GSA Connects 2021 in Portland, Oregon

Paper No. 214-2
Presentation Time: 8:20 AM


ZHANG, Tianran1, KELLER, C. Brenhin Brenhin1, HALVERSON, Galen P.2, HOGGARD, Mark J.3, ROONEY, Alan4, BERGMANN, Kristin D.5 and STRAUSS, Justin1, (1)Department of Earth Sciences, Dartmouth College, Hanover, NH 03755, (2)Department of Earth and Planetary Sciences, McGill University, Montreal, QC H3A 0E8, Canada, (3)Research School of Earth Sciences, Australian National University, 142 Mills Rd, Canberra, ACT 0200, Australia, (4)Department of Earth & Planetary Sciences, Yale University, 210 Whitney Ave., New Haven, CT 06511, (5)Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139

Basin analysis is regarded as the standard method for quantitatively reconstructing the subsidence and thermal histories of sedimentary basins, yielding critical insights into the architecture and dynamics of continental lithosphere. Although this technique has been successfully applied to Phanerozoic basins, a crucial component missing from previous approaches is the robust integration of uncertainties in input parameters. This shortcoming becomes especially problematic when reconstructing the evolution of Precambrian sedimentary basins due to their common lack of abundant and precise geochronological data. Here, we present a comprehensive approach for calibrating Precambrian sedimentation in simple rift basins. Building upon the classic sequence of decompaction, backstripping, and McKenzie-type thermal subsidence modelling, the main contribution of this new model is the incorporation and propagation of uncertainties using a Bayesian framework. Specifically, the following input parameters are treated as distributions rather than single values: 1) lithology-controlled physical properties (e.g. surface porosity, porosity-depth coefficient and grain density); 2) radiometric ages; and 3) stratigraphic heights. In addition, our model allows for inputs from multiple outcrops of the same succession, which accounts for along-strike variations. As a case study, we apply this model to the Tonian Akademikerbreen Group in northwestern Svalbard, Norway. Our subsidence model outputs for this succession provide an updated age model for key global event horizons (e.g., the Bitter Springs and Islay carbon isotope excursions) and paleontological data. These results are then compared with existing ages from the contemporaneous Mackenzie Mountain and Windermere supergroups of northwestern Canada, and Tambien Group of Ethiopia. By integrating subsidence modelling with a Bayesian age-depth model, the age-height relationship generated in this study provides more reliable time constraints for key chemostratigraphic and biostratigraphic events, including a comprehensive assessment of their uncertainty, and will eventually lead to a more robust timescale for the Tonian Period.