YOUNG SCIENTIST AWARD (DONATH MEDAL): QUANTITATIVE STRATIGRAPHIC CORRELATION

The proliferation of physical and chemical stratigraphic data characterizing Neoproterozoic and Paleozoic sedimentary basins invites the application of quantitative techniques for temporal correlation. While the peaks and nadirs of Neoproterozoic and Paleozoic high-amplitude, periodic δ¹³C_carb excursions serve as ideal correlation datums, localized basin geodynamics and sedimentation (and other geochemical factors) produce partial and aliased δ¹³C_carb signals that can thwart intra- and interbasinal correlation (Myrow and Grotzinger, 2000). Dynamic programming algorithms guide the alignment of Plio–Pleistocene paleoclimate records subject to similar challenges with signal preservation (e.g., Lisiecki and Lisiecki, 2002; Channell et al., 2009); while this numerical tool has been adopted to resolve questions of Paleozoic biostratigraphic sequencing (Sadler, 2004), the method remains underutilized in the alignment of pre-Cenozoic chemostratigraphic records. Moreover, the physical stratigraphic record provides one means of quantifying the amplitude of Neoproterozoic and Paleozoic low- and mid-latitude glaciations (Hoffman et al., 1998; Finnegan et al., 2010). Insight from Plio–Pleistocene glacial cycles illustrates that ice-volume changes produce a geographically variable sea-level change that can be discerned with gravitationally self-consistent models of a viscoelastic Earth. Here I review case studies of the application of these quantitative techniques to the Neoproterozoic–Paleozoic sedimentary record that both resolve and complicate stratigraphic correlation. The application of dynamic programming algorithms to Cambrian Series 1–2 δ¹³C_carb correlation raises questions about inferred trends in Cambrian evolutionary tempo (Hay et al., 2019), while the application of glacial isostatic adjustment models to Neoproterozoic and Paleozoic glacial stratigraphies raise questions about the temporal correlation of stratal surfaces arising from glacioeustasy (Creveling and Mitrovica, 2014; Creveling et al., 2018).