-track annealing in apatite: Chemical Geology, v. 198, p. 107-137.
Carlson, W. D., Donelick, R. A., and Ketcham, R. A., 1999, Variability of apatite fission-track annealing kinetics I: Experimental results: American Mineralogist, v. 84, p. 1213-1223.
Donelick, R. A., Roden, M. K., Mooers, J. D., Carpenter, B. S., and Miller, D. S., 1990, Etchable length reduction of induced fission tracks in apatite at room temperature (~23°C): Crystallographic orientation effects and "initial" mean lengths: Nuclear Tracks and Radiation Measurements, v. 17, no. 3, p. 261-265.
Green, P. F., 1988, The relationship between track shortening and fission track age reduction in apatite: Combined influences of inherent instability, annealing anisotropy, length bias and system calibration: Earth and Planetary Science Letters, v. 89, p. 335-352.
Ketcham, R. A., Carter, A. C., Donelick, R. A., Barbarand, J., and Hurford, A. J., 2007, Improved modeling of fission-track annealing in apatite: American Mineralogist, v. 92, p. 799-810.
Ketcham, R. A., Donelick, R. A., and Carlson, W. D., 1999, Variability of apatite fission-track annealing kinetics III: Extrapolation to geological time scales: American Mineralogist, v. 84, p. 1235-1255.
Ketcham, R. A., Donelick, R. A., and Donelick, M. B., 2000, AFTSolve: A program for multi-kinetic modeling of apatite fission-track data: Geological Materials Research, v. 2, no. 1, p. (electronic).
Tamer, M. T., and Ketcham, R. A., 2020, Is lowâtemperature fissionâtrack annealing in apatite a thermally controlled process?: Geochemistry, Geophysics, Geosystems, v. 21, p. e2019GC008877.
Willett, S. D., 1997, Inverse modeling of annealing of fission tracks in apatite 1: A controlled random search method: American Journal of Science, v. 297, p. 939-969.
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CC1: 'Reply on RC1', Paul. Green, 08 Oct 2021
- AC3: 'Reply on CC1', Dale Issler, 11 Nov 2021
- AC1: 'Reply on RC1', Dale Issler, 09 Nov 2021
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CC1: 'Reply on RC1', Paul. Green, 08 Oct 2021
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RC2: 'Comment on gchron-2021-22', Karl Lang, 08 Oct 2021
*I generally agree with the review posted by Dr. Ketcham and have focused my comments on two areas that remain unaddressed*
Summary
This manuscript presents an important discussion of a critical but often overlooked aspect of apatite fission-track dating: the effect of chemical composition on annealing kinetics. The authors present example data from two Phanerozoic sandstones in northern Canada and present very detailed fission-track and elemental analyses to compare the efficacy of chemical proxy measurements (rmr0, Dpar, etc). They also demonstrate how to exploit the natural chemical variability in a population of minerals to invert detailed T/t histories.
Recommendation
This is a well written and thorough manuscript, the topic is of significance to fission-track and (likely) other geo/thermochronology communities, and is an appropriate submission to Geochronology. It should be accepted. I only have a few minor comments.
General Comments
Use of “detrital” was a little confusing to me at first, since many applications of detrital thermochronology are now also focused on interpreting cooling histories of source rocks prior to deposition, and not simply the common cooling history of detrital minerals in a sedimentary rock after deposition. This is a semantic difference, but perhaps adding a sentence to state this explicitly at the beginning of the manuscript might clear up any confusion amongst readers.
Why does the manuscript include a vigorous preference of LA ICPMS over EDM approach? This seems unrelated to the central motivation of the paper and, in my opinion, is largely unsupported (see comments by line). The authors should explain why they chose to use LA-ICPMS instead of EDM, but they should avoid generalized claims about the relative efficacy of one method over the other (e.g. “The LA-ICP-MS method has some distinct advantages compared with EDM” [117]).
Comments by line.
118. It has not been my experience that analytical costs are lower for LA-ICPMS than for the EDM when measured on a per grain basis. If you can measure 100 grains per mount and 50 mounts fit in a $1000 irradiation package, that’s $5/grain. By comparison, LaserChron (probably cheapest option in US, at least) charges $9-16 per grain for 100 grain samples, not including costs for CL imaging. Also, throughput is not necessarily higher for LA if you have to wait several months for lab time to become available. In my experience the analytical time to produce a complete fission-track dataset is comparable regardless of the analytical approach. I worry that comments like this will gradually discourage scientists from using the EDM, which is a well established and data-rich method.
137-138. Wouldn’t observer bias have a greater impact on age determination when it is only accounted for in spontaneous track counting? It seems to me that observer bias may actually be reduced when it is accounted for in both the spontaneous and induced track counts, rather than just in spontaneous counts. Either way, I don’t consider it fair to say that LA-ICPMS is “more objective” if it still relies on user interpretation and collection of spontaneous track data.
140. This is not an inherent limitation of the EDM, simply a choice by the operator to count fewer grains. Many detrital studies regularly count more than 100 grains per sample with the EDM.
142-143. It is convenient to make this argument here, but one could also make an alternative argument that the induced track print actually allows for more robust data collection because you can avoid the zonation issues you mention to be a problem on line 128-130. I don't understand why this is cast as an example of making the EDM less objective.
145. Again, this is not an inherent problem with EDM it is a choice by the EDM user.
- AC2: 'Reply on RC2', Dale Issler, 09 Nov 2021
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CC3: 'Comment on gchron-2021-22', Ian Duddy, 16 Oct 2021
- Summary
- Serious problems with the quality of the EMPA and LAICPMS AFT data used in this paper irrevocably compromise the conclusions concerning the use and superiority of rmr0over chlorine (wt%) as a kinetic control on apatite fission track annealing. Because of these problems the subsequent thermal history modelling has no basis.
- A detailed assessment is provided in the attached PDF.
Ian Duddy and Paul Green
- October 15th2021
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CC4: 'Reply on CC3', Dale Issler, 20 Oct 2021
The attached PDF contains the author response to the community comment for preprint gchron-2021-22.
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CC5: 'Reply on CC4', Ian Duddy, 20 Oct 2021
We are saddened that Issler et al in their response (CC4) has focused a personal attack on us and our methods rather than responding in appropriate detail to the technical issues surrounding the very poor EMPA data at the heart of our comments. At many points in CC4 the authors take issue with what we do, and how we do it. We respectfully point out that what we do is not at issue here; it is whether the claims made in their paper are justified by the data they present. They are not.
Issler et al question our motives, but we understood that the purpose of this forum was to provide a public space in which comments could be made without anonymity. Few academics seem willing to take the opportunity to air their views in public. Our initial comment was not written in defense of our methods, but to point out the negative attitude with respect to apatite compositional influence in the review of Ketcham.
Despite being first and foremost a commercial entity, Geotrack has strived to maintain a major research effort and has published >>100 peer reviewed papers. If, as Issler et al suggest we operate outside the academic sphere, it is not our doing, although this attitude is not unique.
That the EMPA data is too poor in quality to justify any conclusions regarding the utility, or otherwise, of rmr0as a superior kinetic measure to either chlorine alone, or Dpar, is incontrovertible on the basis of our comment (CC3), yet this is denied by the authors in their response to our comment. Such poor quality data (e.g. totals as low as 81%) would not be accepted in a discussion of igneous rock petrogenesis, for example, and it should not be accepted here.
In this paper, Issler, McDannell, O’Sullivan and Lane, are attempting to replace what they see as inferior indicators of apatite annealing kinetics (in their view, apatite chlorine alone and/or Dpar) with one based on a range of elements - rmr0. The attempt is based on only two samples for which the central EMPA dataset is inadequate as detailed in our comment (CC3). We are not delving into minutiae” when we point out that accurate structural formulae, the basis of rmr0, cannot be determined from analyses with poor totals. The entire story presented in the paper derives from these analyses. Issler et al are obfuscating when they say that there could be many reasons for poor totals, including abundant elements not measured! Regardless, these analyses cannot be used to justify the claims regarding the relationship between rmr0 and AFT annealing. We hope that the authors will address this point directly. We are not exaggerating the problem.
Finally, we are confident that the LTT community would welcome input from those familiar with EMPA analysis and the reliability of structural formulae determined from apatite analyses with low totals such as these.
Ian Duddy and Paul Green, Oct 20 2021.
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RC3: 'Reply on CC5', Richard A. Ketcham, 21 Oct 2021
Comment on discussion contributions by Green, Duddy, and Issler
In comment CC1, Green and Duddy have chosen to interpret a statement I made in a very particular way, which was inconsistent with my other comments in this review, and my other work in AFT thermochronology. I share the reviewers’ frustration with the lack of uptake of the community concerning acquiring kinetic data to help with their quantitative interpretation of their fission-track results. The way I chose to phrase this in my review derives from many individual conversations with fission trackers, about their resistance to acquiring compositional data. Access to a good electron probe, with an operator that is familiar with the vagaries of analyzing apatite, and the additional money to pay for it, do not come easy for many fission-track labs that are under-resourced. For people in that circumstance, I hesitate to try to bully them into it, as it’s a significant expense and trouble which may in fact turn up that compositional variation was insignificant for a given study. Trying a carrot rather than stick approach, I often advise that they should at least look for variation in Dpar (which will vary if Cl varies) and failure to pass chi-squared as signs that a sample will benefit from more work, and certainly not shove it into a thermal history inversion program. Admittedly, the carrot has not worked wonders either, which is why I welcome this contribution from Issler and coworkers to provide more well-documented examples of the advantages of getting the additional data.
Concerning the other point made in CC1, it’s simply inaccurate to say that my previous work has “downplayed the importance of Cl.” All experimental work to date document that it’s a dominant influence, but the data also clearly show that other factors can influence annealing as well; it’s simply a question of how often a given variant is encountered. Table 4 in Carlson et al. (1999) provides some idea, based on one set of EMP analyses from a handful of studies. It will be good to have more data sets to add to this one, and I look forward to Duddy and Green’s upcoming contribution on this front. In any event, the implication of this table is that Cl enrichment is indeed much more common than cation substitutions in sufficient degree to affect annealing, though there remains insufficient data to be confident about just what a sufficient degree is
Although one should be careful in putting too much stock in a single apatite, an important one in the Carlson et al. (1999) data set is Tioga (TI), which has only a modest amount of Cl (0.85 wt. %, or 0.24 apfu), but is among the most resistant analyzed, far beyond apatites with similar or greater Cl content. Based on the limited data, it’s unclear whether this annealing resistance can be attributed to its fairly low but present FeO and MnO content (0.12 and 0.07 apfu, respectively), or its high OH content (0.96 apfu), as other indications in the Carlson et al. (1999) data set point to mixing on the anion site being more important than simple Cl content; near-end-member Cl-apatite B3 is much less annealing-resistant than near-ternary F-Cl-OH apatite B2. There have been occasional studies that have found OH to be influential (Indrelid and Terken, 2000), and so an interesting question remains whether this factor is important – one which certainly requires more data.
Both this study, and associated ones by the Canadian group (e.g., Powell et al., 2018) seem to point to OH as being important, but there are also FeO analyses approaching the values observed in TI apatite. One improvement the authors might consider is providing some indication of the relative influence of OH and cations for these samples, at least as approximated by the rmr0 equation; maybe as simple as calculating rmr0 from cations alone and OH alone and Cl alone, to give a ballpark estimate, and maybe a closer analysis would allow them to suss out some more insights.
Concerning the quality of the EMP data brought up by Duddy in CC3, this is indeed a concern that merits more attention and discussion by the authors. One thing concerning their EMP totals is that it appears that they do not add in the OH inferred from stoichiometry, as allowed in the spreadsheet provided by Ketcham (2015). Including this factor will push many analyses back over the 98% limit. The systematic bias between the Ca and P site can also be a result of using the wrong number of oxygens to normalize, so that should be double-checked (I could not work out which apatites in the open file report corresponded to which ones in the data table for P013-12, so a more reliable mapping between those would also be good). Once these things are seen to, a frank assessment of the quality of the EMP data is certainly in order. However, I don’t think perfection is necessary for this to be a useful contribution; if someone wants to add a grain of salt because of poor totals, they are entitled to do so. If FeO is present, or F+Cl is absent (indicating a substantial OH content), poor totals will probably not affect that too much.
Concerning the 101%-98% criterion set forth by Duddy and Green, it can be difficult to achieve in apatite, or it can be achieved spuriously, depending on what elements one analyzes for and how one sets up one’s calibration. Looking in particular at the rare earth elements, they are often present, but only a few are characteristically analyzed for with the EMP (Ce, La, maybe Sm and/or Nd); however, because they are heavy, and there are so many of them that generally travel together, it doesn’t take much in terms of atoms per formula unit to affect totals. HREE’s are almost never analyzed for with EMP, and are usually depleted compared to LREE’s, but not always, as shown by the increasing number of LA-ICPMS analyses. Neglecting REE’s by omission during calibration may provide better apparent totals, but an incomplete picture. Anion stoichiometry (i.e. not having excess F+Cl) is also an important quality for evaluating EMP data, given the mobility of F under the electron beam (Stormer et al., 1993), which in turn is affected by the crystallographic orientation of the grain.
References cited
Carlson, W. D., Donelick, R. A., and Ketcham, R. A.: Variability of apatite fission-track annealing kinetics I: Experimental results, Am. Mineral., 84, 1213-1223, 10.2138/am-1999-0901, 1999.
Indrelid, S. L. and Terken, J. M. J.: Constraints on the thermal history of the interior basins of the Sultanate of Oman using apatite fission track analysis (AFTA), 9th International Conference on Fission Track Dating and Thermochronology, Lorne, Australia 2000.
Ketcham, R. A.: Technical Note: Calculation of stoichiometry from EMP data for apatite and other phases with mixing on monovalent anion sites, Am. Mineral., 100, 1620-1623, 10.2138/am-2015-5171, 2015.
Powell, J. W., Schneider, D. A., and Issler, D. R.: Application of multi-kinetic apatite fission track and (U-Th)/He thermochronology to source rock thermal history: a case study from the Mackenzie Plain, NWT, Canada, Basin Res., 30, 497-512, 10.1111/bre.12233, 2018.
Stormer, J. C. J., Pierson, M. L., and Tacker, R. C.: Variation of F and Cl X-ray intensity due to anisotropic diffusion in apatite during electron microprobe analysis, Am. Mineral., 78, 641-648, 1993.
Richard Ketcham, 20 October 2021
- AC6: 'Reply on RC3', Dale Issler, 07 Dec 2021
- AC5: 'Reply on CC5', Dale Issler, 02 Dec 2021
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RC3: 'Reply on CC5', Richard A. Ketcham, 21 Oct 2021
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CC5: 'Reply on CC4', Ian Duddy, 20 Oct 2021
- AC4: 'Reply on CC3', Dale Issler, 02 Dec 2021
Dale R. Issler et al.
Data sets
A multikinetic approach to apatite fission-track thermal modelling using elemental data: data and model results for a Permian and Devonian sample from northern Yukon Issler, D. R., McDannell, K. T., Lane, L. S., O’Sullivan, P. B. and Neill, O. K. https://doi.org/10.4095/328844
Dale R. Issler et al.
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