CORRELATING GEOCHRONOLOGY, LITHOSTRATIGRAPHY, BIOSTRATIGRAPHY, AND MAGNETOSTRATIGRAPHY: AN EXERCISE FROM THE MIOCENE BARSTOW FORMATION, CALIFORNIA

We designed a laboratory exercise to teach undergraduate students the principles of stratigraphy and correlation on local and global scales that uses published data from the Barstow Formation, southern California. The Barstow Formation is a middle Miocene terrestrial succession of fluvial and lacustrine sediments with a well-preserved mammalian fossil record. Numerous volcaniclastic horizons and localized carbonate deposits are intercalated. Sediments were rapidly deposited in a tectonically active basin and subjected to post-depositional folding and faulting, resulting in complex field relationships.

In preparation for the lab, brief introductions to argon-based and uranium-lead dating techniques, magnetostratigraphy, and the field area are presented. Students are provided with published lithostratigraphy, biostratigraphy (Woodburne et al., 1990), magnetostratigraphy, and K-Ar and ⁴⁰Ar/³⁹Ar geochronology (MacFadden et al., 1990), which have been correlated to the global magnetic polarity time scale and used to determine the ages of boundaries in North American land mammal stratigraphy (Woodburne, 1996). Students are also given new U-Pb ages of sedimentary carbonates (Cole et al., 2005) and unpublished ⁴⁰Ar/³⁹Ar ages of an important marker tuff.

Students are asked to evaluate the different data sets, draw their own correlations, and evaluate the robustness of their interpretations. For example, students compare the local magnetic polarity record and the dated ashes and carbonates to the global polarity time scale. In doing so, students uncover discrepancies between the data and are forced to exclude some of the data in order to construct stratigraphically-sound correlations.

This exercise provides the opportunity to discuss the differences between ‘relative' and ‘absolute' dating, presents data from a burgeoning geochronological technique, demonstrates real-world geologic complexity, and encourages problem solving with multiple data sets.