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A Rapid, Deterministic Age-Depth Modeling Routine for Geological Sequences With Inherent Depth Uncertainty
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Natural Resources and Sustainable Development. Univ Paris Saclay, Lab CNRS CEA UVSQ, IPSL, LSCE, Gif Sur Yvette, France.
Akita Univ, Grad Sch Int Resource Sci, Akita, Japan.
2019 (English)In: PALEOCEANOGRAPHY AND PALEOCLIMATOLOGY, ISSN 2572-4517, Vol. 34, no 1, p. 122-133Article in journal (Refereed) Published
Abstract [en]

Traditional approaches to age-depth modeling typically assume no uncertainty for the depth value of dated intervals. However, such an assumption may not be fully valid in the case of poor coring recovery or significant sediment deformation, as well as in the case of a large subsampling interval. In consideration of these issues, we present a new age-depth modeling routine, Undatable, which includes uncertainty in both age and depth. Undatable uses Bayesian radiocarbon (C-14) calibration software (MatCal) and a deterministic approach with a positive sediment accumulation rate assumed a priori which, combined with efficient programming practices, allows for the rapid production (in a matter of seconds in many cases) of age-depth models for multiple types of geological archives. Undatable has so far been successfully applied to coral archives, as well as sediment archives from estuarine, lacustrine, and deep-sea environments. Through the inclusion of a bootstrapping option, the software performs particularly well in the case of a large scatter in age-depth constraints by expanding the uncertainty envelope of the age-depth model. Unlike other deterministic models, increasing the density of age-depth constraints results in increased precision in Undatable, even at centennial scale, thus emulating the results of probabilistic models. In addition to the code itself, we also provide an interactive graphical user interface (GUI) that allows users to experiment with multiple age-depth model settings to investigate the sensitivity of a given data set to multiple parameters.

Place, publisher, year, edition, pages
2019. Vol. 34, no 1, p. 122-133
National Category
Environmental Sciences
Identifiers
URN: urn:nbn:se:uu:diva-378212DOI: 10.1029/2018PA003457ISI: 000458730100008OAI: oai:DiVA.org:uu-378212DiVA, id: diva2:1294155
Funder
EU, FP7, Seventh Framework Programme, 339108Swedish Research Council, 637-2014-499Available from: 2019-03-06 Created: 2019-03-06 Last updated: 2019-03-06Bibliographically approved

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Lougheed, Bryan C.

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