sklec.ecnu.edu.cn
Logistics
Date: 19 July 2022
Time: 15:00 UTC
Speaker: Prof. Lorraine Lisiecki (University of California, Santa Barbara)
Abstract
Previously developed software packages that generate probabilistic age models for sediment cores are designed to use either age proxies (e.g., radiocarbon or tephra layers) or stratigraphic alignment (e.g., of benthic δ18O) and cannot combine age inferences from both techniques. Furthermore, many radiocarbon dating packages are not specifically designed for marine sediment cores and default settings may not accurately reflect sedimentation rate variability in the deep ocean, requiring subjective tuning of parameter settings. Here we present a new technique for generating Bayesian age models and stacks of ocean sediment core data, implemented in a software package named BIGMACS (Bayesian Inference Gaussian Process regression and Multiproxy Alignment of Continuous Signals). BIGMACS constructs multiproxy age models by combining age inferences from both radiocarbon ages and δ18O stratigraphic alignment and constrains sedimentation rates using an empirically derived prior model based on 37 14C-dated ocean sediment cores (Lin et al., 2014). BIGMACS also constructs continuous benthic δ18O stacks via a Gaussian process regression, which requires a smaller number of cores than previous stacking methods. This feature allows users to construct stacks for a small hydrographic region that shares a homogeneous deep water δ18O signal, while leveraging radiocarbon dates across multiple cores. Thus, BIGMACS efficiently generates local or regional stacks with smaller uncertainties in both age and δ18O than previously available techniques.
Authors
Lorraine Lisiecki, Taehee Lee, Devin Rand, Jake Gebbie, Charles Lawrence
Registration
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