the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Sep 2024
Nonparametric estimation of age-depth models from sedimentological and stratigraphic information
Abstract. Age-depth models are fundamental tools used in all geohistorical disciplines. They assign stratigraphic positions to ages (e.g., in drill cores or outcrops), which is necessary to estimate rates of past environmental change and establish timing of events in sedimentary sequences. Methods to estimate age-depth models commonly use simplified parametric assumptions on the uncertainties of ages of tie points. The distribution of time between tie points is estimated using simplistic assumptions on the formation of the stratigraphic record, for example that sediment accumulates in discrete events that follow a Poisson process. In general, age-depth models are a crude simplification that fail to provide a comprehensive implementation of all empirical data or expert knowledge (e.g., from sedimentary structures such as erosional surfaces or from basin models). In other words, many information sources that can potentially provide geochronologic information remain un- or underused.
Here, we present two non-parametric methods to estimate age-depth models from complex sedimentological and stratigraphic data. The methods are complementary as they use different sources of information (sedimentation rates and observed tracer values), are implemented in the admtools package for R Software and allow the user to specify any error model and distribution of uncertainties.
As use cases of the methods, we
- construct age-depth models for the Late Devonian Steinbruch Schmidt section in Germany and use it to estimate the timing of the Frasnian-Famennian boundary and the duration of the Upper Kellwasser event.
- use measurements of extra-terrestrial 3He from ODP site 960 (Maud Rise, Weddell Sea) to construct age-depth models for the Paleocene–Eocene thermal maximum (PETM).
The first case study suggests that the Upper Kellwasser event lasted 89 kyr (IQR: 84 to 97 kyr) and places the Frasnian-Famennian boundary at 371.834 ± 0.101 Ma (2 σ), whereas the second case study provides a duration of 41 to 48 kyr for the PETM recovery interval.
These examples show how information from a variety of sedimentological and stratigraphic sources can be combined to estimate age-depth relationships that accurately reflect uncertainties of both available data and expert knowledge.
- Preprint
(923 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 19 Dec 2024)
-
RC1: 'Comment on egusphere-2024-2857', Maarten Blaauw, 25 Oct 2024
reply
This manuscript discusses two modules that could be useful for age-depth models - one that enforces sedimentation to be in chronological order, and another one that adjusts age-depth curves so that they even out fluctuations in proxies that could be assumed to have been subject to constant influxes over time. Neither of the modules are particularly novel, with many developments in this area having taken place decades ago. It also seems that the authors have an axe to grind with regard to existing approaches to age-modelling, and I disagree with most of the arguments made against them.
The description of the availability, development, versioning and use of the R software is described very well and reflects best practice. Much is made of the process not being automatic and requiring much user input. I wonder though how many researchers will have the expertise to select the parameters and settings appropriately/correctly. Is there a danger that users will (inadvertedly) choose settings such that they get age-models based on wishful-thinking? Lines 537-541: Many of the other age-depth models out there, be they Bayesian or classical, are also explicit in their modelling assumptions. As long as the users report what settings and versions were used, the models are replicable, documented, and can run under a wide range of computational environments.
The usage in the abstract of terms like 'simplistic' and 'crude simplification' should be toned down. Many of the currently available techniques presented a significant improvement on what was done before. In lines 512-517, the authors disparage currently popular mathematical distributions for age-depth modelling such as Poisson or gamma distributions as simplistic mathematical shortcuts with little if any real-world relevance. I don't think that is fair criticism - instead, as one of the authors of these methods, I can vouch that these distributions were indeed selected with the aim to provide more realistic deposition models. Everybody would agree that models are simplifications of real-world processes. It is frustrating that the manuscript rejects these mathematical distributions but then proposes a uniform distribution for sediment accumulation rates, which to me seems much less geologically robust/defendable and almost entirely made for ease of coding. Lines 403-6, limits on sedimentation rates: how are these decided? For your chosen limits of 0.1 to 0.6 cm/kyr, is it really geologically likely that any value inbetween that range is equally likely, but that any value outside of that range has a probability of 0%? I don't think so. Why not use say a gamma distribution which by itself enforces sedimentation to never be <0, and which can downweigh sedimentation rates that are considered to be unlikely, without the need to enforce such strict borders? With more information available, the gamma distribution can be made to peak more (or less). Line 70, all models are partly driven by data, and partly by assumptions.
Lines 531-536, yes, ages often scatter beyond their lab uncertainties, but Bayesian models deal very well with such scatter - much better than classical models can. Same for lines 551-551: both are examples of classical models, yet real-world data and simulations have shown that Bayesian models provide much more pessimistic/realistic uncertainty estimates (e.g., Blaauw et al. 2018 <doi:10.1016/j.quascirev.2018.03.032>). How do the uncertainties of the proposed age-depth model compare with existing Bayesian/classical models?
Line 54, note that orbital matching (as opposed to age-depth modelling based on e.g. radiometric dates) makes assumptions that could be seen as simplistic, hard to prove, and causing a degree of circularity with and dependence on other records. See Blaauw 2012 <doi:10.1016/j.quascirev.2010.11.012> for a critical review of climatic tuning - although I agree that sometimes one would need to be pragmatic and choose tuning if no absolute age estimates are available.The CON algorithm is presented using many equations, but in the end defines nothing fundamentally new - this has all been stated before in different notation or terms in previous age-depth model papers. As for FAM, another classical use of correcting for supposed constant influx assumed a constant pollen rain and then applied this assumption to stretch and compress a core's accumulation rate. See Middeldorp 1982 <doi:10.1016/0034-6667(82)90003-3> and Young et al. 1999 <doi:10.1016/S0034-6667(98)00060-8>. Of course, on long time-scales it is unlikely that any proxy will have a constant flux (Figure 4 and the accompanying discussion are interesting in this respect). The interpretation that the authors' FAM model essentially resembles the CRS model doesn't make the new model novel either - the CRS stems from the 1970s.
Line 301, "Timing and positions of tie points can follow arbitrary probability distributions as long as they are strictly ordered." Do you mean that closely spaced dates/tie-points cannot have overlapping age distributions? That would be a drawback of the method. This type of problems is exactly why Bayesian models which use Poisson/Gamma distributions are so successful in modelling sedimentation - they guarantee that reversals are absent, and moreover, they can take advantage of high dating densities; they "learn" with precision becoming ever higher as more dates are added. This is not necessarily the case for classical age-models (see Blaauw et al. 2018 <doi:10.1016/j.quascirev.2018.03.032>).
Fig. 2, isn't the floating age-model in panel B and the anchored one in panel A? If not, then the terminology used is confusing (at least to me). If a core's age is measured relative to an otherwise dated layer (i.e., it is tied), how can it be floating? Surely the age uncertainty of the Bentonite layer is not 0, so there is no point in presenting Figure 2A. (Later on in the Discussion, a point is made that one can set the uncertainty at a layer to 0 in order to estimate the durations of an interval. However, that can also be done by simply subtracting the bottom ages from the top ages for each iteration of say a Bayesian age-depth model, be it floating or anchored.) For Figure 2A, I also don't see a 'sausage-shape' but rather a nematode-shape. Are the reconstructed 95% ranges of Fig. 2B considered realistic? Do they compare well with established methods such as BChron? This should be discussed, e.g. after line 511.
Details:
Line 29 presents a very interesting example of how an updated age-depth model affected evolutionary interpretations.
Relying on R's integrate function could be problematic, as explained in its help function.
36, and further on in the manuscript, the author of Oxcal is Bronk Ramsey (not Ramsey)
60, CONdensation (not CONndensation)
68, superposition
91, the assumption that accumulation always happens (no hiatuses) would fail easily on long time-scales.
158, congruent
345, missing full stop
378, two assumptions
380, length
501, shown
516, the the
557, full use of these
Table 1, Bacon additionally uses sed.rates and its variability as (prior) information sources.Citation: https://doi.org/10.5194/egusphere-2024-2857-RC1 -
CC1: 'Problematic', Richard Zeebe, 03 Nov 2024
reply
The manuscript discusses age-depth models in general, as well as age models for specific events, including the Paleocene-Eocene Thermal maximum (PETM). Unfortunately, the omission of references to immediately relevant work on the subject and the lack of discussion of the authors' results in relation to previous work is highly problematic.
The authors' PETM age model uses 3He data from Farley and Eltgroth (2003). Omission of the immediately related study by Murphy et al. (2010) using 3He for the PETM is hence rather incomprehensible:
Murphy, B. H., K. A. Farley, and J. C. Zachos. An extraterrestrial 3He-based timescale for the Paleocene-Eocene Thermal Maximum (PETM) from Walvis Ridge, IODP Site 1266. Geochimica et Cosmochimica Acta 74, no. 17 (2010): 5098-5108.
Murphy et al. (2010) designated the end of the PETM CIE recovery to occur at 217 (+44/-31) kyr, a much longer time interval than suggested in the present manuscript (Fig. 6).
Zeebe and Lourens (2019) derived a PETM duration of 170 (+-30) kyr from onset to recovery inflection, again a much longer time interval - and at odds with the present manuscript.
Zeebe, R.E. and Lourens, L.J., 2019. Solar System chaos and the Paleocene-Eocene boundary age constrained by geology and astronomy. Science, 365(6456), pp.926-929.
Regrettably, none of the studies above is mentioned or referenced in the ms (there are more). Neither are the large discrepancies discussed relative to previous work. Perplexingly, there is also no reference to any of the PETM landmark papers led by Zachos in the reference list.
Given the above, the manuscript should not be considered for publication without an in-depth analysis and discussion of relevant previous work on the PETM and PETM duration, most notably those studies at odds with the present manuscript, including the references mentioned above.
Richard E. Zeebe
Citation: https://doi.org/10.5194/egusphere-2024-2857-CC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 328 | 68 | 50 | 446 | 3 | 5 |
- HTML: 328
- PDF: 68
- XML: 50
- Total: 446
- BibTeX: 3
- EndNote: 5
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1